Multi-device interconnection control system of ship lightering system

Abstract

The invention relates to the technical field of ship lightering system control, and discloses a ship lightering system multi-device interconnection control system which comprises an interconnection parameter analysis module, a lightering feature extraction module, an interconnection association modeling module, a linkage abnormity intervention module and a dynamic feedback adjustment module. The system analyzes the matching degree of an interconnection structure and communication reliability by calling data such as equipment communication protocol parameters and interconnection hierarchy depth, and generates an analysis parameter set; on the basis of the extracted scene space feature value and distribution variation, optimizing feature parameters; analyzing association mapping of the interconnection hierarchy and lightering characteristics, and dynamically regulating and controlling association strength and paths; the linkage real-time feature matching rate and offset are extracted, abnormity is intervened, and a mapping path is adjusted; and adjusting the target path parameter according to the linkage demand and the response time, and generating a feedback data table. According to the system, dynamic regulation and control and abnormal intervention of multi-device interconnection are realized, and the control efficiency and reliability of lightering operation are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of shipboard barge system control, in particular to a shipboard barge system multi-device interconnection control system. Background Art

[0002] In the shipping industry, barge operations refer to the transfer of cargo from one ship to another. This process involves the coordinated operation of multiple devices, such as cranes, conveying equipment, and positioning devices. With the development of the shipping industry and the continuous increase in cargo transportation volume, the scale and complexity of barge operations are also increasing, placing greater demands on the interconnected control of multiple devices.

[0003] Traditional shipboard transfer systems, with their multi-device interconnected control, present numerous challenges. For one thing, the lack of standardized communication protocols between devices leads to a mismatch between the interconnection structure and device communication reliability, resulting in unstable data transmission and prone to communication failures, impacting the efficiency and safety of transfer operations. For example, devices from different manufacturers may utilize different communication protocols, which can easily lead to compatibility issues during data transmission, resulting in data loss or errors.

[0004] Within the barge-transfer scenario space, the extraction of eigenvalues ​​and distribution changes is not precise enough to accurately reflect changes in the actual operating environment. This affects the stability of the equipment's output precision, making it difficult to adjust operating parameters in a timely manner based on environmental changes, thereby affecting the quality and efficiency of barge-transfer operations. For example, in different water environments, changes in factors such as water depth and water flow velocity will affect barge-transfer operations. If these eigenvalues ​​cannot be accurately extracted, it will be impossible to properly adjust the equipment's operating parameters.

[0005] Inadequate analysis of the correlation and mapping between interconnection levels and barge characteristics hinders effective control of equipment linkage. When anomalies arise during equipment linkage, there's a lack of effective intervention, resulting in delayed resolution and further impacting the smooth progress of barge operations. For example, if a device experiences a malfunction or malfunction during multi-device linkage, traditional control systems struggle to quickly identify and adjust the operating status of other devices to ensure operational continuity.

[0006] Existing control systems lack dynamic feedback adjustment mechanisms, are unable to optimize system parameters based on real-time operational data, and are unable to adapt to complex and changing operating environments. This makes it difficult to effectively improve the efficiency and reliability of ship-to-ship operations, hindering the development of the shipping industry. Summary of the Invention

[0007] The purpose of the present invention is to provide a multi-device interconnection control system for a ship-based barge system to solve the problems raised in the above-mentioned background technology.

[0008] To achieve the above-mentioned object, the present invention provides the following technical solution: a multi-device interconnected control system for a shipboard transfer system, the system comprising: The interconnection parameter parsing module, based on the operating status information of the ship-borne barge equipment, calls the equipment's communication protocol parameters, interconnection layer depth, and barge coverage data, analyzes the degree of match between the interconnection structure and the equipment's communication reliability, assigns equipment layer parsing control values ​​and coverage balancing parameters, and generates an interconnection parameter parsing parameter set; A lightering feature extraction module extracts feature values ​​and distribution variations in the ship-to-ship lightering scene space based on the interconnected parameter parsing parameter set, analyzes the impact of feature extraction device accuracy output on stability, adjusts the spatial feature distribution balance, and generates a lightering feature optimization parameter set; an interconnection association modeling module, which analyzes the association and mapping between the interconnection level and the barge characteristics based on the barge characteristic optimization parameter set, adjusts the association input rate and the mapping path distribution ratio, redistributes the distribution trend and dynamic parameter value of the association strength, and generates the interconnection association dynamic control result; A linkage abnormality intervention module, based on the interconnected association dynamic control results, extracts the real-time feature matching rate and association offset during the ship-to-ship barge linkage process, analyzes the impact of the fluctuation range on the barge linkage response rate, dynamically adjusts the association mapping path and feature matching ratio within the target range, and generates an abnormality intervention adjustment data set; The dynamic feedback adjustment module analyzes the linkage demand distribution ratio and response time of the ship target lightering based on the abnormal intervention adjustment data set, adjusts the parameters of the target linkage path, and generates a ship lightering linkage feedback data table.

[0009] Preferably, analyzing the matching degree between the interconnection structure and the device communication reliability includes: Based on the operating status information of the ship-borne transfer equipment, the communication protocol parameters, depth data and transfer coverage data of the equipment are extracted. The time window is set, and the time points are selected to match the data. By comparing the data correlation and performing data screening, the communication protocol parameters and depth data are obtained. Based on the communication protocol parameters and depth data, the path is matched and checked, the difference between the protocol and the depth is calculated, the interconnection structure and the depth distribution are corrected in combination with the coverage change, and the path parameters are adjusted according to the impact of the coverage on the data to obtain the protocol and depth matching situation; Based on the protocol and depth matching, perform device communication reliability analysis, set reliability analysis standards, combine the dynamic changes of device operation, evaluate the protocol distribution under differentiated coverage conditions, compare stability indicators and optimize coverage conditions, and obtain the degree of matching between the interconnection structure and device communication reliability.

[0010] Preferably, the steps of obtaining the interconnection parameter parsing parameter set are specifically as follows: Based on the matching degree between the interconnection structure and the device communication reliability, analyze the communication transmission and reliability changes of the device under differentiated operating conditions, and perform weighted calculation on the protocol distribution of the device to obtain the preliminary hierarchical analysis requirements of the device; Based on the preliminary hierarchical analysis requirements of the devices, the hierarchical balance between the devices is analyzed, the relationship between the communication transmission efficiency and the load distribution between the devices is identified, and the device hierarchical analysis parameters are modified to obtain the hierarchical analysis data set between the devices; Combining the inter-device hierarchical parsing data set with the reliability matching result, the interconnection hierarchies between the devices are allocated, the required balance and reliability requirements are optimized and matched, and an interconnection parameter parsing parameter set is obtained.

