Self-adaptive synchronous coordination control method for laying vessel reel and turnover plate device

Abstract

The application discloses a laying ship reel and plate turning device adaptive synchronous coordination control method, and belongs to the technical field of laying ships, which comprises the following steps: collecting real-time data of the operation of the laying ship reel and the plate turning device; preprocessing the real-time data of the operation of the laying ship reel and the plate turning device; establishing a laying ship reel and plate turning device adaptive synchronous coordination control mathematical model, analyzing the operation characteristic data of the laying ship reel and the plate turning device, and performing adaptive synchronous coordination control and evaluation optimization on the laying ship reel and the plate turning device. The application solves the problem that the existing laying ship reel and plate turning device cannot be effectively adaptively and synchronously controlled, resulting in low work efficiency and safety of the laying ship. The application can effectively adaptively and synchronously control the laying ship reel and the plate turning device, and can improve the work efficiency and safety of the laying ship.

Description

Technical Field

[0001] This invention relates to the field of rafting vessel technology, specifically to an adaptive synchronous coordination control method for the rafting vessel's drum and flapping device. Background Technology

[0002] The reel and flapper device of the cable laying vessel are key components of the vessel. The reel and flapper device each play a crucial role in the submarine cable laying process. The reel is responsible for winding up and connecting the cable, providing continuity and stability for the laying work. The flapper device is responsible for positioning and adjustment, ensuring that the cable is laid smoothly along the predetermined path. The two complement each other and work together to ensure the smooth progress of the submarine cable laying work.

[0003] Chinese patent CN117755443A discloses a multi-functional laying vessel, including a hull, a frame connected to the hull deck, a lifting assembly connected to the frame, a laying assembly on one side of the hull for laying the soft material, an intelligent mechanical filling assembly on one side of the frame, a power assembly inside the hull to provide power for the movement of the lifting assembly, the laying assembly, and the intelligent mechanical filling assembly, and a rudder propeller assembly located at the stern of the hull. However, this patent has the following drawbacks:

[0004] Existing technologies cannot effectively adaptively synchronize and coordinate the control of the laying vessel's drum and tipping device, resulting in low working efficiency and safety of the laying vessel. Summary of the Invention

[0005] The purpose of this invention is to provide an adaptive synchronous coordination control method for the laying vessel drum and the tipping device, which can effectively perform adaptive synchronous coordination control on the laying vessel drum and the tipping device, thereby improving the working efficiency and safety of the laying vessel and solving the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] An adaptive synchronous coordination control method for the laying vessel drum and tipper device includes:

[0008] Collect operating data of the laying vessel's drum device and the laying vessel's tipping device to obtain real-time operating data of the laying vessel's drum and tipping device based on the Internet of Things;

[0009] Preprocess the real-time operating data of the laying vessel's drum and tipping device to determine the operating characteristic data of the laying vessel's drum and tipping device;

[0010] An adaptive synchronous coordinated control mathematical model for the laying vessel's drum and tipper device was established. The operating characteristic data of the laying vessel's drum and tipper device were analyzed to determine the adaptive synchronous coordinated control results. The adaptive synchronous coordinated control and evaluation optimization of the laying vessel's drum and tipper device were then carried out, including: comparing the deviation values ​​corresponding to the operating parameter data with the rated allowable error range corresponding to the operating parameter data, and using the fluctuation information corresponding to the deviation values ​​to determine abnormal risk warnings.

[0011] Preferably, real-time data on the operation of the laying vessel drum and tipper device are acquired, and the following operations are performed:

[0012] Sensors are installed on the laying vessel drum and tipping device based on Internet of Things (IoT) technology.

[0013] Based on sensors, the diameter, width, speed, torque, moment of inertia, and applied tension of the laying vessel drum device are monitored and collected in real time to obtain the operating data of the laying vessel drum device.

[0014] Based on sensors, the flipping angle, movement range, movement speed and positioning of the planking vessel tipping device are monitored and collected in real time to obtain the planking vessel tipping device operation data;

[0015] Specifically, based on the operating data of the laying vessel's drum device and the laying vessel's tipping device, real-time operating data of the laying vessel's drum and tipping device based on the Internet of Things are determined.

[0016] Preferably, the real-time operating data of the IoT-based laying vessel drum and tipping device are preprocessed, and the following operations are performed:

[0017] Acquire real-time operational data of the laying vessel's drum and tipping device based on the Internet of Things;

[0018] Using Pandas tools, the real-time operation data of the laying vessel drum and tipper device based on IoT is cleaned to remove duplicate, missing, and outlier values ​​that are useless for the adaptive synchronization and coordination control of the laying vessel drum and tipper device.

[0019] Based on the sqlitebiter tool, the real-time operating data of the laying vessel drum and tipping device based on IoT is transformed to remove the dimensional differences between the real-time operating data of the laying vessel drum and tipping device based on IoT, and to determine the standardized real-time operating data of the laying vessel drum and tipping device.

