Submarine cable laying tension control method based on environment judgment

Abstract

The invention discloses a submarine cable laying tension control method based on environment judgment, and the method comprises the following steps for each adjustment time slot t: (1) obtaining the ship speed v (t) of a cable laying ship at the current moment, the laying operation condition parameters of the cable laying ship, and the environment parameters; (2) predicting the target tension allowance Ts of the laying operation at the current moment by using the regression model; (3) target tension T (t + 1) is calculated, the target tension is the difference between the maximum tension Tmax capable of being borne by the cable and the target tension allowance Ts (t + 1), and the target ship speed V (t + 1) of the cable laying ship is obtained according to relation mapping of the cable tension and the ship speed; and (4) controlling the rotating speed of the motor by adopting a feedback regulation algorithm, and updating the target cable laying speed Vc (t + 1) at the next moment. The tension allowance is determined according to expert experience by adopting a regression algorithm, the cable laying tension is adaptively adjusted according to different submarine topographies, ocean current speeds and other factors, it is guaranteed that the tension of loose filling is appropriate, the sheath abrasion risk is reduced, and the submarine cable laying reliability is guaranteed.

Description

Technical Field

[0001] The present invention belongs to the field of marine communication engineering, and more specifically, relates to a method and system for controlling the tension of submarine cable laying based on environmental judgment. Background Art

[0002] With the increasing global demand for energy, submarine cables, as the lifeline connecting land and sea, have become increasingly important. As the core carrier of global communication and power transmission, the tension control during the laying process of submarine cables is directly related to the lifespan of the submarine cables and the reliability of the system. Tension control needs to adapt to different sea area environments: in shallow waters, tension laying is adopted, and a preset tension value is used to reduce the sag of the submarine cable, while in deep waters, slack laying is mostly adopted, that is, the release speed of the submarine cable is slightly higher than the ship speed to adapt to terrain changes. Currently, existing methods use adaptive PID to control the tension of submarine cable laying to ensure the accuracy and stability of tension control.

[0003] However, the complex marine environment brings multiple technical challenges. Submarine cable laying faces a complex and changeable seabed environment, especially in areas with complex seabed topography or adverse geological phenomena, where the laying difficulty increases significantly. In this case, the control of filling slack becomes a key issue in the tension control of submarine cable laying, which directly affects the stability and laying effect of the submarine cable.

[0004] The complexity of the seabed topography is mainly reflected in the following aspects: features such as elevation difference, slope, steep seabed mountains or canyons, etc., which will pose challenges to the laying and fixing of submarine cables. For example, when burying in soft soil areas with high water content and low bearing capacity, due to insufficient bearing capacity, the submarine cable may sink or overturn, increasing the recovery difficulty or damaging the submarine cable being laid. In addition, adverse geological phenomena such as underwater slope instability, pockmarks and sand waves are also the main adverse geological phenomena affecting submarine cable projects.

[0005] Due to the complexity of the above topography, it is necessary to adjust the filling slack to adapt to the specific conditions of the seabed. However, improper adjustment of the filling slack will lead to negative slack phenomenon, which will in turn cause deformation, damage or even fracture of the submarine cable. The occurrence of the negative slack phenomenon is closely related to the tension control of the submarine cable. Once the tension exceeds the allowable value of the submarine cable, the submarine cable may be damaged. In addition, the importance lies in that the most dangerous areas are the areas where the cable exits the ship and the touchdown areas, and the tension control in these areas is particularly difficult.

[0006] Traditional methods rely on experience to adjust the tension control of submarine cable laying to achieve filling slack, but the negative slack phenomenon will cause friction between the submarine cable and the seabed, increasing the risk of sheath wear. Summary of the Invention

[0007] In view of the above defects or improvement requirements of the prior art, the present invention provides a method for controlling the tension of submarine cable laying based on environmental judgment, aiming to incorporate working condition parameters and environmental parameters into the consideration of tension margin design, and then adopt feedback regulation to control the ship speed, so as to ensure that the tension control of submarine cable laying is suitable for different working conditions and environments, thereby solving the technical problem that the existing automatic tension control method cannot well perform adaptive filling relaxation, resulting in an increased risk of sheath wear.

