Intelligent Control Method and System for Sinking Mat Vessel Operation Based on Ship Internet of Things

Abstract

The present invention discloses an intelligent control method and system for the operation of a mattress-laying vessel based on the ship Internet of Things, belonging to the technical field of ship engineering. The method includes the following steps: S1: Using Internet of Things technology, collect real-time data on the operation of the mattress-laying vessel based on the ship Internet of Things; S2: Process and analyze the real-time data on the operation of the mattress-laying vessel based on the ship Internet of Things, timely discover potential safety hazards and problems, and determine the risk prediction result of the mattress-laying vessel operation; S3: According to the risk prediction result of the mattress-laying vessel operation, perform intelligent real-time adjustment control on the vessel to keep the vessel within a safe range during the mattress-laying process. The present invention solves the problem that the existing technology cannot effectively realize the intelligent control of the mattress-laying operation of the vessel. The present invention can realize functions such as real-time monitoring, remote control, and automatic control, can improve the safety and efficiency of the vessel during the mattress-laying process, can reduce the risk of human operation errors, improve the accuracy and safety of the mattress-laying work, and reduce the operation cost.

Description

Technical Field

[0001] The present invention relates to the technical field of ship engineering, and specifically to an intelligent control method and system for the operation of a mattress-laying ship based on the ship Internet of Things. Background Art

[0002] A mattress-laying ship, also known as a mattress-sinking ship, is designed to meet the engineering needs of soft foundation riverbed structures for the regulation of the Yangtze River and coastal waterways, combined with the construction of the laying of slope protection and bottom protection flexible mattresses in the Yangtze River waters. When a ship operates at sea, it is necessary to observe and monitor the water environment around the hull to ensure navigation safety.

[0003] Chinese Patent Application No. CN108279598A discloses a monitoring and protection system for the anchor cables of a mattress-laying ship and its protection method, including optoelectronic switches for detecting the cable laying device and the cable, etc. The monitoring, warning and protection system for the anchor rope equipment can assist ship positioning, avoid major accidents of the anchor steel rope equipment, save a large amount of human and material costs, and avoid affecting the construction progress and efficiency due to equipment failures. However, this patent has the following defects:

[0004] When existing ships perform mattress-laying operations, they usually need to manually control the position and speed of the ship. This operation method has many problems and risks. For example, improper operation may lead to accidents such as ship out of control and collision. At the same time, it will also waste a large amount of human, material and time resources, and cannot effectively achieve the intelligent control of ship mattress-laying operations. Summary of the Invention

[0005] The purpose of the present invention is to provide an intelligent control method and system for the operation of a mattress-laying ship based on the ship Internet of Things, which can realize functions such as real-time monitoring, remote control and automatic control, improve the safety and efficiency of the ship during mattress-laying, reduce the risk of human operation errors, improve the accuracy and safety of mattress-laying work, reduce operating costs, and solve the problems raised in the above background art.

[0006] To achieve the above purpose, the present invention provides the following technical solutions:

[0007] An intelligent control method for the operation of a mattress-laying ship based on the ship Internet of Things includes the following steps:

[0008] S1: Using Internet of Things technology, collect ship operation status data ship sta-data , ship operation environment data ship env-data and mattress-laying equipment status data Lay sta-data , determine the real-time data of the operation of the mattress-laying ship based on the ship Internet of Things, and transmit the collected real-time data of the operation of the mattress-laying ship based on the ship Internet of Things to the cloud computing platform based on wireless communication technology;

[0009] S2: Process and analyze the real-time data of the mattress-laying vessel operation based on the ship Internet of Things through a cloud computing platform, and perform risk prediction on the real-time data of the mattress-laying vessel operation based on deep learning technology to timely discover potential safety hazards and problems, and determine the risk prediction results of the mattress-laying vessel operation;

[0010] S3: According to the risk prediction results of the mattress-laying vessel operation, perform intelligent real-time adjustment and control on the R pos running position, R spe speed, R cou heading, R att attitude R dra and draft of the vessel, so that the vessel always remains within a safe range during the mattress-laying process and can respond to emergencies in a timely manner.

[0011] Preferably, in the above S1, when collecting the real-time data of the mattress-laying vessel operation based on the ship Internet of Things, the following operations are performed:

[0012] Based on the Internet of Things technology, use sensors to R pos real-time monitor and collect the R spe running position, R cou speed, R att heading, R dra attitude ship sta-data and draft of the mattress-laying vessel during operation to obtain the vessel operation status data;

[0013] Based on the Internet of Things technology, use sensors to W sp real-time monitor and collect the W hei wind speed, W vel wave height, W tem water flow speed, M phe water temperature ship env-data and meteorological conditions of the mattress-laying vessel during operation to obtain the vessel operation environment data;

[0014] Based on the Internet of Things technology, use sensors to R sta real-time monitor the F inf operation status,L sit Perform real-time monitoring and acquisition to obtain the status data of the laying equipment Lay sta-data ;

[0015] Among them, based on the ship operation status data ship sta-data , ship operation environment data ship env-data and the status data of the laying equipment Lay sta-data , determine the real-time data of the laying ship operation based on the ship Internet of Things.