[0011] Preferably, the steps for obtaining the characteristic values ​​and distribution changes in the ship-transfer scene space are specifically as follows: Based on the interconnected parameter parsing parameter set, extract the depth data in the ship-transfer scene space, screen the depth points in each time period, combine the depth change trend of the differentiated positions in the space, analyze the depth volatility, and obtain the depth data in the ship-transfer scene space; Based on the depth data in the ship-transfer scene space, each depth point and the corresponding characteristic value are calculated. By analyzing the relationship between depth and characteristics, the characteristic change amount of each measurement point is identified. Combined with the equipment structure parameters, the characteristic change of the differentiated positions is compared to obtain the characteristic distribution and distribution change data; Based on the feature distribution and distribution change data, the overall feature distribution in the ship transfer scenario space is analyzed, the feature distribution change is optimized in combination with the depth data, the impact of the feature change on the equipment performance is analyzed, the stable feature configuration under differentiated operating conditions is determined, and the feature value and distribution change in the ship transfer scenario space are obtained.

[0012] Preferably, the steps for obtaining the optimized parameter set of lightering characteristics are as follows: Based on the feature values ​​and distribution changes in the ship-transfer scene space, a time series of feature changes is determined, current feature values ​​are compared with original feature data, feature distribution changes at each moment are analyzed, and corresponding thresholds are defined according to equipment status partitions to generate a preliminary feature change parameter set; Analyze the preliminary feature change parameter set, analyze the impact of the features in the space on the stability of the equipment precision output, identify the correlation between the features and the precision output, and calculate the section precision stability influence coefficient; By analyzing the influence coefficient of the accuracy stability of the section, combining the spatial feature change parameters, adjusting the feature distribution balance, optimizing the feature control data, and generating the optimized parameter set of the barge feature.

[0013] Preferably, the steps for obtaining the interconnected association dynamic control result are specifically: Based on the barge characteristic optimization parameter set, the associated adsorption data of the barge surface is extracted, the adsorption rate of the associated on the surface of different materials is monitored, the associated mapping characteristics are inferred in combination with the external environmental factors of time and water area, the adsorption and mapping rate coefficients are defined, and the adsorption mapping dynamic parameter set is generated; Analyze the impact of the adsorption mapping dynamic parameter set on the correlation flow rate and distribution, optimize the ratio between the mapping path and the correlation input rate according to the requirements of the correlation intensity distribution on the barge surface, calculate the adjustment coefficient of the correlation intensity distribution trend, and generate the correlation intensity control result; The association strength control result is analyzed, the proportional relationship between the association input rate and the mapping path is adjusted, the distribution trend of the association strength is allocated, and the adsorption mapping parameter and the adjustment coefficient are combined to obtain the interconnection dynamic control result.

[0014] Preferably, the step of acquiring the abnormal intervention adjustment data set is specifically: Based on the interconnected association dynamic control results, the monitoring equipment monitors the feature matching rate and associated offset in real time during the linkage process, identifies the fluctuation range, eliminates equipment failure outliers, analyzes the average matching rate of the data, and obtains feature and associated fluctuation data; Analyze the impact of the characteristics and associated fluctuation range on the barge linkage response rate, use the known barge response rate, analyze the relationship between the correlation and the characteristics, calculate the response rate under the differentiated fluctuation range, and obtain the response rate impact data; Based on the response rate impact data, the association mapping path and feature matching ratio within the target range are dynamically adjusted. Adjustments are made based on the relationship between the response rate impact data and the features and associated fluctuation ranges. The associated flow rate and feature control range are allocated to generate an abnormal intervention adjustment data set.

[0015] Preferably, the steps for obtaining the ship-to-ship transfer linkage feedback data table are specifically as follows: Based on the abnormal intervention adjustment data set, the distribution of ship target transfer linkage demand and response time are analyzed, linkage strength data at differentiated response time points are collected, linkage time distribution is sorted, intensity change trends are analyzed and data are classified to obtain ship transfer linkage distribution data; Based on the ship-to-ship linkage distribution data, target linkage path parameters are adjusted, the optimal response time and intensity distribution of the barge linkage are analyzed, and by comparing the intensity changes under differentiated linkage conditions, the operating conditions of the linkage time, water flow velocity, and correlation intensity are adjusted to obtain the target linkage path parameters; Based on the target linkage path parameters, the linkage conditions are adjusted according to the current operating parameters, the variable relationship between the response time, water flow velocity, and correlation strength is controlled, and real-time linkage is performed according to the adjusted parameters to obtain a ship transfer linkage feedback data table.

[0016] Preferably, the step of adjusting the target linkage path parameters is specifically as follows: Based on the ship-to-ship linkage distribution data, the intensity peak points under differentiated linkage conditions are extracted, time thresholds are set to divide effective linkage intervals, and the intensity change rate within the intervals is compared with the baseline value to screen the key parameters affecting the linkage efficiency; Assign weights to the key parameters, evaluate the impact of parameter adjustment on linkage coverage in combination with the lightering space structure parameters, and calculate parameter adjustment weight coefficients; The weight coefficient is adjusted according to the parameters, and the operating conditions of linkage time, water flow velocity, and correlation strength are adjusted according to the weight priority to ensure that the adjusted parameters meet the requirements of optimal response time and intensity distribution, and obtain the target linkage path parameters.

[0017] Preferably, the real-time linkage control steps are specifically as follows: Based on the target linkage path parameters, the linkage time series and the water area flow velocity gradient are set, a mapping model of the correlation strength, time, and water area flow velocity is established, and the parameter target values ​​within each time period are determined; Monitor the actual value of the parameter during real-time linkage, calculate the deviation between the actual value and the target value, and generate the parameter compensation; The linkage equipment output is dynamically adjusted according to the parameter compensation amount, and the deviations of time, water velocity, and correlation strength are corrected to ensure that the real-time linkage process meets the target path parameter requirements and complete the generation of the ship transfer linkage feedback data table.