[0020] Preferably, in addition to preprocessing the real-time operating data of the IoT-based laying vessel drum and tipping device, the following operations are also performed:

[0021] Acquire real-time operational data of the laying vessel's drum and tipping device based on the Internet of Things;

[0022] Based on the bubble sorting method, the real-time operation data of the laying vessel drum and tipping device based on the Internet of Things are sorted to determine the real-time operation data of the laying vessel drum and tipping device with order.

[0023] Based on principal component analysis, feature extraction is performed on the real-time operating data of the laying vessel drum and tipping device based on the Internet of Things (IoT). The extracted feature vectors are useful for the adaptive synchronous coordination control of the laying vessel drum and tipping device, and the operating feature data of the laying vessel drum and tipping device based on the IoT are determined.

[0024] Preferably, an adaptive synchronous coordination control mathematical model for the laying vessel drum and tipper device is established, and the following operations are performed:

[0025] Collect historical operational data of the laying vessel's drum and tipping device;

[0026] Based on deep learning technology, the deep learning model is trained according to the collected historical operation data of the laying vessel drum and tipping device, and an artificial intelligence-based adaptive synchronous coordination control mathematical model for the laying vessel drum and tipping device is constructed.

[0027] The constructed AI-based adaptive synchronous coordination control mathematical model for the laying vessel drum and flap device was tested. Based on accuracy, recall rate and F1-score, it was determined whether the constructed AI-based adaptive synchronous coordination control mathematical model for the laying vessel drum and flap device could achieve the expected effect, and the test results of the mathematical model were determined.

[0028] When the constructed AI-based adaptive synchronous coordination control mathematical model for the laying vessel drum and flap device fails to achieve the expected results, the parameters and structure of the constructed AI-based adaptive synchronous coordination control mathematical model for the laying vessel drum and flap device are adjusted, and the AI-based adaptive synchronous coordination control mathematical model for the laying vessel drum and flap device is continuously iterated and optimized to determine the optimal adaptive synchronous coordination control mathematical model for the laying vessel drum and flap device.

[0029] Preferably, the operational characteristic data of the laying vessel drum and tipping device based on the Internet of Things are analyzed, and the following operations are performed:

[0030] Obtain the optimal adaptive synchronous coordination control mathematical model for the laying vessel drum and flap device, and deploy the optimal adaptive synchronous coordination control mathematical model for the laying vessel drum and flap device in the actual adaptive synchronous coordination control environment of the laying vessel drum and flap device.

[0031] The operational characteristic data of the laying vessel drum and tipper device based on the Internet of Things are input into the optimal adaptive synchronous coordination control mathematical model of the laying vessel drum and tipper device. The operational characteristic data of the laying vessel drum and tipper device based on the optimal adaptive synchronous coordination control mathematical model of the laying vessel drum and tipper device are analyzed.

[0032] Determine how the laying vessel's drum and tipper device can adaptively and synchronously coordinate control, adjust and optimize the operating characteristic data of the laying vessel's drum and tipper device, determine the adaptive synchronous and coordinated control results of the laying vessel's drum and tipper device, and perform adaptive synchronous and coordinated control on the laying vessel's drum and tipper device.

[0033] Preferably, adaptive synchronous coordination control is performed on the laying vessel drum and tipper device, and the following operations are performed:

[0034] When the cable is being wound up or unwound by the cable reel device on the laying vessel, the cable-turning device on the laying vessel adjusts the turning angle in a timely manner according to the position and length of the cable, so that the cable can pass smoothly through the cable reel device on the laying vessel.

[0035] When the cable-laying vessel's tipping device is adjusting its position, the cable-laying vessel's drum device adjusts its winding and unwinding speed in a timely manner according to the adjusted position of the cable-laying vessel's tipping device, so that the cable-laying vessel's tipping device can position the cable and lay the cable smoothly along the predetermined path.

[0036] Preferably, the adaptive synchronous coordinated control evaluation and optimization of the laying vessel drum and tipper device are performed, and the following operations are performed:

[0037] After adaptive synchronous coordination control of the laying vessel's drum and tipping device, the system monitors and evaluates the operation of the laying vessel's drum and tipping device in real time, obtains timely operational feedback from the laying vessel's drum and tipping device, issues timely warnings and alarms when the laying vessel's drum and tipping device malfunctions, and performs maintenance and management of the laying vessel's drum and tipping device to optimize the laying vessel's drum and tipping device.

[0038] Preferably, timely operational feedback from the laying vessel's drum and tipping device is obtained. When the laying vessel's drum and tipping device malfunction, an early warning alarm is issued promptly, and the following operations are performed:

[0039] Real-time monitoring of operating parameter data during the operation of the laying vessel's drum and tipping device;

[0040] The operating parameter data of the laying vessel drum and the tipping device during operation are compared with the target values ​​corresponding to the operating parameters to obtain the deviation value between the operating parameter data and the target values ​​corresponding to the operating parameters.