[0016] Preferably, in S1, the real-time data of the laying ship operation based on the ship Internet of Things collected is transmitted to the cloud computing platform, and the following operations are performed:

[0017] Set the bandwidth of the communication channel corresponding to the cloud computing platform at the initial operation to be lower than the rated bandwidth of the communication channel;

[0018] Real-time monitor the data volume corresponding to each laying ship operation real-time data transmission;

[0019] Extract the data volume growth rate between the data volumes corresponding to every two adjacent real-time data transmissions;

[0020] Compare the data volume growth rate between the data volumes corresponding to every two adjacent real-time data transmissions with a preset data volume growth rate threshold;

[0021] When the data volume growth rate exceeds the preset data volume growth rate threshold, then retrieve the data volume corresponding to each laying ship operation real-time data transmission;

[0022] Use the data volume corresponding to each laying ship operation real-time data transmission to obtain the data volume prediction value corresponding to the data transmission;

[0023] Among them, the data volume prediction value is obtained through the following formula:

[0024]

[0025] Among them, S represents the data volume prediction value; C max represents the maximum data volume corresponding to the real-time data transmission; C f represents the variance of the data transmission volume corresponding to the laying ship operation real-time data transmission; C p represents the average data volume corresponding to the real-time data transmission; G p represents the average time interval corresponding to the laying ship operation real-time data transmission; G max represents the maximum time interval corresponding to the laying ship operation real-time data transmission; g represents the variance of the time interval corresponding to the laying ship operation real-time data transmission; Ct represents the data transmission data volume of other data during the real-time data transmission of the paving ship operation; r represents the variance corresponding to the comprehensive data transmission time interval of other data except the real-time data of the paving ship operation; f p represents the comprehensive data transmission frequency of other data except the real-time data of the paving ship operation; f c represents the data transmission frequency of the real-time data of the paving ship operation;

[0026] Set the reserved bandwidth for the cloud computing platform according to the predicted data volume value.

[0027] Preferably, when setting the reserved bandwidth for the cloud computing platform according to the predicted data volume value, perform the following operations:

[0028] Extract the predicted data volume value;

[0029] Use the predicted data volume value to obtain the predicted load of the communication channel corresponding to the cloud computing platform;

[0030] Among them, the predicted load is obtained through the following formula:

[0031]

[0032] Among them, L represents the predicted load; L0 represents the actual load of the current communication channel; C b represents the data volume standard deviation corresponding to the real-time data transmission; C tb represents the data transmission data volume standard deviation of other data during the real-time data transmission of the paving ship operation; C tp represents the average value of the data transmission data volume of other data during the real-time data transmission of the paving ship operation; S represents the predicted data volume value; C e represents the rated bandwidth of the communication channel;

[0033] Compare the predicted load with a preset load reference value;

[0034] When the predicted load exceeds the preset load reference value, set the reserved bandwidth using the predicted load;

[0035] Among them, the reserved bandwidth is obtained through the following formula:

[0036]

[0037] Among them, B represents the reserved bandwidth; B c represents the preset reserved bandwidth base value; P represents the actual bandwidth utilization rate of the current communication channel; L represents the predicted load; L yIt represents a preset load reference value; λ represents a sensitivity coefficient for preset bandwidth adjustment, and the sensitivity coefficient is obtained through experiments according to actual application requirements;

[0038] Release the bandwidth of the communication channel corresponding to the cloud computing platform according to the reserved bandwidth, and transmit the real-time data of the laying ship operation based on the ship Internet of Things collected according to the released bandwidth to the cloud computing platform;

[0039] When the ratio between the released bandwidth value and the rated bandwidth of the communication channel reaches or exceeds the rated bandwidth occupancy ratio of the communication channel, a communication overload warning is issued.

[0040] Preferably, in S2, when processing the real-time data of the laying ship operation based on the ship Internet of Things, the following operations are performed:

[0041] Clean the real-time data of the laying ship operation based on the ship Internet of Things based on the Power Query tool;

[0042] Retrieve the real-time data of the laying ship operation based on the ship Internet of Things one by one, check whether there are duplicate values, missing values and abnormal values in the real-time data of the laying ship operation based on the ship Internet of Things, and process the duplicate values, missing values and abnormal values existing in the real-time data of the laying ship operation based on the ship Internet of Things;

[0043] Among them, remove the duplicate values in the real-time data of the laying ship operation based on the ship Internet of Things, and delete the duplicate data records;

[0044] Among them, use the interpolation method to fill in the missing values in the real-time data of the laying ship operation based on the ship Internet of Things to make the missing values in the real-time data of the laying ship operation based on the ship Internet of Things complete;

[0045] Among them, use the interpolation method to correct the abnormal values in the real-time data of the laying ship operation based on the ship Internet of Things to make the abnormal values in the real-time data of the laying ship operation based on the ship Internet of Things normal.

[0046] Preferably, in S2, when processing the real-time data of the laying ship operation based on the ship Internet of Things, the following operations are also performed:

[0047] Normalize the real-time data of the laying ship operation based on the ship Internet of Things based on Z-score standardization;

[0048] Unify the real-time data of the laying ship operation based on the ship Internet of Things to the same dimension and range, remove the dimension differences between the real-time data of the laying ship operation based on the ship Internet of Things, and determine the standardized real-time data of the laying ship operation;

[0049] Integrate the real-time data of the standardized laying vessel operation, integrate the real-time data of the standardized laying vessel operation into a unified data view, and verify the integrated real-time data of the standardized laying vessel operation to determine whether there are any omissions in the integrated real-time data of the standardized laying vessel operation;

[0050] After the data verification is qualified, securely store the integrated real-time data of the standardized laying vessel operation, and store the real-time data of the laying vessel operation in the database.

[0051] Preferably, in S2, analyze the real-time data of the laying vessel operation based on the ship Internet of Things, and perform the following operations:

[0052] According to the intelligent control requirements of the laying vessel operation based on the ship Internet of Things, collect the historical data of the laying vessel operation, and divide the collected historical data of the laying vessel operation to determine the training set and the test set;

[0053] Based on deep learning technology, use the training set to train the deep learning model, enabling the deep learning model to autonomously learn the process of laying vessel operation risk prediction, and determine the risk prediction model of the laying vessel operation based on deep learning;

[0054] Based on the test set, conduct performance testing on the risk prediction model of the laying vessel operation based on deep learning to determine whether the risk prediction model of the laying vessel operation based on deep learning can achieve the expected effect, and adjust the parameters and structure of the risk prediction model of the laying vessel operation based on deep learning according to the test results. After continuous iterative optimization, determine the optimal risk prediction model of the laying vessel operation;

[0055] Deploy the optimal risk prediction model of the laying vessel operation to the actual laying vessel operation risk prediction environment, and input the real-time data of the laying vessel operation into the optimal risk prediction model of the laying vessel operation;

[0056] Analyze the real-time data of the laying vessel operation based on the optimal risk prediction model of the laying vessel operation to determine whether there are any risk behaviors in the laying vessel operation, in order to timely discover potential safety hazards and problems, and determine the risk prediction result of the laying vessel operation.