[0018] Compared with the prior art, the present invention has the following beneficial effects: Regarding interconnection parameter analysis, the system uses the interconnection parameter analysis module to call communication protocol parameters, interconnection layer depth, and lightering coverage data based on equipment operating status information. It then analyzes the degree of compatibility between the interconnection structure and equipment communication reliability, assigns equipment-level analysis control values ​​and coverage balancing parameters, and generates an interconnection parameter analysis parameter set. This process effectively resolves the issue of inconsistent equipment communication protocols, improves the stability and reliability of data transmission, ensures smooth communication between equipment, and provides a solid foundation for smooth lightering operations.

[0019] The lightering feature extraction module, based on a set of interconnected parameter parsing parameters, extracts feature values ​​and distribution variations within the scene space. It analyzes the impact of feature extraction device accuracy on stability, adjusts the spatial feature distribution balance, and generates an optimized parameter set for lightering features. This module accurately extracts feature information within the lightering scene space, promptly reflecting changes in the operating environment and providing accurate data support for equipment precision control, thereby improving equipment operational stability and operational quality.

[0020] The interconnection modeling module, based on a set of optimized parameters for lightering characteristics, analyzes the association and mapping between interconnection levels and lightering characteristics, adjusts the association input rate and the distribution ratio of the mapping paths, and generates dynamic control results for interconnection. This module enables in-depth analysis and dynamic control of the relationship between interconnection levels and lightering characteristics, improving the coordination and accuracy of equipment linkage, enabling more efficient collaboration among multiple devices and enhancing the efficiency of lightering operations.

[0021] The linkage anomaly intervention module, based on the results of dynamic control of interconnected associations, extracts real-time feature matching rates and association offsets, analyzes the impact of fluctuations on the response rate of the barge linkage, dynamically adjusts the association mapping path and feature matching ratio, and generates an anomaly intervention adjustment dataset. This module promptly detects anomalies in the equipment linkage process and intervenes through dynamic adjustments, effectively mitigating the impact of these anomalies on barge operations and improving system reliability and stability.

[0022] The dynamic feedback adjustment module analyzes the linkage demand distribution ratio and response time based on the abnormal intervention adjustment data set, adjusts the parameters of the target linkage path, and generates a linkage feedback data table. Through this module, the system achieves real-time monitoring and dynamic feedback adjustment of the operation process, enabling timely optimization of system parameters based on actual operation conditions, adapting to complex and changing operating environments, and further improving the efficiency and quality of ship-to-ship transfer operations. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 This is a working principle diagram of the multi-device interconnection control system for the ship transfer system of the present invention; Figure 2 Design drawings for matching analysis of interconnection structure and equipment communication reliability; Figure 3 The design diagram obtained by parsing the parameter set for the interconnection parameters; Figure 4 Design diagram for obtaining characteristic values ​​and distribution variations in the ship transfer scenario space. DETAILED DESCRIPTION

[0024] 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.

[0025] See also Figures 1-4The present invention relates to a multi-device interconnected control system for a ship-based barge system, which includes: an interconnected parameter analysis module, a barge feature extraction module, an interconnected association modeling module, a linkage abnormality intervention module, and a dynamic feedback adjustment module. Specific implementation methods are as follows: When the interconnection parameter parsing module is running, it first calls the equipment's communication protocol parameters, interconnection level depth, and transshipment coverage data based on the ship-borne transshipment equipment's operating status information, and then analyzes the degree of match between the interconnection structure and the equipment's communication reliability. It then allocates equipment-level parsing control values ​​and coverage balancing parameters, and finally generates an interconnection parameter parsing parameter set.

[0026] The lightering feature extraction module extracts the feature values ​​and distribution changes in the ship-transferring scene space based on the above-mentioned interconnected parameter parsing parameter set, analyzes the impact of the feature extraction device's accuracy output on stability, adjusts the balance of spatial feature distribution, and thus generates an optimized parameter set for lightering features.

[0027] The interconnection association modeling module optimizes the parameter set based on the barge characteristics, analyzes the association and mapping between the interconnection level and the barge characteristics, adjusts the association input rate and the mapping path distribution ratio, redistributes the distribution trend and dynamic parameter value of the association intensity, and generates the dynamic control results of the interconnection association.

[0028] The linkage abnormal intervention module extracts the real-time feature matching rate and association offset in the ship-to-ship barge linkage process based on the dynamic control results of the interconnected association, analyzes the impact of the fluctuation range on the barge linkage response rate, dynamically adjusts the association mapping path and feature matching ratio within the target range, and generates an abnormal intervention adjustment data set.

[0029] The dynamic feedback adjustment module adjusts the data set based on the abnormal intervention, analyzes the linkage demand distribution ratio and response time of ship target transfer, adjusts the parameters of the target linkage path, and generates a ship transfer linkage feedback data table.

[0030] Example 1: When analyzing the degree of matching between the interconnection structure and the communication reliability of the equipment, the interconnection parameter parsing module of the system needs to first process the operating status information of the ship transfer equipment. From this information, the communication protocol parameters of the equipment, such as the type of communication protocol, transmission rate, data format, etc., are extracted. At the same time, the depth data, that is, the depth information of the position of the equipment in the interconnection layer, and the transfer coverage data, such as the spatial range involved in the transfer operation, the area covered by the equipment, etc. are extracted. Then a time window is set. This time window can be determined according to the actual operation requirements and the equipment operation cycle, such as being set to a complete cycle of a transfer operation or a specific time period. Within this time window, multiple time points are selected to match the data. By comparing the correlation between the data at different time points, the data with valid correlation is screened out, thereby obtaining the communication protocol parameters and depth data.

[0031] After obtaining the communication protocol parameters and depth data, the path must be matched and verified. This requires calculating the differences between the protocol parameters and the depth data, such as the compatibility and transmission efficiency differences between different communication protocols at specific depth levels. At the same time, the interconnection structure and depth distribution are modified based on changes in coverage. Changes in coverage may be caused by adjustments to the barge operation area, the addition or removal of equipment, or changes in location. These changes will affect the data transmission path and reliability. Therefore, the path parameters need to be adjusted based on the impact of coverage on the data to determine the matching between the protocol and depth.