[0008] To achieve the above object, according to one aspect of the present invention, there is provided a method for controlling the tension of submarine cable laying based on environmental judgment, which is characterized in that for each adjustment time slot t, it includes the following steps:

[0009] (1) Obtain the ship speed v(t) of the cable laying ship at the current moment, the working condition parameters of the cable laying operation of the cable laying ship, and environmental parameters;

[0010] (2) Use a regression model to predict the target tension margin T of the laying operation at the current moment according to the working condition parameters of the cable laying operation of the cable laying ship obtained in step (1) and the environmental parameters s ;

[0011] (3) Calculate the target tension T(t + 1) according to the target tension margin T s (t + 1) obtained in step (2), where the target tension is the maximum tension T that the cable can withstand max minus the target tension margin T s (t + 1), and obtain the target ship speed V(t + 1) of the cable laying ship according to the relationship mapping between the cable tension and the ship speed;

[0012] (4) According to the ship speed v(t) at the current moment and the target ship speed V(t + 1), use a feedback regulation algorithm to control the motor speed and update the target cable laying speed V c (t + 1), and the target cable laying speed V c (t + 1) maintains a fixed differential speed with the target ship speed V(t + 1).

[0013] Preferably, in the method for controlling the tension of submarine cable laying based on environmental judgment, the ship speed v(t) of the cable laying ship in step (1) is obtained according to the measured motor speed.

[0014] Preferably, in the method for controlling the tension of submarine cable laying based on environmental judgment, the working condition parameters in step (1) include the submarine cable tension, the entry angle α(t), and the cable laying speed v c(t). The working condition parameters reflect the working state of the cable laying ship; among them: the actual tension of the submarine cable at the current moment is measured by detecting it in real time through a tension sensor and outputting the signal through a transmitter connected thereto, and after A / D conversion by a preamplifier filter and a multi-functional data acquisition card, it is input into a computer; the entry angle α(t) detects the change of the cable laying angle through an angle sensor, and the cable laying speed v c (t) monitors the cable laying speed through a speed sensor.

[0015] Preferably, for the submarine cable laying tension control method based on environment judgment, the environmental parameters in step (1) include wind speed, seawater depth, sea current speed v s (t), laying area category, and average slope; the environmental parameters reflect the environmental interference state including ocean currents, waves, and terrain mutations, and dynamically reflect the real-time laying construction influence; among them: the wind speed is measured by an anemometer; the seawater depth is obtained through a seabed elevation map; the sea current speed is measured by an impeller; for the laying area category, the edge coordinates are obtained by monitoring the edge contours in the image using an edge detection algorithm according to the seabed elevation image, and the elevation image blocks are divided into regions according to the edge contours and the category of the region is judged.

[0016] Preferably, for the submarine cable laying tension control method based on environment judgment, the category of the region is used to characterize the terrain category, and the category can be predefined or classified using a trained and converged classifier.

[0017] Preferably, for the submarine cable laying tension control method based on environment judgment, the average slope is the average value of the change rates of the grid elevation of the grid in all directions, and the calculation method is as follows:

[0018]

[0019] Among them, S (i,j) is the slope of a grid with a size of d x ×d y relative to the surrounding grids. The relative coordinates of this grid are (0, 0), and the coordinates of the surrounding grids are (i, j), where i = -1, 0, 1 and j = -1, 0, 1, and i and j are not both 0 at the same time; the calculation method is as follows:

[0020]

[0021] Among them is the average elevation of the grid with coordinates (i, j), is the average elevation of this grid, and the average elevation of the grid is calculated according to the following method:

[0022]

[0023] Among them, h is the elevation value of the pixel within the grid.

[0024] Preferably, for the submarine cable laying tension control method based on environmental judgment, in step (2), the regression model uses a BP neural network.