[0057] Preferably, in S3, ensure that the ship always remains within a safe range during the laying process, and perform the following operations:

[0058] According to the risk prediction result of the laying vessel operation, analyze the real-time data of the laying vessel operation to find the reasons for the risk of the laying vessel operation. Based on the reasons for the risk of the laying vessel operation, determine the R pos operating position R spe speed R cou, attitude R att and draft R dra to perform intelligent real-time adjustment control;

[0059] When detecting potential safety hazards and problems, promptly issue a risk warning alarm for the operation of the paving ship, and automatically change the speed R spe or course R cou of the ship to ensure that the ship is always within a safe range during the paving process.

[0060] Preferably, in the step S3, in response to emergencies in a timely manner, the following operations are performed:

[0061] Obtain the real-time data of the paving ship operation, the risk prediction results of the paving ship operation, and the intelligent real-time adjustment control scheme, and organize and analyze the real-time data of the paving ship operation, the risk prediction results of the paving ship operation, and the intelligent real-time adjustment control scheme to form an intelligent control report for the paving ship operation, and display the intelligent control report for the paving ship operation in a visual form on the man-machine interface, so that users can intuitively remotely view the operation situation of the paving ship, and perform real-time optimization and monitoring management on the driving route and speed parameters of the ship to respond to emergencies in a timely manner.

[0062] According to another aspect of the present invention, there is provided an intelligent control system for the operation of a paving ship based on the ship Internet of Things, which is used to implement the intelligent control method for the operation of a paving ship based on the ship Internet of Things as described above, including:

[0063] The ship Internet of Things platform is used to connect and integrate the ship with the paving equipment and control system on the shore by using the Internet of Things technology, collect the real-time data of the paving ship operation based on the ship Internet of Things, and transmit the collected real-time data of the paving ship operation based on the ship Internet of Things to the cloud computing platform;

[0064] The cloud computing platform is used to process and analyze the real-time data of the paving ship operation based on the ship Internet of Things, perform real-time monitoring and prediction by using artificial intelligence algorithms, timely discover potential safety hazards and problems, and determine the risk prediction results of the paving ship operation;

[0065] The intelligent control platform is used to automatically or remotely control the driving speed, course of the ship, and the working state of the paving equipment according to the risk prediction results of the paving ship operation to respond to emergencies in a timely manner.

[0066] Compared with the prior art, the beneficial effects of the present invention are:

[0067] Through the Internet of Things technology, the present invention connects and integrates the ship with the paving equipment and control system on the shore, and collects the ship operation state data shipsta-data and the state data of the laying equipment ship env-data to determine the real-time data of the laying vessel operation based on the ship Internet of Things. By processing and analyzing the real-time data of the laying vessel operation based on the ship Internet of Things, and based on deep learning technology, risk prediction is carried out on the real-time data of the laying vessel operation, potential safety hazards and problems are discovered in a timely manner, the risk prediction result of the laying vessel operation is determined, and according to the risk prediction result of the laying vessel operation, the running position Lay sta-data of the ship R pos , speed R spe , course R cou , attitude R att and draft R dra are intelligently adjusted and controlled in real time, so that the ship always remains within a safe range during the laying process, emergencies are responded to in a timely manner, functions such as real-time monitoring, remote control, and automatic control can be realized, the safety and efficiency of the ship during the laying process can be effectively improved, the risk of human operation errors can be reduced, the accuracy and safety of the laying work can be improved, and the operation cost can be reduced. Brief Description of the Drawings

[0068] Figure 1 is a flowchart of the intelligent control method for the laying vessel operation based on the ship Internet of Things of the present invention;

[0069] Figure 2 is a structural diagram of the intelligent control system for the laying vessel operation based on the ship Internet of Things of the present invention. Detailed Embodiments

[0070] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0071] To solve the problem that the existing ships cannot effectively realize the intelligent control of the ship laying operation during the laying operation, please refer to Figure 1 - Figure 2 , the following technical solutions are provided in this embodiment:

[0072] An intelligent control method for the laying vessel operation based on the ship Internet of Things includes the following steps:

[0073] S1: Using Internet of Things technology, collect the ship operation state data shipsta-data and the state data of the laying equipment ship env-data to determine the real-time data of the laying barge operation based on the ship Internet of Things, and based on wireless communication technology, transmit the collected real-time data of the laying barge operation based on the ship Internet of Things to the cloud computing platform; Lay sta-data In this embodiment, to collect the real-time data of the laying barge operation based on the ship Internet of Things, the following operations are performed:

[0074] Based on the Internet of Things technology, use sensors to monitor and collect the running position

[0075] of the laying barge during operation R pos , speed R spe , heading R cou , attitude R att and draft depth in real time, and obtain the ship operation state data R dra ; ship sta-data ;

[0076] Among them, use GPS to monitor and collect the running position R pos of the laying barge during operation to obtain the laying barge operation position information;

[0077] Among them, use a speedometer to monitor and collect the running speed R spe of the laying barge during operation to obtain the laying barge operation speed information;

[0078] Among them, use a gyrocompass to monitor and collect the running heading R cou of the laying barge during operation to obtain the laying barge operation heading information;