[0041] Compare the deviation value corresponding to the operating parameter data with the rated permissible error range corresponding to the operating parameter data;

[0042] When the deviation value corresponding to the operating parameter data exceeds the preset rated allowable error range, an abnormal warning alarm will be triggered.

[0043] When the deviation value corresponding to the operating parameter data does not exceed the preset rated allowable error range, the fluctuation information corresponding to the deviation value of the operating parameter data is retrieved, and the fluctuation information corresponding to the deviation value is used to determine the abnormal risk warning.

[0044] Preferably, when the deviation value corresponding to the operating parameter data does not exceed the preset rated allowable error range, the fluctuation information corresponding to the deviation value of the operating parameter data is retrieved, and the fluctuation information corresponding to the deviation value is used to determine the abnormal risk warning, including:

[0045] When the deviation value corresponding to the operating parameter data does not exceed the preset rated allowable error range, the deviation value corresponding to each type of operating parameter contained in the operating parameter data is retrieved.

[0046] The fluctuation coefficient corresponding to each type of operating parameter is obtained by using the deviation value corresponding to each type of operating parameter.

[0047] The volatility coefficient is obtained using the following formula:

[0048]

[0049] Where S represents the fluctuation coefficient; n represents the number of deviation values ​​corresponding to the operating parameter type; Pi represents the value corresponding to the i-th deviation value; P represents the value of the rated allowable error range corresponding to the operating parameter type; P max This indicates the maximum deviation corresponding to the type of running parameter; P min This indicates the minimum deviation value corresponding to the operating parameter type; m represents the number of deviation values ​​that occur within the time interval between the maximum and minimum deviation values, and the deviation values ​​occurring within the time interval between the maximum and minimum deviation values ​​are used as reference deviation values; P mi This represents the numerical value of the i-th reference deviation.

[0050] Compare the volatility coefficient corresponding to each type of operating parameter with the preset volatility coefficient threshold;

[0051] When the volatility coefficient corresponding to any operating parameter type exceeds its corresponding volatility coefficient threshold, the comprehensive volatility coefficient is obtained by using the volatility coefficients corresponding to all operating parameter types.

[0052] The comprehensive volatility coefficient is obtained by the following formula:

[0053]

[0054] Where Z represents the comprehensive fluctuation coefficient; k represents the number of operating parameter types; S m This represents the volatility coefficient exceeding its corresponding volatility coefficient threshold, which is used as the target volatility coefficient; S ym S represents the volatility threshold corresponding to the target volatility coefficient; i S represents the volatility coefficient corresponding to the i-th operating parameter type, excluding the target volatility coefficient; yi This represents the volatility threshold corresponding to the i-th type of operating parameter, excluding the target volatility coefficient.

[0055] The overall volatility coefficient is compared with a preset overall volatility coefficient threshold.

[0056] When the overall volatility coefficient exceeds the preset overall volatility coefficient threshold, it is determined that there is a risk of operational abnormality and a risk warning is issued.

[0057] Compared with the prior art, the beneficial effects of the present invention are:

[0058] This invention utilizes Internet of Things (IoT) technology to install sensors on the laying vessel's drum and tipping device. Based on these sensors, it acquires operational data from both the drum and tipping devices, determining real-time operational data for both devices. This data is then pre-processed to identify operational characteristics. An adaptive synchronous coordination control mathematical model is established for the drum and tipping device. This model is used to analyze the operational characteristics, determine the adaptive synchronous coordination control results, and perform adaptive synchronous coordination control and evaluation optimization. This allows for effective adaptive synchronous coordination control of the laying vessel's drum and tipping device, improving the working efficiency and safety of the laying vessel. Attached Figure Description

[0059] Figure 1 This is a flowchart illustrating the adaptive synchronous coordination control of the laying vessel drum and flap device of the present invention. Detailed Implementation

[0060] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0061] To address the problem of low efficiency and safety of the laying vessel due to the inability of existing systems to effectively adaptively and synchronously coordinate the drum and flapping devices, please refer to [link to relevant documentation]. Figure 1 This embodiment provides the following technical solution:

[0062] An adaptive synchronous coordination control method for the laying vessel drum and tipper device includes the following steps:

[0063] S1. Data Acquisition: Based on sensors, collect the operating data of the laying vessel's drum device and the laying vessel's tipping device to obtain real-time operating data of the laying vessel's drum and tipping device based on the Internet of Things.

[0064] In this embodiment, real-time operational data of the IoT-based laying vessel drum and tipping device are acquired, and the following operations are performed:

[0065] Sensors are installed on the laying vessel drum and tipping device based on Internet of Things (IoT) technology.

[0066] Based on sensors, the diameter, width, speed, torque, moment of inertia, and applied tension of the laying vessel drum device are monitored and collected in real time to obtain the operating data of the laying vessel drum device.

[0067] It should be noted that the diameter of the laying vessel's drum affects the material winding and unwinding speed and tension; the width of the laying vessel's drum determines the width of material that can be accommodated; the rotational speed of the laying vessel's drum determines the material winding and unwinding speed; the torque of the laying vessel's drum affects the drum's load capacity; the rotational inertia of the laying vessel's drum affects the start-up and stop response time; and the applied tension of the laying vessel's drum affects the drum's winding and unwinding capacity.