[0032] Based on the matching of protocols and depth, perform device communication reliability analysis. Reliability analysis criteria should be set, which may include indicators such as communication success rate, data transmission latency, and error rate. Then, considering dynamic changes during device operation, such as changes in device load and the impact of environmental factors, evaluate the distribution of protocols under different coverage conditions. By comparing stability indicators under different conditions, such as differences in communication stability under different coverage conditions, the coverage conditions are optimized to ultimately determine the degree of match between the interconnect structure and device communication reliability.

[0033] When obtaining the interconnection parameter parsing parameter set, we first analyze the device's communication transmission performance and reliability changes under different operating conditions, based on the previously determined match between the interconnection structure and the device's communication reliability. Different operating conditions may include factors such as the device's operating mode, load size, and ambient temperature, all of which affect the device's communication transmission efficiency and reliability. A weighted calculation is then performed on the device's protocol distribution. This weighting takes into account the importance and impact of different protocols under different operating conditions. For example, certain protocols are more reliable under high load conditions, so they are given higher weights. This results in a preliminary hierarchical parsing of the device's requirements.

[0034] Based on the initial hierarchical resolution requirements of the devices, analyze the hierarchical balance between devices. This requires identifying the relationship between inter-device communication transmission efficiency and load distribution. For example, if some devices in the hierarchy have a heavier communication load, this may lead to load imbalance and affect overall system performance. By modifying the device hierarchical resolution parameters, such as adjusting the device's hierarchical position and communication priority, a hierarchical resolution dataset between devices is obtained.

[0035] Combining the inter-device hierarchical resolution dataset with the reliability matching results, the interconnection hierarchy between devices is allocated. During this allocation process, the system's balancing and reliability requirements must be optimized and matched, for example, ensuring load balancing between devices while also ensuring communication reliability. This series of operations ultimately results in an interconnection parameter resolution parameter set. This parameter set, which includes information such as device-level resolution control values ​​and coverage balancing parameters, provides accurate parameter support for subsequent modules, ensuring more efficient and reliable interconnection control across the entire system. Throughout this process, each step requires precise data processing and full consideration of various factors to ensure the accuracy and effectiveness of the final results. For example, during data extraction, data integrity and accuracy must be ensured; during analysis, all factors that may affect communication reliability and hierarchical resolution must be fully considered; and during the allocation and optimization process, appropriate adjustments and configurations must be made based on the overall system performance and requirements.

[0036] Example 2: When the barge feature extraction module obtains the characteristic values ​​and distribution changes in the ship barge scene space, it must first work based on the interconnected parameter parsing parameter set. Extract the depth data in the ship barge scene space from the interconnected parameter parsing parameter set. The depth data here covers the depth information of different positions in the scene space, such as the water depth of the barge operation area, the depth position of the equipment installation, etc. Then filter the depth points in each time period. The time period can be determined according to the process of the barge operation or the cycle of data collection, such as one time period per hour or day. Combined with the depth change trend of different positions in the space, analyze the volatility of the depth, such as whether the depth of different positions changes frequently in a certain time period, the magnitude of the change, etc., so as to obtain the depth data in the ship barge scene space.

[0037] After obtaining the depth data, calculations are performed for each depth point and the corresponding characteristic value. Characteristic values ​​can include physical or chemical parameters related to the barge scene, such as temperature, pressure, flow rate, and substance concentration. By analyzing the relationship between depth and characteristic values, such as the temperature change pattern as the depth increases, the corresponding relationship between pressure and depth, etc., the characteristic change amount of each measurement point is identified. At the same time, combined with the equipment structure parameters, such as the installation position and measurement range of the sensor, the characteristic changes at different positions are compared to obtain the characteristic distribution and distribution change data. For example, in different areas of the barge scene, is the distribution of characteristic values ​​uniform? Does the distribution change over time?

[0038] Based on the feature distribution and distribution change data, the overall feature distribution within the ship-transfer scenario space is further analyzed. This requires comprehensive consideration of the feature values ​​in each region to determine whether the overall distribution meets expectations or contains anomalies. Feature distribution changes are optimized in conjunction with depth data. For example, in areas with large depth variations, the reasonableness of feature distribution changes is analyzed to determine whether adjustments are necessary. Furthermore, the impact of feature changes on equipment performance is analyzed, such as whether drastic changes in certain feature values ​​will lead to unstable equipment operation and decreased measurement accuracy. Through these analyses, stable feature configurations under different operating conditions are determined, ultimately resulting in the feature values ​​and distribution changes within the ship-transfer scenario space.

[0039] When obtaining the optimized parameter set for the transshipment feature, first determine the time series of the feature changes based on the feature values ​​and distribution changes in the ship transshipment scenario space. The time series needs to record the changes in the feature values ​​in chronological order, such as recording the changes in a certain feature value in one day in units of minutes. Compare the current feature value with the original feature data. The original feature data can be the feature value during the initial operation of the equipment or the feature data during historical normal operation. Analyze the changes in the feature distribution at each moment, such as whether the feature value at a certain moment deviates from the normal range and what the trend of the change is. Define the corresponding thresholds based on the equipment status partition. For example, divide the equipment operation status into normal, warning, fault and other areas, set the corresponding feature value threshold for each area, and generate a preliminary feature change parameter set.

[0040] Analyze the preliminary feature variation parameter set, focusing on the impact of spatial features on the stability of the device's precision output. For example, examine whether fluctuations in eigenvalues ​​lead to instability in the device's measurement accuracy, and how different combinations of eigenvalues ​​affect the precision output. Identify the correlation between features and precision output, for example, whether changes in certain eigenvalues ​​are directly causal with decreases in precision output. Calculate the segment precision stability impact coefficient, which measures the impact of feature variations on precision stability within different segments.

[0041] By analyzing the impact coefficients of section accuracy and stability and incorporating spatial feature variation parameters, the balance of feature distribution is adjusted. For example, in sections with uneven feature distribution, equipment operating parameters and the layout of measurement points can be adjusted to achieve a more balanced feature distribution. Feature control data is optimized, for example, by determining the appropriate feature control range and control method, ultimately generating an optimized parameter set for the barge feature. This parameter set, which includes information such as optimized feature distribution parameters and accuracy and stability-related parameters, provides more accurate feature data support for subsequent interconnected modeling modules, ensuring that the system can better adapt to feature variations within the scene space during the barge process, thereby improving system stability and reliability. Throughout the implementation process, meticulous data processing is required at every stage, comprehensively considering the impact of various factors. For example, when extracting depth data and feature values, data accuracy and real-time performance must be ensured. When analyzing the impact of feature variations on equipment performance, the actual operating conditions and structural characteristics of the equipment must be considered. When adjusting the balance of feature distribution, the overall system requirements and practical feasibility must be comprehensively considered.