[0079] Among them, use an attitude indicator to monitor and collect the running attitude R att of the laying barge during operation to obtain the laying barge operation attitude information;

[0080] Among them, use a fathometer to monitor and collect the draft depth R dra of the laying barge during operation to obtain the laying barge operation draft information;

[0081] Based on the Internet of Things technology, use sensors to measure the wind speed W sp , wave height Whei 、 Water flow velocity W vel 、 Water temperature W tem and meteorological conditions M phe are monitored and collected in real time to obtain ship operation environment data ship env-data ;

[0082] Among them, an anemometer is used to monitor and collect the wind speed during the operation of the mattress-laying ship W sp in real time to obtain the wind speed information of the mattress-laying ship operation;

[0083] Among them, a wave height meter is used to monitor and collect the wave height during the operation of the mattress-laying ship W hei in real time to obtain the wave height information of the mattress-laying ship operation;

[0084] Among them, a water flow velocimeter is used to monitor and collect the water flow velocity during the operation of the mattress-laying ship W vel in real time to obtain the water flow velocity information of the mattress-laying ship operation;

[0085] Among them, a temperature sensor is used to monitor and collect the water temperature during the operation of the mattress-laying ship W tem in real time to obtain the water temperature information of the mattress-laying ship operation;

[0086] Among them, a meteorological sensor is used to monitor and collect the meteorological conditions during the operation of the mattress-laying ship M phe in real time to obtain the meteorological information of the mattress-laying ship operation;

[0087] Based on the Internet of Things technology, sensors are used to monitor and collect the operating status R sta 、 fault information F inf and load conditions L sit of the shore-based mattress-laying equipment in real time to obtain the status data of the mattress-laying equipment Lay sta-data ;

[0088] Among them, based on the ship operation status data ship sta-data 、 ship operation environment data ship env-data and mattress-laying equipment status data Lay sta-data , the real-time data of the mattress-laying ship operation based on the ship Internet of Things is determined.

[0089] Specifically, in S1, the real-time data of the laying barge operation based on the ship Internet of Things is collected and transmitted to the cloud computing platform, and the following operations are performed:

[0090] Set the bandwidth of the communication channel corresponding to the cloud computing platform at the initial operation to be lower than the rated bandwidth of the communication channel;

[0091] Real-time monitor the data volume corresponding to each real-time data transmission of the laying barge operation;

[0092] Extract the data volume growth rate between the data volumes corresponding to every two adjacent real-time data transmissions;

[0093] Compare the data volume growth rate between the data volumes corresponding to every two adjacent real-time data transmissions with a preset data volume growth rate threshold;

[0094] When the data volume growth rate exceeds the preset data volume growth rate threshold, then retrieve the data volume corresponding to each real-time data transmission of the laying barge operation;

[0095] Use the data volume corresponding to each real-time data transmission of the laying barge operation to obtain a data volume prediction value corresponding to the data transmission;

[0096] Among them, the data volume prediction value is obtained through the following formula:

[0097]

[0098] Among them, S represents the data volume prediction value; C max represents the maximum data volume corresponding to the real-time data transmission; C f represents the variance of the data transmission volume corresponding to the real-time data transmission of the laying barge operation; C p represents the average data volume corresponding to the real-time data transmission; G p represents the average time interval corresponding to the real-time data transmission of the laying barge operation; G max represents the maximum time interval corresponding to the real-time data transmission of the laying barge operation; g represents the variance of the time interval corresponding to the real-time data transmission of the laying barge operation; C t represents the data transmission data volume of other data during the real-time data transmission of the laying barge operation; r represents the variance corresponding to the comprehensive data transmission time interval of other data except the real-time data of the laying barge operation; f p represents the comprehensive data transmission frequency of other data except the real-time data of the laying barge operation; f c represents the data transmission frequency of the real-time data of the laying barge operation;

[0099] Set the reserved bandwidth for the cloud computing platform according to the data volume prediction value.

[0100] The technical effects of the above technical solution are as follows: By setting the bandwidth at the initial operation of the communication channel corresponding to the cloud computing platform to be lower than the rated bandwidth of the communication channel and dynamically adjusting it according to subsequent data transmission requirements, this technical solution realizes the effective utilization of bandwidth resources. This avoids the over-allocation and waste of bandwidth resources, especially when the data transmission volume is low or stable. By real-time monitoring the data volume corresponding to the real-time data transmission of each paving ship operation, this technical solution can timely detect changes in the data transmission volume. When the data volume growth rate exceeds the preset data volume growth rate threshold, the system can quickly respond, further analyze and predict future data transmission requirements. Using the above technical solution and mathematical model, this technical solution can relatively accurately predict future data transmission volumes. Based on these predicted values, the cloud computing platform can reserve sufficient bandwidth resources in advance to ensure the smoothness and stability of data transmission. This predictive bandwidth reservation strategy helps reduce transmission delays or data loss problems caused by insufficient bandwidth. By dynamically adjusting the bandwidth and predictive reservation, this technical solution not only improves the utilization rate of cloud computing platform resources but also helps reduce operating costs. Since the bandwidth resources are allocated according to actual needs, unnecessary waste is avoided. This technical solution can dynamically adjust according to the real-time changes in the data transmission volume, thereby enhancing the flexibility and adaptability of the entire system. This helps to cope with fluctuations in data transmission volume under different operation scenarios and ensures that the system always operates in the best state.

[0101] In summary, through strategies such as real-time monitoring, dynamic adjustment, and predictive bandwidth reservation, this technical solution realizes the effective utilization and optimal configuration of bandwidth resources of the cloud computing platform, improves the stability and flexibility of the system, and reduces operating costs.