[0068] Based on sensors, the flipping angle, movement range, movement speed and positioning of the planking vessel tipping device are monitored and collected in real time to obtain the planking vessel tipping device operation data;

[0069] Specifically, based on the operating data of the laying vessel's drum device and the laying vessel's tipping device, real-time operating data of the laying vessel's drum and tipping device based on the Internet of Things are determined.

[0070] S2. Data preprocessing: Preprocess the real-time operating data of the laying vessel drum and tipping device based on the Internet of Things to determine the operating characteristic data of the laying vessel drum and tipping device based on the Internet of Things.

[0071] In this embodiment, the real-time operating data of the IoT-based laying vessel drum and tipping device are preprocessed by performing the following operations:

[0072] Acquire real-time operational data of the laying vessel's drum and tipping device based on the Internet of Things;

[0073] Using Pandas tools, real-time data of the operation of the laying vessel drum and tipper device based on IoT is cleaned to remove duplicate, missing, and outlier values ​​that are useless for the adaptive synchronization and coordination control of the laying vessel drum and tipper device. This can improve the accuracy and efficiency of subsequent processing of the real-time data of the laying vessel drum and tipper device.

[0074] Based on the sqlitebiter tool, the real-time operating data of the laying vessel drum and tipping device based on IoT is transformed to remove the dimensional differences between the real-time operating data of the laying vessel drum and tipping device based on IoT, and to determine the standardized real-time operating data of the laying vessel drum and tipping device.

[0075] Based on the bubble sorting method, the real-time operation data of the laying vessel drum and tipping device based on the Internet of Things are sorted to determine the real-time operation data of the laying vessel drum and tipping device with order.

[0076] Based on principal component analysis, feature extraction is performed on the real-time operating data of the laying vessel drum and tipping device based on the Internet of Things (IoT). The extracted feature vectors are useful for the adaptive synchronous coordination control of the laying vessel drum and tipping device, and the operating feature data of the laying vessel drum and tipping device based on the IoT are determined.

[0077] S3. Adaptive Synchronous Coordination Control: Establish a mathematical model for adaptive synchronous coordination control of the laying vessel drum and tipping device, analyze the operating characteristic data of the laying vessel drum and tipping device based on the Internet of Things, determine the adaptive synchronous coordination control results of the laying vessel drum and tipping device, and perform adaptive synchronous coordination control and evaluation optimization on the laying vessel drum and tipping device.

[0078] In this embodiment, an adaptive synchronous coordination control mathematical model for the laying vessel drum and the tipping plate device is established, and the following operations are performed:

[0079] Collect historical operational data of the laying vessel's drum and tipping device;

[0080] Based on deep learning technology, the deep learning model is trained according to the collected historical operation data of the laying vessel drum and tipping device, and an artificial intelligence-based adaptive synchronous coordination control mathematical model for the laying vessel drum and tipping device is constructed.

[0081] The constructed AI-based adaptive synchronous coordination control mathematical model for the laying vessel drum and flap device was tested. Based on accuracy, recall rate and F1-score, it was determined whether the constructed AI-based adaptive synchronous coordination control mathematical model for the laying vessel drum and flap device could achieve the expected effect, and the test results of the mathematical model were determined.

[0082] When the constructed AI-based adaptive synchronous coordination control mathematical model for the laying vessel drum and flap device fails to achieve the expected results, the parameters and structure of the constructed AI-based adaptive synchronous coordination control mathematical model for the laying vessel drum and flap device are adjusted, and the AI-based adaptive synchronous coordination control mathematical model for the laying vessel drum and flap device is continuously iterated and optimized to determine the optimal adaptive synchronous coordination control mathematical model for the laying vessel drum and flap device.

[0083] In this embodiment, the operational characteristic data of the IoT-based laying vessel drum and tipper device are analyzed, and the following operations are performed:

[0084] Obtain the optimal adaptive synchronous coordination control mathematical model for the laying vessel drum and flap device, and deploy the optimal adaptive synchronous coordination control mathematical model for the laying vessel drum and flap device in the actual adaptive synchronous coordination control environment of the laying vessel drum and flap device.

[0085] The operational characteristic data of the laying vessel drum and tipper device based on the Internet of Things are input into the optimal adaptive synchronous coordination control mathematical model of the laying vessel drum and tipper device. The operational characteristic data of the laying vessel drum and tipper device based on the optimal adaptive synchronous coordination control mathematical model of the laying vessel drum and tipper device are analyzed.

[0086] Determine how the laying vessel's drum and tipper device can adaptively and synchronously coordinate control, adjust and optimize the operating characteristic data of the laying vessel's drum and tipper device, determine the adaptive synchronous and coordinated control results of the laying vessel's drum and tipper device, and perform adaptive synchronous and coordinated control on the laying vessel's drum and tipper device.