[0025] For the regression model, its inputs are: the working condition parameters of the cable laying operation of the cable laying ship and the environmental parameter working condition parameters; its output is the target tension margin T s (t + 1);

[0026] For the regression model, its inputs are: the working condition parameters of the cable laying operation of the cable laying ship and the environmental parameter working condition parameters; its output is the target tension margin T s (t + 1);

[0027] Preferably, for the submarine cable laying tension control method based on environmental judgment, in step (3), the target tension T(t + 1) is calculated as follows:

[0028] T(t + 1) = T max - T s (t + 1)

[0029] The target ship speed V(t + 1) is calculated according to the following method:

[0030]

[0031] Among them, ρ is the seawater density, g is the acceleration due to gravity, ρ1 is the cable density, r is the cable radius, h(t) is the seawater depth at the current moment, α(t) is the entry angle at the current moment, r is the submarine cable radius, C s is the resistance coefficient in the axial direction of seawater, v s (t) is the sea current speed.

[0032] Preferably, for the submarine cable laying tension control method based on environmental judgment, in step (4), a fuzzy PID controller is used to control the motor speed. Specifically, according to the ship speed v(t) at the current moment and the target ship speed V(t + 1), reasoning is carried out according to fuzzy rules and defuzzification is performed to obtain the exact value of the differential coefficient k(t), and a PID controller is used to control the motor speed.

[0033] Preferably, for the submarine cable laying tension control method based on environmental judgment, the fuzzy PID controller includes a fuzzy inference module and a PID control module;

[0034] Fuzzy inference module: Integrated with a fuzzy set and a defuzzy set, according to the ship speed v(t) at the current moment and the target ship speed V(t + 1), a speed adjustment rule is obtained according to fuzzy rules, and the defuzzy set uses the centroid method to calculate the exact differential control coefficient;

[0035] The PID control module controls the motor speed according to the PID algorithm based on the ship speed v(t) at the current moment and the target ship speed V(t + 1), in accordance with the differential control coefficient k(t) output by the fuzzy inference module.

[0036] Generally speaking, compared with the prior art, the above technical solution conceived by the present invention can achieve the following beneficial effects:

[0037] The submarine cable laying tension control method based on environmental judgment provided by the present invention determines the tension margin according to the working condition parameters and environmental parameters by using a regression algorithm, and adaptively adjusts the cable laying tension according to different factors such as the seabed topography and sea current speed, ensuring that the tension of the slack filling is appropriate, reducing the risk of sheath wear, and ensuring the reliability of submarine cable laying. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 are the BP neural network structure parameters adopted by the submarine cable laying tension control method based on environmental judgment provided by the embodiments of the present invention;

[0039] Figure 2 is the simulation effect diagram of the BP neural network adopted by the submarine cable laying tension control method based on environmental judgment provided by the embodiments of the present invention;

[0040] Figure 3 is the force analysis diagram of the submarine cable;

[0041] Figure 4 is the schematic diagram of the fuzzy PID controller structure adopted by the submarine cable laying tension control method based on environmental judgment provided by the embodiments of the present invention;

[0042] Figure 5 is the result diagram of the fuzzy PID controller adopted by the submarine cable laying tension control method based on environmental judgment provided by the embodiments of the present invention;

[0043] Figure 6 is the simulation result diagram of the fuzzy PID controller adopted by the submarine cable laying tension control method based on environmental judgment provided by the embodiments of the present invention;

[0044] Figure 7 is the training effect diagram of the submarine cable laying tension control method based on environmental judgment provided by the embodiments of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0045] In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0046] The submarine cable laying tension control method based on environment judgment provided by the present invention includes the following steps for each adjustment time slot t:

[0047] (1) Obtain the ship speed v(t) of the cable laying ship at the current moment, the cable laying operation condition parameters of the cable laying ship, and the environmental parameters;

[0048] The ship speed v(t) of the cable laying ship is obtained according to the measured motor speed;