[0102] Specifically, for the cloud computing platform, bandwidth reservation settings are made according to the predicted data volume value, and the following operations are performed:

[0103] Extract the predicted data volume value;

[0104] Use the predicted data volume value to obtain the predicted load of the communication channel corresponding to the cloud computing platform;

[0105] Among them, the predicted load is obtained through the following formula:

[0106]

[0107] Among them, L represents the predicted load; L0 represents the actual load of the current communication channel; C b represents the standard deviation of the data volume corresponding to real-time data transmission; C tb represents the standard deviation of the data transmission volume of other data during the real-time data transmission of the paving ship operation; C tpDenote the average data transmission volume of other data during the real-time data transmission of the laying barge operation; S denotes the predicted data volume; C e Denote the rated bandwidth of the communication channel;

[0108] Compare the predicted load with a preset load reference value;

[0109] When the predicted load exceeds the preset load reference value, set a reserved bandwidth using the predicted load;

[0110] Among them, the reserved bandwidth is obtained through the following formula:

[0111]

[0112] Among them, B denotes the reserved bandwidth; B c Denote a preset reserved bandwidth base value; P denotes the actual bandwidth utilization rate of the current communication channel; L denotes the predicted load; L y Denote a preset load reference value; λ denotes a preset sensitivity coefficient for bandwidth adjustment, and the sensitivity coefficient is obtained through experiments according to actual application requirements;

[0113] Release the bandwidth of the communication channel corresponding to the cloud computing platform according to the reserved bandwidth, and transmit the collected real-time data of the laying barge operation based on the ship Internet of Things to the cloud computing platform according to the released bandwidth;

[0114] When the ratio between the released bandwidth value and the rated bandwidth of the communication channel reaches or exceeds the rated bandwidth occupancy ratio of the communication channel, perform a communication overload warning.

[0115] The technical effects of the above technical solution are as follows: By extracting the predicted data volume and using a complex predicted load formula to calculate the predicted load of the communication channel corresponding to the cloud computing platform, this technical solution can accurately predict future communication requirements. Based on this predicted load, the system can dynamically set the reserved bandwidth to ensure the smoothness and stability of data transmission. The reserved bandwidth formula in this technical solution takes into account multiple factors, including the preset reserved bandwidth base value, the actual bandwidth utilization rate of the current communication channel, the predicted load, the preset load reference value, and the preset sensitivity coefficient of bandwidth adjustment. This comprehensive consideration makes bandwidth management more flexible and can be dynamically adjusted according to actual application requirements. Through precise bandwidth reservation and flexible bandwidth management, this technical solution avoids over-allocation and waste of bandwidth resources. It ensures that bandwidth resources are available when needed and do not occupy excessive resources when not needed, thus improving the utilization rate of resources. When the ratio between the released bandwidth value and the rated bandwidth of the communication channel reaches or exceeds the rated bandwidth occupancy ratio of the communication channel, the system can issue a communication overload warning. This warning mechanism helps to timely detect and address potential communication bottlenecks, thus avoiding problems such as data transmission interruption or delay. Through dynamic bandwidth reservation and release, as well as the communication overload warning mechanism, this technical solution can improve the stability of the entire system. It ensures the continuity and reliability of data transmission, thereby improving the efficiency and quality of the laying vessel operation based on the ship Internet of Things. The bandwidth management strategy in this technical solution is scalable. As the data transmission volume increases or communication requirements change, the system can adapt to new requirements by adjusting parameters such as the reserved bandwidth base value, the load reference value, and the sensitivity coefficient. This scalability enables the system to continuously meet future possible data transmission and communication requirements.

[0116] In summary, through precise bandwidth reservation, flexible bandwidth management, efficient resource utilization, communication overload warning, and improvement of system stability and scalability, this technical solution realizes the effective utilization and optimal allocation of bandwidth resources in the cloud computing platform.

[0117] S2: Process and analyze the real-time data of the laying vessel operation based on the ship Internet of Things through the cloud computing platform. Based on deep learning technology, predict the risks of the real-time data of the laying vessel operation, timely discover potential safety hazards and problems, and determine the risk prediction results of the laying vessel operation.

[0118] In this embodiment, when processing the real-time data of the laying vessel operation based on the ship Internet of Things, the following operations are performed:

[0119] Clean the real-time data of the laying vessel operation based on the ship Internet of Things based on the Power Query tool.

[0120] Retrieve the real-time data of the mattress-laying vessel operation based on the ship Internet of Things one by one, check whether there are duplicate values, missing values and outliers in the real-time data of the mattress-laying vessel operation based on the ship Internet of Things, and process the duplicate values, missing values and outliers existing in the real-time data of the mattress-laying vessel operation based on the ship Internet of Things;

[0121] Among them, remove the duplicate values in the real-time data of the mattress-laying vessel operation based on the ship Internet of Things, and delete the duplicate data records;

[0122] Among them, use the interpolation method to fill in the missing values in the real-time data of the mattress-laying vessel operation based on the ship Internet of Things to make the missing values in the real-time data of the mattress-laying vessel operation based on the ship Internet of Things complete;

[0123] Among them, use the interpolation method to correct the outliers in the real-time data of the mattress-laying vessel operation based on the ship Internet of Things to make the outliers in the real-time data of the mattress-laying vessel operation based on the ship Internet of Things normal.

[0124] In this embodiment, when processing the real-time data of the mattress-laying vessel operation based on the ship Internet of Things, the following operations are also performed:

[0125] Based on Z-score standardization, normalize the real-time data of the mattress-laying vessel operation based on the ship Internet of Things;

[0126] Unify the real-time data of the mattress-laying vessel operation based on the ship Internet of Things to the same dimension and range, remove the dimensional differences between the real-time data of the mattress-laying vessel operation based on the ship Internet of Things, and determine the standardized real-time data of the mattress-laying vessel operation;

[0127] Integrate the standardized real-time data of the mattress-laying vessel operation, integrate the standardized real-time data of the mattress-laying vessel operation into a unified data view, and check the integrated standardized real-time data of the mattress-laying vessel operation to determine whether there are any omissions in the integrated standardized real-time data of the mattress-laying vessel operation;

[0128] After the data verification is qualified, securely store the integrated standardized real-time data of the mattress-laying vessel operation, and store the real-time data of the mattress-laying vessel operation in the database.