[0087] Among them, when the cable is being wound up and unwound by the cable reel device on the laying vessel, the cable-turning plate device on the laying vessel adjusts the turning angle in a timely manner according to the position and length of the cable, so that the cable can pass smoothly through the cable reel device on the laying vessel.

[0088] When the cable-laying vessel's tipping device is adjusting its position, the cable-laying vessel's drum device adjusts its winding and unwinding speed in a timely manner according to the adjusted position of the tipping device, so that the tipping device can position the cable and lay it smoothly along the predetermined path.

[0089] Specifically, timely operational feedback from the laying vessel's drum and tipping device should be obtained. When the laying vessel's drum and tipping device malfunction, an early warning alarm should be issued promptly, and the following actions should be taken:

[0090] Real-time monitoring of operating parameter data during the operation of the laying vessel's drum and tipping device;

[0091] The operating parameter data of the laying vessel drum and the tipping device during operation are compared with the target values ​​corresponding to the operating parameters to obtain the deviation value between the operating parameter data and the target values ​​corresponding to the operating parameters.

[0092] Compare the deviation value corresponding to the operating parameter data with the rated permissible error range corresponding to the operating parameter data;

[0093] When the deviation value corresponding to the operating parameter data exceeds the preset rated allowable error range, an abnormal warning alarm will be triggered.

[0094] When the deviation value corresponding to the operating parameter data does not exceed the preset rated allowable error range, the fluctuation information corresponding to the deviation value of the operating parameter data is retrieved, and the fluctuation information corresponding to the deviation value is used to determine the abnormal risk warning.

[0095] The technical effects of the above solution are as follows: By monitoring the operating parameters of the laying vessel drum and the tipping device in real time, the operating status of the equipment can be understood in a timely manner, providing a basis for subsequent data analysis and anomaly early warning. Real-time monitoring helps to capture instantaneous changes during equipment operation, which is of great significance for timely detection of potential problems. Comparing the real-time monitored operating parameter data with preset target values ​​and calculating the deviation value helps to quantify the difference between the equipment's operating status and the ideal state. By comparing the deviation value with the rated allowable error range, it is possible to objectively assess whether the equipment's operating status is within the normal range.

[0096] When the deviation exceeds the preset allowable error range, the system automatically issues an anomaly warning alarm. This helps to promptly identify problems and take measures to prevent further deterioration. The anomaly warning alarm function improves the safety and reliability of equipment operation and reduces losses caused by equipment failure. When the deviation does not exceed the rated allowable error range, the system further retrieves the fluctuation information corresponding to the deviation value and uses this information to determine anomaly risk warnings. This detailed risk assessment method helps to identify situations that, although currently within the allowable range, may have potential risks or abnormal trends, thus allowing for early preventative measures. The entire technical solution, through automated monitoring, data analysis, and early warning alarms, greatly improves the efficiency of equipment management. It provides managers with accurate and timely data support, helping them make more scientific decisions and optimize equipment operation and maintenance strategies.

[0097] In summary, the above technical solutions, through functions such as real-time monitoring, data analysis, anomaly early warning alarms, and risk warning judgment, effectively improve the operational safety and reliability of the laying vessel drum and tipping device, reduce the risk of equipment failure, and improve management efficiency and scientific decision-making.

[0098] Specifically, when the deviation value corresponding to the operating parameter data does not exceed the preset rated allowable error range, the fluctuation information corresponding to the deviation value of the operating parameter data is retrieved, and the fluctuation information corresponding to the deviation value is used to determine the abnormal risk warning, including:

[0099] When the deviation value corresponding to the operating parameter data does not exceed the preset rated allowable error range, the deviation value corresponding to each type of operating parameter contained in the operating parameter data is retrieved.

[0100] The fluctuation coefficient corresponding to each type of operating parameter is obtained by using the deviation value corresponding to each type of operating parameter.

[0101] The volatility coefficient is obtained using the following formula:

[0102]

[0103] Where S represents the fluctuation coefficient; n represents the number of deviation values ​​corresponding to the operating parameter type; Pi represents the value corresponding to the i-th deviation value; P represents the value of the rated allowable error range corresponding to the operating parameter type; P max This indicates the maximum deviation corresponding to the type of running parameter; P min This indicates the minimum deviation value corresponding to the operating parameter type; m represents the number of deviation values ​​that occur within the time interval between the maximum and minimum deviation values, and the deviation values ​​occurring within the time interval between the maximum and minimum deviation values ​​are used as reference deviation values; P mi This represents the numerical value of the i-th reference deviation.

[0104] Compare the volatility coefficient corresponding to each type of operating parameter with the preset volatility coefficient threshold;

[0105] When the volatility coefficient corresponding to any operating parameter type exceeds its corresponding volatility coefficient threshold, the comprehensive volatility coefficient is obtained by using the volatility coefficients corresponding to all operating parameter types.