[0049] The condition parameters include the submarine cable tension, the entry angle α(t), and the cable laying speed v c (t). The condition parameters reflect the working state of the cable laying ship; where: the submarine cable laying tension at the current moment is detected in real time by a tension sensor, and the signal is output through a transmitter connected thereto, and after A / D conversion by a preamplifier filter and a multifunctional data acquisition card, it is input into a computer, so as to measure the actual tension of the submarine cable at the current moment; the entry angle α(t) detects the change of the cable laying angle through an angle sensor, and the cable laying speed v c (t) monitors the cable laying speed through a speed sensor;

[0050] The environmental parameters include the wind speed, the seawater depth h(t), the sea current speed v s (t), the laying area category, and the average slope; the environmental parameters reflect the environmental interference state including ocean currents, waves, and terrain mutations, and dynamically reflect the real-time laying construction influence; where: the wind speed is measured by an anemometer; the seawater depth is obtained through a seabed elevation map; the sea current speed is measured by an impeller; the laying area category is obtained by monitoring the edge contour in the image using an edge detection algorithm according to the seabed elevation image to obtain the edge coordinates, and the elevation image block is divided into regions according to the edge contour and the category of the region is judged; the category of the region is used to characterize the terrain category, and the category can be predefined or classified using a trained and converged classifier; the average slope is the average value of the change rates of the grid elevation of the grid where it is located in all directions, and the calculation method is as follows:

[0051]

[0052] Where, S (i,j) is the slope of a grid with a size of d x ×d y relative to the surrounding grids, the relative coordinates of this grid are (0,0), and the coordinates of the surrounding grids are (i,j), i = -1, 0, 1, j = -1, 0, 1, and i and j are not both 0 at the same time; the calculation method is as follows:

[0053]

[0054] Where is the average elevation of the grid with coordinates (i, j). is the average elevation of the grid, and the average elevation of the grid is calculated as follows:

[0055]

[0056] where h is the elevation value of the pixel within the grid.

[0057] (2) According to the cable laying operation condition parameters obtained in step (1) and the environmental parameter condition parameters, use the regression model to predict the target tension margin T of the laying operation at the current moment s ;

[0058] The tension margin refers to the preset additional tension safety margin during the laying of submarine cables, which is used to cope with the tension fluctuations caused by dynamic environmental disturbances. Its core function is to ensure that the actual stress of the cable is always lower than the material ultimate strength and maintain the stability of the catenary shape, avoiding structural damage caused by insufficient or excessive tension.

[0059] For the regression model, a BP neural network is used. The BP neural network is a feedforward neural network with strong generalization ability, consisting of three-layer network structures: an input layer, a hidden layer, and an output layer. In the input layer, the input variables are transmitted to the neural network controller. In the prediction of the cable laying tension margin, the role of using the BP neural network is to predict the cable tension margin based on the input condition parameters and environmental characteristics, thereby helping to optimize the cable laying process and ensuring the safety and effectiveness of the cable.

[0060] For the regression model, its input is: the cable laying operation condition parameters of the cable laying ship and the environmental parameter condition parameters; its output is: the target tension margin T s (t + 1);

[0061] The training data of the regression model are the empirical data of actual cable laying, and the loss function uses the mean square error.

[0062] (3) Calculate the target tension T(t + 1) according to the target tension margin T s (t + 1) obtained in step (2). The target tension is the maximum tension T that the cable can withstand max minus the target tension margin T s (t + 1), and obtain the target ship speed V(t + 1) of the cable laying ship according to the relationship mapping between the cable tension and the ship speed; the calculation method of the target tension T(t + 1) is as follows:

[0063] T(t + 1) = T max -T s (t + 1)

[0064] The target ship speed V(t + 1) is calculated as follows:

[0065]

[0066] Where ρ is the seawater density, g is the acceleration due to gravity, ρ1 is the cable density, r is the cable radius, h(t) is the seawater depth at the current moment, α(t) is the entry angle at the current moment, r is the submarine cable radius, C s is the resistance coefficient in the axial direction of seawater, v s (t) is the sea current speed.