[0129] In this embodiment, when analyzing the real-time data of the mattress-laying vessel operation based on the ship Internet of Things, the following operations are performed:

[0130] According to the intelligent control requirements of the mattress-laying vessel operation based on the ship Internet of Things, collect the historical data of the mattress-laying vessel operation, and divide the collected historical data of the mattress-laying vessel operation to determine the training set and the test set;

[0131] Based on deep learning technology, use the training set to train the deep learning model, so that the deep learning model autonomously learns the process of predicting the risk of the mattress-laying vessel operation, and determine the risk prediction model of the mattress-laying vessel operation based on deep learning;

[0132] Based on the test set, perform performance testing on the risk prediction model for the laying vessel operation based on deep learning to determine whether the risk prediction model for the laying vessel operation based on deep learning can achieve the expected effect, and adjust the parameters and structure of the risk prediction model for the laying vessel operation based on deep learning according to the test results. After continuous iterative optimization, determine the optimal risk prediction model for the laying vessel operation;

[0133] Deploy the optimal risk prediction model for the laying vessel operation to the actual risk prediction environment of the laying vessel operation, and input the real-time data of the laying vessel operation into the optimal risk prediction model for the laying vessel operation;

[0134] Analyze the real-time data of the laying vessel operation based on the optimal risk prediction model for the laying vessel operation to determine whether there are risk behaviors in the laying vessel operation, so as to timely discover potential safety hazards and problems and determine the risk prediction result of the laying vessel operation.

[0135] S3: According to the risk prediction result of the laying vessel operation, for the running position of the ship R pos 、speed R spe 、heading R cou 、attitude R att and draft R dra perform intelligent real-time adjustment and control to keep the ship within a safe range during the laying process and timely respond to emergencies.

[0136] In this embodiment, to keep the ship within a safe range during the laying process, perform the following operations:

[0137] According to the risk prediction result of the laying vessel operation, analyze the real-time data of the laying vessel operation, find out the reasons for the risks in the laying vessel operation, and based on the reasons for the risks in the laying vessel operation, for the running position of the ship R pos 、speed R spe 、heading R cou 、attitude R att and draft R dra perform intelligent real-time adjustment and control;

[0138] When detecting potential safety hazards and problems, timely issue a risk warning alarm for the laying vessel operation and automatically change the speed of the ship R spe or heading R cou, ensure that the ship always remains within the safe range during the mattress laying process.

[0139] In this embodiment, to respond to emergencies in a timely manner, the following operations are performed:

[0140] Obtain the real-time data of the mattress laying ship operation, the risk prediction result of the mattress laying ship operation, and the intelligent real-time adjustment control scheme, and organize and analyze the real-time data of the mattress laying ship operation, the risk prediction result of the mattress laying ship operation, and the intelligent real-time adjustment control scheme to form an intelligent control report for the mattress laying ship operation, and display the intelligent control report for the mattress laying ship operation in a visual form on the man-machine interaction interface, enabling users to intuitively view the operation status of the mattress laying ship remotely, and perform real-time optimization and monitoring management on the ship's traveling route and speed parameters to respond to emergencies in a timely manner.

[0141] To better demonstrate the intelligent control principle of the mattress laying ship operation based on the ship Internet of Things, this embodiment now provides an intelligent control system for the mattress laying ship operation based on the ship Internet of Things, which is used to implement the intelligent control method for the mattress laying ship operation based on the ship Internet of Things as described above, including:

[0142] The ship Internet of Things platform is used to connect and integrate the ship with the shore-based mattress laying equipment and control system by using the Internet of Things technology, collect the real-time data of the mattress laying ship operation based on the ship Internet of Things, and transmit the collected real-time data of the mattress laying ship operation based on the ship Internet of Things to the cloud computing platform;

[0143] The cloud computing platform is used to process and analyze the real-time data of the mattress laying ship operation based on the ship Internet of Things, perform real-time monitoring and prediction by using artificial intelligence algorithms, timely discover potential safety hazards and problems, and determine the risk prediction result of the mattress laying ship operation;

[0144] The intelligent control platform is used to automatically or remotely control the traveling speed, heading of the ship, and the working status of the mattress laying equipment according to the risk prediction result of the mattress laying ship operation to respond to emergencies in a timely manner.

[0145] In summary, by applying Internet of Things technology to connect and integrate the ship with the shore-based mattress laying equipment and control system, functions such as real-time monitoring, remote control, and automatic control can be realized, effectively improving the safety and efficiency of the ship during the mattress laying process, reducing the risk of human operation errors, and enhancing the accuracy and safety of the mattress laying work. Secondly, by optimizing parameters such as the ship's travel route and speed, energy consumption and fuel consumption can be reduced, thereby lowering the operating costs. Additionally, real-time monitoring and management data can be provided to help enterprises better understand the operating conditions of the ship and equipment, early warning of possible faults and problems, and thus conduct targeted maintenance and management to avoid losses caused by faults and accidents and respond promptly to emergencies. Through the ship networking technology, real-time monitoring and remote control of the ship's mattress laying operation can be achieved. Relevant personnel can view information such as the real-time position, speed, and attitude of the ship through terminal devices such as computers and mobile phones. At the same time, they can also remotely control parameters such as the ship's navigation direction and speed to ensure that the ship completes the mattress laying task safely and efficiently. By collecting and organizing various data of the ship during the mattress laying process, such as the running track and speed, through the ship networking technology, it can help enterprises better understand the operating conditions of the ship and equipment, discover problems and take corresponding measures, and also contribute to formulating the ship maintenance plan and reducing the operating costs.