[0106] The comprehensive volatility coefficient is obtained by the following formula:

[0107]

[0108] Where Z represents the comprehensive fluctuation coefficient; k represents the number of operating parameter types; S m This represents the volatility coefficient exceeding its corresponding volatility coefficient threshold, which is used as the target volatility coefficient; S ym S represents the volatility threshold corresponding to the target volatility coefficient; i S represents the volatility coefficient corresponding to the i-th operating parameter type, excluding the target volatility coefficient; yiThis represents the volatility threshold corresponding to the i-th type of operating parameter, excluding the target volatility coefficient.

[0109] The overall volatility coefficient is compared with a preset overall volatility coefficient threshold.

[0110] When the overall volatility coefficient exceeds the preset overall volatility coefficient threshold, it is determined that there is a risk of operational abnormality and a risk warning is issued.

[0111] The technical effects of the above technical solution are as follows: This indicates the range of deviation values; the larger the range, the greater the fluctuation of the deviation value. This part is related to the denominator. Divide, denominator It reflects the degree of fluctuation within a specific time interval. The combination of the two comprehensively considers the influence of the range of deviation values ​​and the fluctuation situation within a specific time interval on the fluctuation coefficient. Taking the square root of the sum of squared deviations and dividing by n−1 yields the sample standard deviation of the proportion of all deviation values, quantifying the degree of fluctuation of all deviation values. Adding 1 to the sample standard deviation increases this value; the larger the sample standard deviation (i.e., the more drastic the fluctuation of all deviation values), the larger this value becomes. The above formula comprehensively considers the range of deviation values, the degree of fluctuation of all deviation values, and the degree of fluctuation of the reference deviation value within a specific time interval, ultimately yielding a fluctuation coefficient S, used to measure the fluctuation of the deviation values ​​corresponding to the operating parameter type. A larger fluctuation coefficient S indicates a larger range of deviation values ​​corresponding to the operating parameter type, and also a greater degree of dispersion of the deviation values ​​relative to the rated allowable error range (including both overall and within a specific time interval), meaning a more drastic degree of deviation fluctuation. Conversely, a smaller fluctuation coefficient S indicates a smaller range of deviation values, less dispersion, and relatively smoother deviation fluctuation.

[0112] This solution achieves refined analysis of equipment operating status by retrieving the deviation value corresponding to each type of operating parameter and calculating the fluctuation coefficient for each type. This analysis method can more accurately capture minute fluctuations during equipment operation, providing a foundation for subsequent abnormal risk warnings. The calculation formula for the fluctuation coefficient considers the number, magnitude, and distribution of deviation values, comprehensively reflecting the fluctuation of equipment operating parameters. The fluctuation coefficient allows for a quantitative assessment of the stability of equipment operating status, providing accurate numerical basis for risk warnings. The solution sets fluctuation coefficient thresholds and a comprehensive fluctuation coefficient threshold, forming a multi-level risk warning mechanism. When the fluctuation coefficient of any operating parameter type exceeds its corresponding threshold, a further comprehensive fluctuation coefficient calculation is triggered to assess the overall operational risk of the equipment. This multi-level risk warning mechanism improves the accuracy and reliability of warnings. The calculation formula for the comprehensive fluctuation coefficient considers the target fluctuation coefficient exceeding the threshold and the fluctuation coefficients of other operating parameter types, comprehensively assessing the overall operational risk of the equipment. This comprehensive assessment method avoids misjudgments caused by single parameter fluctuations, improving the accuracy of risk warnings. When the comprehensive fluctuation coefficient exceeds a preset threshold, the solution determines that there is an operational anomaly risk and issues a risk warning. This early warning mechanism helps to promptly identify potential problems during equipment operation, enabling measures to be taken for risk prevention and control, and avoiding losses caused by equipment failure. The entire technical solution improves the scientific nature of equipment management through quantitative assessment, multi-level early warning, and proactive prevention. It provides managers with accurate and timely risk warning information, helping them make more informed decisions and optimize equipment operation and maintenance strategies.

[0113] In summary, the above-mentioned technical solutions effectively improve the safety and reliability of equipment operation, reduce the risk of equipment failure, and provide strong support for equipment management and maintenance through refined fluctuation analysis, quantitative assessment of fluctuation coefficients, multi-level risk early warning, comprehensive assessment of comprehensive fluctuation coefficients, early warning and risk prevention, and improved scientific equipment management.

[0114] It should be noted that during the laying process, the laying vessel's drum and tilting device need to work closely together to ensure laying quality. Specifically, when the laying vessel's drum is winding up or unwinding the cable, the tilting device adjusts the tilting angle in a timely manner according to the cable's position and length, allowing the cable to pass smoothly through the drum. When the tilting device is adjusting its position, the laying vessel's drum adjusts its winding and unwinding speed accordingly, enabling the tilting device to position the cable and lay it smoothly along the predetermined path. Therefore, by implementing adaptive synchronous coordination control of the laying vessel's drum and tilting device, the working efficiency and safety of the laying vessel can be improved.