[0067] (4) According to the ship speed v(t) and the target ship speed V(t + 1) at the current moment, a feedback control algorithm is used to control the motor speed, and the target cable laying speed V c (t + 1) is updated. The target cable laying speed V c (t + 1) maintains a fixed differential speed with the target ship speed V(t + 1), that is: V c (t + 1) = γV(t + 1), where γ is a fixed differential speed ratio, taking a value greater than 1;.

[0068] In the preferred solution, a fuzzy PID controller is used to control the motor speed. Specifically, according to the ship speed v(t) and the target ship speed V(t + 1) at the current moment, reasoning is carried out according to fuzzy rules and defuzzification is performed to obtain the accurate value k(t) of the differential coefficient, and a PID controller is used to control the motor speed.

[0069] Fuzzy PID control is a feedback control algorithm that combines fuzzy control and PID control. By inputting the error and its change rate into the fuzzy PID control and using fuzzy rules for parameter tuning, online real-time control of the system can be achieved. Fuzzy PID control can adjust the system online in real time and has good stability. Since the target tension is based on the prediction of the regression model, when the data volume is small or incomplete, the jitter amplitude is large, and it is easy to have the situation of accelerating or decelerating too fast when controlling the ship speed according to the target tension. Through fuzzy reasoning, the control is made more stable, and the jitter phenomenon caused by the prediction of the target tension based on the regression model is balanced.

[0070] Among them, the fuzzy PID controller includes a fuzzy reasoning module and a PID control module;

[0071] Fuzzy reasoning module: Integrated with a fuzzy set and a defuzzy set, according to the ship speed v(t) and the target ship speed V(t + 1) at the current moment, a speed adjustment rule is obtained according to fuzzy rules, and the defuzzy set calculates the accurate differential control coefficient by the centroid method;

[0072] The PID control module, according to the ship speed v(t) and the target ship speed V(t + 1) at the current moment, according to the differential control coefficient k(t) output by the fuzzy reasoning module, controls the motor speed according to the PID algorithm.

[0073] Make the target cable laying speed V c (t + 1) and the target ship speed V(t + 1) maintain a fixed differential speed, further relaxing the tension margin and reducing the risk of sheath friction.

[0074] The following are examples:

[0075] The method for controlling the tension of submarine cable laying based on environmental judgment provided in this embodiment includes the following steps for each adjustment time slot t:

[0076] (1) Obtain the submarine cable laying tension, the cable laying operation condition parameters of the cable laying ship, and the environmental parameters at the current moment;

[0077] The ship speed v(t) of the cable laying ship is obtained according to the measured motor speed;

[0078] The said condition parameters include the submarine cable tension T(t), the entry angle α(t), and the cable laying speed v c (t). The condition parameters reflect the working state of the cable laying ship; among them: the actual tension of the submarine cable at the current moment is measured by a tension sensor in real time and the signal is output through a transmitter connected thereto, and after A / D conversion by a preamplifier filter and a multi-functional data acquisition card, it is input into a computer; the entry angle α(t) detects the change of the cable laying angle through an angle sensor, and the cable laying speed v c (t) monitors the cable laying speed through a speed sensor;

[0079] The said environmental parameters include wind speed, seawater depth, sea current speed τ s (t), the category of the laying area, and the average slope; the environmental parameters reflect the environmental interference state including ocean currents, waves, and terrain mutations, and dynamically reflect the real-time laying construction impact; among them: the wind speed is measured by an anemometer; the seawater depth is obtained through a seabed elevation map; the sea current speed is measured by an impeller; the category of the laying area is obtained by monitoring the edge contour in the image using an edge detection algorithm according to the seabed elevation image to obtain the edge coordinates, and the elevation image blocks are divided into regions according to the edge contour and the category of the region is judged; the category of the region is used to characterize the terrain category, and the category can be predefined or classified using a trained and converged classifier; the average slope is the average value of the change rate of the grid elevation of the grid where it is located in each direction, and the calculation method is as follows:

[0080]

[0081] Wherein, S (i,j) is of size d x ×d yThe slope of the grid with respect to the surrounding grids, where the relative coordinates of this grid are (0, 0), and the coordinates of the surrounding grids are (i, j), i = -1, 0, 1, j = -1, 0, 1, and i and j are not both 0 at the same time; the calculation method is as follows:

[0082]

[0083] where is the average elevation of the grid with coordinates (i, j), is the average elevation of this grid, and the average elevation of the grid is calculated according to the following method:

[0084]

[0085] where h is the elevation value of the pixel within the grid.

[0086] (2) Predict the target tension margin T of the laying operation at the current moment using the laying operation condition parameters of the cable laying vessel obtained in step (1) and the environmental parameter condition parameters s ;

[0087] The prediction of the submarine cable tension margin usually involves complex non-linear relationships, including the interaction of multiple factors such as the marine environment and installation conditions. The prediction of the submarine cable tension margin usually requires considering multiple input variables simultaneously. The prediction model of the submarine cable tension margin needs to have a certain adaptive ability to cope with data changes under different conditions.

[0088] Reasons for choosing the BP network: The BP neural network is a multi-layer feedforward network with strong non-linear fitting ability. Through the non-linear activation function Sigmoid of the hidden layer, the BP neural network can effectively model complex non-linear relationships and is suitable for dealing with complex problems in submarine cable tension margin prediction. It can process multiple input variables simultaneously and automatically learn the complex relationships between variables through the network structure. Compared with traditional multiple linear regression models, the BP neural network is more suitable for dealing with high-dimensional data and non-linear interactions between variables. It can continuously adjust the network weights through the backpropagation algorithm and can adaptively optimize the model parameters according to the training data. This self-adaptability makes the BP neural network have strong generalization ability when facing different scenarios and can better adapt to the uncertainties in submarine cable tension margin prediction.

[0089] Therefore, due to its powerful non-linear modeling ability, multi-variable processing ability, adaptive learning ability, high-precision prediction, robustness and scalability, the BP neural network becomes an ideal choice for predicting the submarine cable tension margin. Compared with other regression models, such as linear regression, polynomial regression and other models, the BP neural network is more suitable for dealing with complex and non-linear prediction problems in ocean engineering.

[0090] The regression model has the following inputs: the working condition parameters of the cable laying vessel during the laying operation and the environmental parameter working condition parameters; and its output is the target tension margin T s (t + 1);

[0091] The training data of the regression model is the empirical data of actual submarine cable laying, and the mean square error is used as the loss function.

[0092] The code of this embodiment constructs a feedforward neural network by setting the parameters of the BP neural network, uses the mapminmax function to normalize the input data. At the same time, the display interval, learning rate, and maximum number of training times during the training process are set to ensure that the network can learn and predict efficiently. Specifically for the task of this patent, the network structure is designed with 10 neurons in the first hidden layer and 10 neurons in the second hidden layer, the learning rate is set to 0.035, the number of training times is 1000, and the convergence error is 1e-5. Among them, the pressure transformation value is used as the input of a neuron in the input layer, and the ship speed is used as the target output of the output layer, as follows Figure 1 shown. Initialize the network parameters, including weights and biases, which will be continuously adjusted in the subsequent training process to optimize the network performance.

[0093] Divide the data set into a training set, a validation set, and a test set. In the training stage, input the working condition parameters and environmental parameters into the BP neural network, calculate the predicted tension margin through forward propagation, and compare it with the tension in the data set and the beam, and calculate the mean square error (MSE). Use the backpropagation algorithm to adjust the weights and biases, gradually reduce the error, and enable the neural network to learn the mapping relationship between the pressure transformation value and the tension margin.