[0146] It should be noted that in this article, relational terms such as first and second are only used 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 term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, such that a process, method, article or device comprising a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article or device.

[0147] Although the embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An intelligent control method for the operation of a mattress-laying vessel based on the ship Internet of Things, characterized in that, It includes the following steps: S1: Use Internet of Things technology to collect data on the operating status of the ship, ship sta-data data on the ship's operating environment, ship env-data and data on the status of the laying equipment, Lay sta-data and determine the real-time data of the laying vessel operation based on the ship Internet of Things. Based on wireless communication technology, transmit the collected real-time data of the laying vessel operation based on the ship Internet of Things to the cloud computing platform; S2: Process and analyze the real-time data of the laying barge operation based on the ship Internet of Things through a cloud computing platform. Based on deep learning technology, predict the risks of the real-time data of the laying barge operation, timely discover potential safety hazards and problems, and determine the risk prediction results of the laying barge operation; S3: According to the predicted results of the operation risks of the laying vessel, make intelligent real-time adjustment and control on the running position R pos , speed R spe , course R cou , attitude R att and draft R dra to keep the vessel always within the safe range during the laying process; In S1, the real-time data of the laying barge operation based on the ship Internet of Things collected is transmitted to the cloud computing platform, and the following operations are performed: Set the bandwidth of the communication channel corresponding to the cloud computing platform at the initial operation to be lower than the rated bandwidth of the communication channel; Real-time monitor the data volume corresponding to each transmission of the real-time data of the laying barge operation; Extract the data volume growth rate between the data volumes corresponding to every two adjacent real-time data transmissions; Compare the data volume growth rate between the data volumes corresponding to every two adjacent real-time data transmissions with a preset data volume growth rate threshold; When the data volume growth rate exceeds the preset data volume growth rate threshold, then retrieve the data volume corresponding to each transmission of the real-time data of the laying barge operation; Use the data volume corresponding to each transmission of the real-time data of the laying barge operation to obtain the predicted data volume value corresponding to the data transmission; Set the reserved bandwidth for the cloud computing platform according to the predicted data volume value, and perform the following operations: Extract the predicted data volume value; Use the predicted data volume value to obtain the expected load of the communication channel corresponding to the cloud computing platform; Compare the expected load with a preset load reference value; When the expected load exceeds the preset load reference value, then set the reserved bandwidth using the expected load; Release the bandwidth of the communication channel corresponding to the cloud computing platform according to the reserved bandwidth, and transmit the real-time data of the laying barge operation based on the ship Internet of Things collected according to the released bandwidth to the cloud computing platform; When the ratio between the released bandwidth value and the rated bandwidth of the communication channel reaches or exceeds the rated bandwidth occupancy ratio of the communication channel, then give a communication overload warning.

2. The intelligent control method for the operation of a mattress-laying vessel based on the ship Internet of Things according to claim 1, wherein, In S1, collect the real-time data of the laying barge operation based on the ship Internet of Things, and perform the following operations: Based on Internet of Things technology, sensors are used to monitor and collect the running position R pos , speed R spe , heading R cou , attitude R att and draft depth R dra of the laying barge during operation in real time, and obtain the ship operation status data ship sta-data ; Based on Internet of Things technology, sensors are used to monitor in real time and collect the wind speed W sp , wave height W hei , water flow velocity W vel , water temperature W tem and meteorological conditions M phe during the operation of the laying vessel, so as to obtain the data of the ship operation environment ship env-data ; Based on Internet of Things technology, sensors are used to monitor and collect the operating status R sta , fault information F inf and load conditions L sit of the bedding equipment on the shore in real time, and obtain the status data of the bedding equipment Lay sta-data ; Among them, based on the ship operation status data ship sta-data , the ship operation environment data ship env-data and the laying equipment status data Lay sta-data , the real-time data of the laying ship operation based on the ship Internet of Things is determined.

3. The intelligent control method for the laying vessel operation based on the ship Internet of Things according to claim 1, wherein, The predicted data volume value is obtained through the following formula: ； Among them, S represents the predicted value of the data volume; C max represents the maximum data volume corresponding to the real-time data transmission; C f represents the variance of the data transmission volume corresponding to the real-time data transmission of the laying barge operation; C p represents the average data volume corresponding to the real-time data transmission; G p represents the average time interval corresponding to the real-time data transmission of the laying barge operation; G max represents the maximum time interval corresponding to the real-time data transmission of the laying barge operation; g represents the variance of the time interval corresponding to the real-time data transmission of the laying barge operation; C t represents the data transmission data volume of other data during the real-time data transmission of the laying barge operation; r represents the variance corresponding to the comprehensive data transmission time interval of other data except the real-time data of the laying barge operation; f p represents the comprehensive data transmission frequency of other data except the real-time data of the laying barge operation; f c represents the data transmission frequency of the real-time data of the laying barge operation.

4. The intelligent control method for the laying barge operation based on the ship Internet of Things according to claim 3, wherein Where, The expected load is obtained through the following formula: ； Among them, L represents the predicted load; L0 represents the actual load of the current communication channel; C b represents the standard deviation of the data volume corresponding to real-time data transmission; C tb represents the standard deviation of the data transmission volume of other data during the real-time data transmission of the laying vessel operation; C tp represents the average value of the data transmission volume of other data during the real-time data transmission of the laying vessel operation; S represents the predicted value of the data volume; C e represents the rated bandwidth of the communication channel; Where the reserved bandwidth is obtained through the following formula: ； Among them, B represents the reserved bandwidth; B c represents the preset basic value of the reserved bandwidth; P represents the actual bandwidth utilization rate of the current communication channel; L represents the expected payload; L y represents the preset reference value of the payload; λ represents the sensitivity coefficient of the preset bandwidth adjustment.