[0115] It should be noted that after the adaptive synchronous coordination control of the laying vessel's drum and tipping device is implemented, the operation of the laying vessel's drum and tipping device is monitored and evaluated in real time. The operation feedback of the laying vessel's drum and tipping device is obtained in a timely manner. When the laying vessel's drum and tipping device is in abnormal operation, an early warning alarm is issued in a timely manner. The laying vessel's drum and tipping device is maintained and managed, and optimized to deal with possible problems in a timely manner, so as to ensure the safe and efficient operation of the laying vessel's drum and tipping device.

[0116] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0117] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. An adaptive synchronous coordination control method for the laying vessel drum and tipper device, characterized in that, include: Collect operating data of the laying vessel's drum device and the laying vessel's tipping device to obtain real-time operating data of the laying vessel's drum and tipping device based on the Internet of Things; Preprocess the real-time operating data of the laying vessel's drum and tipping device to determine the operating characteristic data of the laying vessel's drum and tipping device; An adaptive synchronous coordinated control mathematical model for the laying vessel's drum and tipper device was established. The operating characteristic data of the laying vessel's drum and tipper device were analyzed to determine the adaptive synchronous coordinated control results. The adaptive synchronous coordinated control and evaluation optimization of the laying vessel's drum and tipper device were then carried out. Timely obtain operational feedback from the laying vessel's drum and tipping device. When the laying vessel's drum and tipping device malfunction, retrieve the fluctuation information corresponding to the deviation value of the operational parameter data, use the fluctuation information corresponding to the deviation value to determine the abnormal risk, and issue an early warning alarm in a timely manner.

2. The adaptive synchronous coordination control method for the laying vessel drum and tipper device as described in claim 1, characterized in that, Timely obtain operational feedback from the laying vessel's drum and tipper device; when the laying vessel's drum and tipper device malfunction, issue an early warning alarm and perform the following operations: Real-time monitoring of operating parameter data during the operation of the laying vessel's drum and tipping device; The operating parameter data of the laying vessel drum and the tipping device during operation are compared with the target values ​​corresponding to the operating parameters to obtain the deviation value between the operating parameter data and the target values ​​corresponding to the operating parameters. Compare the deviation value corresponding to the operating parameter data with the rated permissible error range corresponding to the operating parameter data; When the deviation value corresponding to the operating parameter data exceeds the preset rated allowable error range, an abnormal warning alarm will be triggered. When the deviation value corresponding to the operating parameter data does not exceed the preset rated allowable error range, the fluctuation information corresponding to the deviation value of the operating parameter data is retrieved, and the fluctuation information corresponding to the deviation value is used to determine the abnormal risk warning.

3. The adaptive synchronous coordination control method for the laying vessel drum and tipper device as described in claim 2, characterized in that, When the deviation value corresponding to the operating parameter data does not exceed the preset rated allowable error range, the fluctuation information corresponding to the deviation value of the operating parameter data is retrieved, and the fluctuation information corresponding to the deviation value is used to determine the abnormal risk warning, including: When the deviation value corresponding to the operating parameter data does not exceed the preset rated allowable error range, the deviation value corresponding to each type of operating parameter contained in the operating parameter data is retrieved. The fluctuation coefficient corresponding to each type of operating parameter is obtained by using the deviation value corresponding to each type of operating parameter. Compare the volatility coefficient corresponding to each type of operating parameter with the preset volatility coefficient threshold; When the volatility coefficient corresponding to any operating parameter type exceeds its corresponding volatility coefficient threshold, the comprehensive volatility coefficient is obtained by using the volatility coefficients corresponding to all operating parameter types. The overall volatility coefficient is compared with a preset overall volatility coefficient threshold. When the overall volatility coefficient exceeds the preset overall volatility coefficient threshold, it is determined that there is a risk of operational abnormality and a risk warning is issued.

4. The adaptive synchronous coordination control method for the laying vessel drum and tipper device as described in claim 1, characterized in that, Establish an adaptive synchronous coordination control mathematical model for the laying vessel drum and tipper device, and perform the following operations: Collect historical operational data of the laying vessel's drum and tipping device; Based on deep learning technology, the deep learning model is trained according to the collected historical operation data of the laying vessel drum and tipping device, and an artificial intelligence-based adaptive synchronous coordination control mathematical model for the laying vessel drum and tipping device is constructed. The constructed AI-based adaptive synchronous coordination control mathematical model for the laying vessel drum and flap device was tested. Based on accuracy, recall rate and F1-score, it was determined whether the constructed AI-based adaptive synchronous coordination control mathematical model for the laying vessel drum and flap device could achieve the expected effect, and the test results of the mathematical model were determined. When the constructed AI-based adaptive synchronous coordination control mathematical model for the laying vessel drum and flap device fails to achieve the expected results, the parameters and structure of the constructed AI-based adaptive synchronous coordination control mathematical model for the laying vessel drum and flap device are adjusted, and the AI-based adaptive synchronous coordination control mathematical model for the laying vessel drum and flap device is continuously iterated and optimized to determine the optimal adaptive synchronous coordination control mathematical model for the laying vessel drum and flap device.