[0042] Example 3: When the interconnected association modeling module obtains the results of dynamic control of interconnected associations, it is necessary to perform specific operations based on the barge characteristic optimization parameter set. Extract the associated adsorption data of the barge surface. The associated adsorption data here include information such as the adsorption intensity and adsorption rate of the barge surface with related substances or energy. For example, in the liquid barge scenario, the adsorption of specific liquid molecules by the barge surface. At the same time, monitor the adsorption rate associated with different material surfaces. Different material surfaces may refer to the surfaces of different components of the barge equipment, such as metal materials, plastic materials, etc. The surface characteristics of these materials will affect the adsorption rate. Combined with external environmental factors of time and water area, such as the length of operation time, temperature, pH, flow rate, etc. of the water area, infer the associated mapping characteristics, such as the change pattern of adsorption over time and the mapping mode under different water environments. By defining the adsorption and mapping rate coefficients, these influencing factors are quantified to generate an adsorption mapping dynamic parameter set.

[0043] After obtaining the dynamic parameter set for the adsorption mapping, its impact on the associated flow rate and distribution is analyzed. The associated flow rate may refer to the flow rate of matter or energy during the transfer process, while the distribution refers to its distribution within the transfer space. Based on the requirements for the distribution of the associated strength on the transfer surface, such as certain areas requiring stronger associated strength to ensure transfer stability and efficiency, the ratio between the mapping path and the associated input rate is optimized. For example, when the associated strength requirement in a certain area is high, the number of mapping paths in that area is increased or the associated input rate is increased. By calculating the adjustment coefficient of the associated strength distribution trend, the magnitude and direction of the adjustment are determined, and the associated strength control results are generated.

[0044] The results of the association strength control are analyzed to further adjust the proportional relationship between the association input rate and the mapping path. This requires comprehensive consideration of the actual conditions of the barge process, such as the equipment's carrying capacity and energy consumption, to rationally allocate the distribution trend of the association strength. Combining the adsorption mapping parameters with the adjustment coefficient, after multiple calculations and adjustments, the dynamic control results of the interconnected associations are finally obtained. This result can guide the system to dynamically adjust the association relationships during the barge process to adapt to different barge requirements and environmental changes.

[0045] When the linkage abnormal intervention module obtains the abnormal intervention adjustment data set, it first monitors the feature matching rate and association offset during the real-time linkage process of the devices based on the results of dynamic interconnection and association control. The feature matching rate refers to the degree to which the detected features match the expected features, while the association offset refers to the degree to which the association relationship deviates from the preset value. During the monitoring process, the data fluctuation range is identified and outliers caused by reasons such as equipment failure are eliminated to prevent these outliers from interfering with subsequent analysis. By analyzing the average matching rate of the data, feature and association fluctuation data are obtained, such as the average level of feature matching rate and the magnitude of fluctuation over a period of time.

[0046] Analyze the impact of feature and correlation fluctuation ranges on the response rate of the barge linkage. Using the known barge response rate, analyze the relationship between correlation and features. For example, whether a decrease in feature matching rate leads to a decrease in response rate, and the extent to which an increase in correlation offset affects the response rate. By calculating the response rate under different fluctuation ranges, we can obtain response rate impact data, such as the change in response rate when the feature matching rate fluctuates within ±5%.

[0047] Based on the response rate impact data, dynamically adjust the association mapping paths and feature matching ratios within the target range. This adjustment is based on the relationship between the response rate impact data, features, and the associated fluctuation range. For example, when the response rate drops below a certain level, increase the number of association mapping paths or improve the feature matching ratio to improve the response rate. Simultaneously, assign the association flow rate and feature control range to ensure the system operates properly after dynamic adjustments, ultimately generating an abnormal intervention adjustment dataset.

[0048] Example 4: When the dynamic feedback adjustment module obtains the ship-transshipment linkage feedback data table, it needs to carry out specific work based on the abnormal intervention adjustment data set. For example, in a scenario where a cargo ship is carrying out crude oil transshipment operations, the abnormal intervention adjustment data set in the scenario is first used to analyze the ship-target transshipment linkage demand distribution and response time. Specifically, the linkage strength data at different response time points are collected. The linkage strength data here may include the collaborative operation strength between equipment, energy transmission strength, etc., such as the collaborative work strength of oil pumps and valves at different response times. Arrange the linkage time distribution, such as recording the time nodes of each stage from starting the transshipment equipment to reaching a stable operating state, analyze the intensity change trend and classify the data, for example, divide the change of linkage strength over time into the startup stage, the stable stage and the end stage, and obtain the ship-transshipment linkage distribution data.

[0049] Based on the obtained ship-to-ship transfer linkage distribution data, the target linkage path parameters are adjusted. Taking the linkage between oil pipelines and storage tanks in crude oil transfer as an example, the optimal response time and strength distribution of the transfer linkage are analyzed. By comparing the strength changes under different linkage conditions, such as the difference in linkage strength under different oil flow rates and pipeline pressures, the operating conditions of linkage time, water flow rate, and linkage strength are adjusted. For example, when the oil flow rate increases, the linkage time is appropriately extended to ensure stable pipeline pressure, while the linkage strength is adjusted to maintain oil transfer efficiency, thus obtaining the target linkage path parameters.

[0050] Based on the target linkage path parameters, the linkage conditions are adjusted according to the current operating parameters to control the variable relationships between response time, water flow rate, and correlation strength. In crude oil transfer, the current operating parameters may include the speed of the oil pump and the valve opening of the pipeline. Based on the adjusted target linkage path parameters, such as controlling the response time within a specific range, adjusting the water flow rate (here, the flow rate of crude oil in the pipeline) to a certain interval, and setting the correlation strength to a corresponding value, real-time linkage is performed based on these adjusted parameters, and various data during the linkage process are recorded to generate a ship transfer linkage feedback data table.