5. The intelligent control method for the laying vessel operation based on the ship Internet of Things according to claim 2, wherein In S2, process the real-time data of the laying barge operation based on the ship Internet of Things, and perform the following operations: Based on the Power Query tool, clean the real-time data of the laying barge operation based on the ship Internet of Things; Retrieve the real-time data of the laying barge operation based on the ship Internet of Things one by one, check whether there are duplicate values, missing values and abnormal values in the real-time data of the laying barge operation based on the ship Internet of Things, and process the duplicate values, missing values and abnormal values existing in the real-time data of the laying barge operation based on the ship Internet of Things; Among them, remove the duplicate values in the real-time data of the laying barge operation based on the ship Internet of Things, and delete the duplicate data records; Among them, use the interpolation method to fill in the missing values in the real-time data of the laying barge operation based on the ship Internet of Things to make the missing values in the real-time data of the laying barge operation based on the ship Internet of Things complete; Among them, the interpolation method is used to correct the outliers in the real-time data of the laying barge operation based on the ship Internet of Things, making the outliers in the real-time data of the laying barge operation based on the ship Internet of Things normal.

6. The intelligent control method for the operation of the mattress-laying vessel based on the ship Internet of Things according to claim 5, wherein In the above S2, when processing the real-time data of the laying barge operation based on the ship Internet of Things, the following operations are also performed: Based on Z-score standardization, normalize the real-time data of the laying barge operation based on the ship Internet of Things; Unify the real-time data of the laying barge operation based on the ship Internet of Things to the same dimension and range, remove the dimension differences between the real-time data of the laying barge operation based on the ship Internet of Things, and determine the standardized real-time data of the laying barge operation; Integrate the standardized real-time data of the laying barge operation, integrate the standardized real-time data of the laying barge operation into a unified data view, and check the integrated standardized real-time data of the laying barge operation to determine whether there are any omissions in the integrated standardized real-time data of the laying barge operation; After the data verification is qualified, securely store the integrated standardized real-time data of the laying barge operation, and store the real-time data of the laying barge operation in the database.

7. The intelligent control method for the laying vessel operation based on the ship Internet of Things according to claim 6, wherein In the above S2, when analyzing the real-time data of the laying barge operation based on the ship Internet of Things, the following operations are performed: According to the intelligent control requirements of the laying barge operation based on the ship Internet of Things, collect the historical data of the laying barge operation, and divide the collected historical data of the laying barge operation to determine the training set and the test set; Based on deep learning technology, use the training set to train the deep learning model, enabling the deep learning model to autonomously learn the process of laying barge operation risk prediction, and determine the laying barge operation risk prediction model based on deep learning; Based on the test set, conduct performance testing on the laying barge operation risk prediction model based on deep learning to determine whether the laying barge operation risk prediction model based on deep learning can achieve the expected effect, and adjust the parameters and structure of the laying barge operation risk prediction model based on deep learning according to the test results. After continuous iterative optimization, determine the optimal laying barge operation risk prediction model; Deploy the optimal laying barge operation risk prediction model to the actual laying barge operation risk prediction environment, and input the real-time data of the laying barge operation into the optimal laying barge operation risk prediction model; Analyze the real-time data of the laying barge operation based on the optimal laying barge operation risk prediction model to determine whether there are risk behaviors in the laying barge operation, so as to timely discover potential safety hazards and problems, and determine the laying barge operation risk prediction result.

8. The intelligent control method for the laying vessel operation based on the ship Internet of Things according to claim 7, characterized in that, In the above S3, to keep the ship within a safe range during the laying process, the following operations are performed: Based on the predicted results of the operation risks of the mattress-laying vessel, analyze the real-time data of the mattress-laying vessel operation, find out the causes of the operation risks of the mattress-laying vessel, and based on the causes of the operation risks of the mattress-laying vessel, conduct intelligent real-time adjustment control on the running position R pos , speed R spe , course R cou , attitude R att and draft R dra for intelligent real-time adjustment control; When potential safety hazards and problems are detected, issue a risk warning alarm for the operation of the mattress-laying vessel in a timely manner and automatically change the speed of the vessel R spe or the course R cou , ensuring that the vessel always remains within a safe range during the mattress-laying process.

9. The intelligent control method for the operation of the mattress-laying vessel based on the ship Internet of Things according to claim 8, characterized in that, In the above S3, to respond to emergencies in a timely manner, the following operations are performed: Obtain the real-time data of the laying barge operation, the risk prediction results of the laying barge operation, and the intelligent real-time adjustment control scheme, and organize and analyze the real-time data of the laying barge operation, the risk prediction results of the laying barge operation, and the intelligent real-time adjustment control scheme to form an intelligent control report for the laying barge operation, and display the intelligent control report of the laying barge operation in a visual form on the man-machine interaction interface, enabling users to intuitively view the operation status of the laying barge remotely, and perform real-time optimization and monitoring management on the driving route and speed parameters of the ship to promptly respond to emergencies.

10. The intelligent control system for the mattress-laying vessel operation based on the ship Internet of Things is used to implement the intelligent control method for the mattress-laying vessel operation based on the ship Internet of Things as described in claim 9, and is characterized in that Including: A ship networking platform, which is used to connect and integrate the ship with the shore laying equipment and control system by using Internet of Things technology, collect the real-time data of the laying barge operation based on the ship networking, and transmit the collected real-time data of the laying barge operation based on the ship networking to the cloud computing platform; A cloud computing platform, which is used to process and analyze the real-time data of the laying barge operation based on the ship networking, perform real-time monitoring and prediction by using artificial intelligence algorithms, timely discover potential safety hazards and problems, and determine the risk prediction results of the laying barge operation; An intelligent control platform, which is used to automatically or remotely control the driving speed, heading of the ship, and the working status of the laying equipment according to the risk prediction results of the laying barge operation to promptly respond to emergencies.

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