5. The adaptive synchronous coordination control method for the laying vessel drum and tipper device as described in claim 4, characterized in that, The operational characteristic data of the laying vessel drum and tipper device based on the Internet of Things were analyzed, and the following operations were performed: Obtain the optimal adaptive synchronous coordination control mathematical model for the laying vessel drum and flap device, and deploy the optimal adaptive synchronous coordination control mathematical model for the laying vessel drum and flap device in the actual adaptive synchronous coordination control environment of the laying vessel drum and flap device. The operational characteristic data of the laying vessel drum and tipper device based on the Internet of Things are input into the optimal adaptive synchronous coordination control mathematical model of the laying vessel drum and tipper device. The operational characteristic data of the laying vessel drum and tipper device based on the optimal adaptive synchronous coordination control mathematical model of the laying vessel drum and tipper device are analyzed. Determine how the laying vessel's drum and tipper device can adaptively and synchronously coordinate control, adjust and optimize the operating characteristic data of the laying vessel's drum and tipper device, determine the adaptive synchronous and coordinated control results of the laying vessel's drum and tipper device, and perform adaptive synchronous and coordinated control on the laying vessel's drum and tipper device.

6. The adaptive synchronous coordination control method for the laying vessel drum and tipper device as described in claim 5, characterized in that, Adaptive synchronous coordination control is applied to the laying vessel drum and tipper device to perform the following operations: When the cable is being wound up or unwound by the cable reel device on the laying vessel, the cable-turning device on the laying vessel adjusts the turning angle in a timely manner according to the position and length of the cable, so that the cable can pass smoothly through the cable reel device on the laying vessel. When the cable-laying vessel's tipping device is adjusting its position, the cable-laying vessel's drum device adjusts its winding and unwinding speed in a timely manner according to the adjusted position of the cable-laying vessel's tipping device, so that the cable-laying vessel's tipping device can position the cable and lay the cable smoothly along the predetermined path.

7. The adaptive synchronous coordination control method for the laying vessel drum and tipper device as described in claim 6, characterized in that, An adaptive synchronous coordinated control evaluation and optimization of the laying vessel drum and tipper device is performed, and the following operations are carried out: After adaptive synchronous coordination control of the laying vessel's drum and tipping device, the system monitors and evaluates the operation of the laying vessel's drum and tipping device in real time, obtains timely operational feedback from the laying vessel's drum and tipping device, issues timely warnings and alarms when the laying vessel's drum and tipping device malfunctions, and performs maintenance and management of the laying vessel's drum and tipping device to optimize the laying vessel's drum and tipping device.

8. The adaptive synchronous coordination control method for the laying vessel drum and tipper device as described in claim 1, characterized in that, Obtain real-time operational data of the laying vessel's drum and tipper device, and perform the following operations: Sensors are installed on the laying vessel drum and tipping device based on Internet of Things (IoT) technology. Based on sensors, the diameter, width, speed, torque, moment of inertia, and applied tension of the laying vessel drum device are monitored and collected in real time to obtain the operating data of the laying vessel drum device. Based on sensors, the flipping angle, movement range, movement speed and positioning of the planking vessel tipping device are monitored and collected in real time to obtain the planking vessel tipping device operation data; Specifically, based on the operating data of the laying vessel's drum device and the laying vessel's tipping device, real-time operating data of the laying vessel's drum and tipping device based on the Internet of Things are determined.

9. The adaptive synchronous coordination control method for the laying vessel drum and tipper device as described in claim 1, characterized in that, The real-time operational data of the IoT-based laying vessel drum and tipping device are preprocessed, and the following operations are performed: Acquire real-time operational data of the laying vessel's drum and tipping device based on the Internet of Things; Using Pandas tools, the real-time operation data of the laying vessel drum and tipper device based on IoT is cleaned to remove duplicate, missing, and outlier values ​​that are useless for the adaptive synchronization and coordination control of the laying vessel drum and tipper device. Based on the sqlitebiter tool, the real-time operating data of the laying vessel drum and tipping device based on IoT is transformed to remove the dimensional differences between the real-time operating data of the laying vessel drum and tipping device based on IoT, and to determine the standardized real-time operating data of the laying vessel drum and tipping device.

10. The adaptive synchronous coordination control method for the laying vessel drum and tipper device as described in claim 1, characterized in that, The real-time operational data of the IoT-based laying vessel drum and tipping device are preprocessed, and the following operations are performed: Acquire real-time operational data of the laying vessel's drum and tipping device based on the Internet of Things; Based on the bubble sorting method, the real-time operation data of the laying vessel drum and tipping device based on the Internet of Things are sorted to determine the real-time operation data of the laying vessel drum and tipping device with order. Based on principal component analysis, feature extraction is performed on the real-time operating data of the laying vessel drum and tipping device based on the Internet of Things (IoT). The extracted feature vectors are useful for the adaptive synchronous coordination control of the laying vessel drum and tipping device, and the operating feature data of the laying vessel drum and tipping device based on the IoT are determined.

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