[0051] The steps for adjusting the target linkage path parameters are as follows: Using the crude oil transfer scenario as an example, based on the distribution data of ship-to-ship linkage, the intensity peaks under different linkage conditions are extracted, such as the linkage intensity peak at an oil flow rate of 500 cubic meters per hour and a pipeline pressure of 0.8 MPa. Time thresholds are set to divide the effective linkage interval, for example, from 10 minutes after the oil pump is started to 20 minutes before the end of the transfer operation. The intensity change rate within this interval is compared with the baseline value to screen out key parameters that affect linkage efficiency, such as oil flow rate, pipeline pressure, and valve opening.

[0052] The selected key parameters are weighted and combined with the barge space structural parameters to assess the impact of parameter adjustments on linkage coverage. In crude oil barge, barge space structural parameters include pipeline length, diameter, and storage tank capacity. For example, adjustments to oil flow rate affect the crude oil flow rate within the pipeline, which in turn affects linkage coverage (i.e., the range of crude oil transfer within the pipeline). By calculating parameter adjustment weight coefficients, the importance of each key parameter is ranked.

[0053] Adjust the weight coefficients based on the parameters and adjust the operating conditions for linkage time, water velocity, and correlation strength according to the weight priority. For example, if the oil flow rate has the highest weight coefficient, adjust the oil flow rate to the target value first, and adjust the linkage time accordingly to match the flow rate change. This ensures that the adjusted parameters meet the requirements for optimal response time and intensity distribution, thereby obtaining the target linkage path parameters.

[0054] Throughout the implementation process, it is necessary to closely integrate with specific lightering operation scenarios. For example, in another scenario involving a bulk carrier lightering ore, the distribution of ship-targeted lightering linkage demand and response time analysis will vary depending on the density of the ore and the type of lightering equipment. The collected linkage strength data may involve the collaborative operation strength of the grab and conveyor belt. When compiling the linkage time distribution, factors such as the grab's grab cycle and the conveyor belt's operating speed must be considered. When analyzing strength changes under different linkage conditions, it is necessary to pay attention to the impact of parameters such as the ore loading capacity and the grab's lifting speed on the linkage strength.

[0055] When adjusting target linkage path parameters, consider the barge space structure parameters, including cargo hold volume and conveyor belt length. The weighting of these parameters should consider the balance between ore loading efficiency and equipment wear. For example, if the grab frequency weight coefficient is high, prioritize adjusting the grab frequency while also adjusting the linkage time to minimize excessive equipment wear. This ensures that the adjusted parameters meet the actual needs of bulk cargo barge operations.

[0056] Example 5: Taking a container ship performing a fuel oil transfer operation as an example, the real-time linkage control steps must be implemented based on target linkage path parameters. In this scenario, the target linkage path parameters may include parameters such as the linkage time sequence during fuel oil transfer, the water velocity gradient (i.e., the fuel oil flow rate in the pipeline), and the correlation strength. First, the linkage time sequence and water velocity gradient are set. For example, the transfer operation can be divided into the initial fuel injection phase, the stable fuel transfer phase, and the final emptying phase. Each phase is assigned a different time node and corresponding water velocity gradient. For example, the initial flow rate is set to 1.5 m / s, and the stable phase is increased to 2.5 m / s. Simultaneously, a mapping model is established between the correlation strength, time, and water velocity. This model comprehensively considers the synergistic relationship between equipment during the fuel oil transfer process, such as the variation of the correlation strength between the fuel pump speed and pipeline pressure over time and flow rate. The target parameter values ​​for each time period are determined. For example, during the stable fuel transfer phase, the correlation strength target value is set to maintain the pipeline pressure at 0.6 MPa.

[0057] During the real-time linkage process of the barge operation, the actual values ​​of the parameters are continuously monitored, such as monitoring the actual pipeline pressure through a pressure sensor and monitoring the actual fuel oil flow rate through a flow meter. The deviation between the actual value and the target value is calculated. For example, if the actual value of the pipeline pressure in the stable phase is 0.55 MPa, the deviation from the target value of 0.6 MPa is -0.05 MPa. Based on the deviation, a parameter compensation is generated, such as increasing the speed of the oil pump to increase the pipeline pressure. The output of the linkage equipment is dynamically adjusted based on the parameter compensation, such as controlling the frequency converter of the oil pump to increase the speed, thereby correcting the deviation in time, water flow rate, and correlation strength to ensure that the real-time linkage process meets the target path parameter requirements. Throughout the process, the adjusted parameters and linkage status data are continuously recorded to complete the generation of the ship-to-ship linkage feedback data table.

[0058] Taking the barging operation of liquefied natural gas (LNG) carriers as an example, the target linkage path parameters may involve special requirements in low-temperature environments. When setting the linkage time sequence, the time for pre-cooling the LNG pipeline must be considered. For example, the pre-cooling stage lasts for 30 minutes to ensure that the pipeline temperature drops below -162°C. The water flow velocity gradient must be set according to the physical properties of LNG. The flow velocity is low in the initial pre-cooling stage to avoid stress damage to the pipeline due to sudden temperature changes. The flow velocity is increased to the design value during the stable gas transmission stage. When establishing a mapping model of correlation strength, time, and water flow velocity, it is necessary to pay attention to the correlation between the switching state of the cryogenic valve and the pipeline pressure and flow velocity. For example, in the pre-cooling stage, the correlation strength target value is set to ensure that the valve opens slowly to avoid excessive pressure fluctuations.

[0059] During real-time monitoring, temperature sensors monitor the temperature at various points in the pipeline, while pressure sensors monitor the pressure. If the actual pipeline temperature at a certain point during the precooling phase is -158°C, falling short of the target value of -162°C, a +4°C deviation is calculated, and parameter compensation is generated, such as increasing the precooling time or adjusting the refrigeration equipment's output power. Dynamic adjustments are made to the output of linked equipment, such as activating a backup chiller and extending the precooling phase, until the pipeline temperature reaches the target. During the adjustment process, various parameters are continuously monitored to ensure the safety and stability of LNG transfers. A linked feedback data sheet containing a complete record of these adjustments is generated.

[0060] In the scenario of iron ore being transported by bulk carriers, the target linkage path parameters need to take into account the ore's particle size and the mechanical characteristics of the transport equipment. When setting the linkage time sequence, the grab bucket's grab cycle needs to be considered. For example, each grab cycle is 60 seconds, including stages such as descent, grabbing, lifting, movement, and unloading. In this scenario, the water velocity gradient can be understood as the conveyor belt's operating speed. The speeds of different stages are set based on the ore's bulk density, such as 1.2 m / s for the initial loading stage and 1.8 m / s for the stable conveying stage. A mapping model is established between the association strength, time, and conveyor belt speed, focusing on the matching relationship between the grab bucket's grab frequency and the conveyor belt speed. For example, during the stable conveying stage, the association strength target value is set to maintain a 1:1.5 ratio between the grab bucket's grab frequency and the conveyor belt speed to prevent ore accumulation or idling on the conveyor belt.

[0061] During the real-time linkage process, the actual conveyor belt speed and grab bucket frequency are monitored. If the actual conveyor belt speed is 1.6 m / s, which is lower than the target value of 1.8 m / s, the deviation is calculated as -0.2 m / s, and parameter compensation is generated, such as adjusting the drive power of the conveyor belt motor. The equipment output is dynamically adjusted to increase the motor power to bring the conveyor belt speed to the target value, and the grab bucket frequency is adjusted accordingly to maintain the correlation strength ratio between the two. During this process, the accumulation of ore is also monitored to avoid ore spillage or equipment overload caused by speed adjustments, ensuring efficient and stable barge operations. Ultimately, a complete linkage feedback data table is generated.

[0062] In different transshipment scenarios, the parameter monitoring methods and adjustment strategies vary. When a tanker transships crude oil, it is necessary to monitor the tank level with a radar level gauge and the crude oil flow rate with a mass flow meter. If the rate of increase in the liquid level does not match the flow rate, it is necessary to check whether there is a leak in the pipeline or abnormal valve opening. When adjusting the correlation strength, it is necessary to consider the change in crude oil viscosity with temperature. For example, when the temperature decreases, the viscosity of crude oil increases, and the power of the oil pump needs to be increased accordingly to maintain the flow rate. When a chemical tanker transships corrosive liquids, real-time monitoring needs to focus on the corrosion of the pipeline material, and data is obtained through an online corrosion monitor. If the corrosion rate exceeds the threshold, it is necessary to adjust the linkage parameters, such as reducing the flow rate or shortening the transshipment time, to ensure equipment safety.

[0063] Regardless of the scenario, the core of real-time linkage control lies in achieving dynamic control of the linkage process through a closed-loop process based on target path parameters, including real-time monitoring, deviation calculation, compensation generation, and equipment adjustment. Each link must be closely integrated with the characteristics of the specific transfer object and the operating parameters of the equipment to ensure timely and accurate adjustments. For example, when adjusting parameter compensation, the response delay of the equipment must be considered to avoid parameter oscillation caused by excessive adjustment. When dynamically adjusting equipment output, the equipment's safe operating limits must be followed, such as the maximum speed of the oil pump and the opening and closing speed range of the valve.

[0064] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "includes," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0065] 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 multi-device interconnection control system for a ship-based barge system, characterized in that: The system comprises: The interconnection parameter parsing module, based on the operating status information of the ship-borne barge equipment, calls the equipment's communication protocol parameters, interconnection layer depth, and barge coverage data, analyzes the degree of match between the interconnection structure and the equipment's communication reliability, assigns equipment layer parsing control values ​​and coverage balancing parameters, and generates an interconnection parameter parsing parameter set; A lightering feature extraction module extracts feature values ​​and distribution variations in the ship-to-ship lightering scene space based on the interconnected parameter parsing parameter set, analyzes the impact of feature extraction device accuracy output on stability, adjusts the spatial feature distribution balance, and generates a lightering feature optimization parameter set; an interconnection association modeling module, which analyzes the association and mapping between the interconnection level and the barge characteristics based on the barge characteristic optimization parameter set, adjusts the association input rate and the mapping path distribution ratio, redistributes the distribution trend and dynamic parameter value of the association strength, and generates the interconnection association dynamic control result; A linkage abnormality intervention module, based on the interconnected association dynamic control results, extracts the real-time feature matching rate and association offset during the ship-to-ship barge linkage process, analyzes the impact of the fluctuation range on the barge linkage response rate, dynamically adjusts the association mapping path and feature matching ratio within the target range, and generates an abnormality intervention adjustment data set; The dynamic feedback adjustment module analyzes the linkage demand distribution ratio and response time of the ship target lightering based on the abnormal intervention adjustment data set, adjusts the parameters of the target linkage path, and generates a ship lightering linkage feedback data table.

2. The multi-device interconnection control system for shipboard barge system according to claim 1 is characterized in that: The analysis of the matching degree between the interconnection structure and the device communication reliability includes: Based on the operating status information of the ship-borne transfer equipment, the communication protocol parameters, depth data and transfer coverage data of the equipment are extracted. The time window is set, and the time points are selected to match the data. By comparing the data correlation and performing data screening, the communication protocol parameters and depth data are obtained. Based on the communication protocol parameters and depth data, the path is matched and checked, the difference between the protocol and the depth is calculated, the interconnection structure and the depth distribution are corrected in combination with the coverage change, and the path parameters are adjusted according to the impact of the coverage on the data to obtain the protocol and depth matching situation; Based on the protocol and depth matching, perform device communication reliability analysis, set reliability analysis standards, combine the dynamic changes of device operation, evaluate the protocol distribution under differentiated coverage conditions, compare stability indicators and optimize coverage conditions, and obtain the degree of matching between the interconnection structure and device communication reliability.

3. The multi-device interconnection control system for shipboard transfer system according to claim 2 is characterized in that: The steps for obtaining the interconnection parameter parsing parameter set are specifically as follows: Based on the matching degree between the interconnection structure and the device communication reliability, analyze the communication transmission and reliability changes of the device under differentiated operating conditions, and perform weighted calculation on the protocol distribution of the device to obtain the preliminary hierarchical analysis requirements of the device; Based on the preliminary hierarchical analysis requirements of the devices, the hierarchical balance between the devices is analyzed, the relationship between the communication transmission efficiency and the load distribution between the devices is identified, and the device hierarchical analysis parameters are modified to obtain the hierarchical analysis data set between the devices; Combining the inter-device hierarchical parsing data set with the reliability matching result, the interconnection hierarchies between the devices are allocated, the required balance and reliability requirements are optimized and matched, and an interconnection parameter parsing parameter set is obtained.

4. The multi-device interconnection control system for shipboard transfer system according to claim 3 is characterized in that: The steps for obtaining the characteristic values ​​and distribution changes in the ship-transfer scene space are specifically as follows: Based on the interconnected parameter parsing parameter set, extract the depth data in the ship-transfer scene space, screen the depth points in each time period, combine the depth change trend of the differentiated positions in the space, analyze the depth volatility, and obtain the depth data in the ship-transfer scene space; Based on the depth data in the ship-transfer scene space, each depth point and the corresponding characteristic value are calculated. By analyzing the relationship between depth and characteristics, the characteristic change amount of each measurement point is identified. Combined with the equipment structure parameters, the characteristic change of the differentiated positions is compared to obtain the characteristic distribution and distribution change data; Based on the feature distribution and distribution change data, the overall feature distribution in the ship transfer scenario space is analyzed, the feature distribution change is optimized in combination with the depth data, the impact of the feature change on the equipment performance is analyzed, the stable feature configuration under differentiated operating conditions is determined, and the feature value and distribution change in the ship transfer scenario space are obtained.

5. The multi-device interconnection control system for shipboard transfer system according to claim 4 is characterized in that: The steps for obtaining the optimized parameter set of lightering characteristics are specifically as follows: Based on the feature values ​​and distribution changes in the ship-transfer scene space, a time series of feature changes is determined, current feature values ​​are compared with original feature data, feature distribution changes at each moment are analyzed, and corresponding thresholds are defined according to equipment status partitions to generate a preliminary feature change parameter set; Analyze the preliminary feature change parameter set, analyze the impact of the features in the space on the stability of the equipment precision output, identify the correlation between the features and the precision output, and calculate the section precision stability influence coefficient; By analyzing the influence coefficient of the accuracy stability of the section, combining the spatial feature change parameters, adjusting the feature distribution balance, optimizing the feature control data, and generating the optimized parameter set of the barge feature.

6. The multi-device interconnection control system for ship transfer system according to claim 5, characterized in that: The steps for obtaining the interconnection dynamic control result are specifically as follows: Based on the barge characteristic optimization parameter set, the associated adsorption data of the barge surface is extracted, the adsorption rate of the associated on the surface of different materials is monitored, the associated mapping characteristics are inferred in combination with the external environmental factors of time and water area, the adsorption and mapping rate coefficients are defined, and the adsorption mapping dynamic parameter set is generated; Analyze the impact of the adsorption mapping dynamic parameter set on the correlation flow rate and distribution, optimize the ratio between the mapping path and the correlation input rate according to the requirements of the correlation intensity distribution on the barge surface, calculate the adjustment coefficient of the correlation intensity distribution trend, and generate the correlation intensity control result; The association strength control result is analyzed, the proportional relationship between the association input rate and the mapping path is adjusted, the distribution trend of the association strength is allocated, and the adsorption mapping parameter and the adjustment coefficient are combined to obtain the interconnection dynamic control result.

7. The multi-device interconnection control system for shipboard transfer system according to claim 6, characterized in that: The steps for obtaining the abnormal intervention adjustment data set are specifically as follows: Based on the interconnected association dynamic control results, the monitoring equipment monitors the feature matching rate and associated offset in real time during the linkage process, identifies the fluctuation range, eliminates equipment failure outliers, analyzes the average matching rate of the data, and obtains feature and associated fluctuation data; Analyze the impact of the characteristics and associated fluctuation range on the barge linkage response rate, use the known barge response rate, analyze the relationship between the correlation and the characteristics, calculate the response rate under the differentiated fluctuation range, and obtain the response rate impact data; Based on the response rate impact data, the association mapping path and feature matching ratio within the target range are dynamically adjusted. Adjustments are made based on the relationship between the response rate impact data and the features and associated fluctuation ranges. The associated flow rate and feature control range are allocated to generate an abnormal intervention adjustment data set.

8. The multi-device interconnection control system for shipboard transfer system according to claim 7, characterized in that: The specific steps for obtaining the ship-to-ship transfer linkage feedback data table are as follows: Based on the abnormal intervention adjustment data set, the distribution of ship target transfer linkage demand and response time are analyzed, linkage strength data at differentiated response time points are collected, linkage time distribution is sorted, intensity change trends are analyzed and data are classified to obtain ship transfer linkage distribution data; Based on the ship-to-ship linkage distribution data, target linkage path parameters are adjusted, the optimal response time and intensity distribution of the barge linkage are analyzed, and by comparing the intensity changes under differentiated linkage conditions, the operating conditions of the linkage time, water flow velocity, and correlation intensity are adjusted to obtain the target linkage path parameters; Based on the target linkage path parameters, the linkage conditions are adjusted according to the current operating parameters, the variable relationship between the response time, water flow velocity, and correlation strength is controlled, and real-time linkage is performed according to the adjusted parameters to obtain a ship transfer linkage feedback data table.

9. The multi-device interconnection control system for shipboard transfer system according to claim 8, characterized in that: The steps for adjusting the target linkage path parameters are as follows: Based on the ship-to-ship linkage distribution data, the intensity peak points under differentiated linkage conditions are extracted, time thresholds are set to divide effective linkage intervals, and the intensity change rate within the intervals is compared with the baseline value to screen the key parameters affecting the linkage efficiency; Assign weights to the key parameters, evaluate the impact of parameter adjustment on linkage coverage in combination with the lightering space structure parameters, and calculate parameter adjustment weight coefficients; The weight coefficient is adjusted according to the parameters, and the operating conditions of linkage time, water flow velocity, and correlation strength are adjusted according to the weight priority to ensure that the adjusted parameters meet the requirements of optimal response time and intensity distribution, and obtain the target linkage path parameters.

10. The multi-device interconnection control system for ship transfer system according to claim 9, characterized in that: The control steps of the real-time linkage are specifically as follows: Based on the target linkage path parameters, the linkage time series and the water area flow velocity gradient are set, a mapping model of the correlation strength, time, and water area flow velocity is established, and the parameter target values ​​within each time period are determined; Monitor the actual value of the parameter during real-time linkage, calculate the deviation between the actual value and the target value, and generate the parameter compensation; The linkage equipment output is dynamically adjusted according to the parameter compensation amount, and the deviations of time, water velocity, and correlation strength are corrected to ensure that the real-time linkage process meets the target path parameter requirements and complete the generation of the ship transfer linkage feedback data table.

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