[0094] % Custom neural network structure

[0095] hidden_layer_sizes = [10, 10]; % Two hidden layers, with 10 and 10 neurons respectively. net = feedforwardnet(hidden_layer_sizes); % Create a neural network using the custom hidden layer structure

[0096] % Set training parameters

[0097] net.trainParam.lr = 0.035; % Learning rate

[0098] net.trainParam.epochs = 1000; % Number of training times

[0099] net.trainParam.goal = 1e-5; % Convergence error

[0100] % Train the neural network

[0101] [net, tr] = train(net, x1, y1); % Use the normalized data for neural network training, where x1 is the value after normalizing the input x value of the training data, and y1 is the value after normalizing the output y value of the training data.

[0102] Plot the system output and desired output curves to visually display the simulation results, as Figure 2 shown.

[0103] (3) Calculate the target tension T(t + 1) according to the target tension margin T(t + 1) obtained in step (2). The target tension is the maximum tension T that the cable can withstand s minus the target tension margin T(t + 1), and obtain the target ship speed V(t + 1) of the cable laying ship according to the relationship mapping between the cable tension and the ship speed; The calculation method of the target tension T(t + 1) is as follows: max and the target tension margin T(t + 1), s and obtain the target ship speed V(t + 1) of the cable laying ship according to the relationship mapping between the cable tension and the ship speed; The calculation method of the target tension T(t + 1) is as follows:

[0104] T(t + 1) = T max - T(t + 1) s

[0105] The target ship speed V(t + 1) is calculated as follows:

[0106]

[0107] where ρ is the seawater density, g is the acceleration due to gravity, ρ1 is the cable density, r is the cable radius, h(t) is the seawater depth at the current moment, α(t) is the entry angle at the current moment, r is the submarine cable radius, C s is the resistance coefficient in the axial direction of seawater, v(t) s is the sea current speed.

[0108] During the submarine cable operation, understanding the force situation of the cable is crucial for ensuring the smooth progress of the laying process. It can be seen from the force analysis diagram of the submarine cable that assuming the configuration of the cable in seawater is approximately a straight line, its forces mainly include tension, mainly composed of tensile force, fluid resistance, and gravity.

[0109] In actual engineering, marine environmental factors have a significant impact on the force situation of submarine cable laying operations. Use force balance to analyze the steady-state characteristics of cable laying, as Figure 3 shown:

[0110] Select the Morison equation as the empirical formula for the impact of the marine environment on the project in this topic. This formula considers factors such as fluid density, resistance coefficient, relative velocity, and frontal area, and can be used to estimate the tangential and normal fluid resistances. At steady state, the force mode of submarine cable laying is as follows:

[0111] The force analysis along the cable direction is T(t) + Ds(t) - G·cosα(t) = 0, and the expression for the tension can be obtained as:

[0112] T(t) = G·cosα(t) - Ds(t)

[0113] where T(t) is the tension of the cable at the current moment, α(t) is the cable entry angle, Ds(t) is the axial fluid resistance, and G is the gravity of the cable.

[0114] According to the Morison equation, the formula for the axial fluid resistance is:

[0115]

[0116] where: ρ is the fluid density, C s is the axial resistance coefficient, S is the cross-sectional area of the cable, and v c is the cable laying speed.

[0117] Combining the above equations and substituting S = πr 2 and V s = v s cosα(t), where r is the cable radius and V s is the speed of the cable laying vessel, the relationship mapping between the tension and the vessel speed is obtained:

[0118]

[0119] V(t + 1) = V s - v s (t)

[0120] where ρ1 is the cable density, g is the acceleration due to gravity, r is the cable radius, and h(t) is the seawater depth at the current moment.

[0121] The calculation formula for V(t + 1) after simplifying the equation is as follows:

[0122]

[0123] It shows that the laying water depth h(t), the entry angle α(t), and the relative vessel speed are the key factors determining the cable tension. The dynamic characteristics in actual work are analyzed as follows:

[0124] When the vessel speed increases, the tension first decreases and then stabilizes;

[0125] When the vessel speed decreases, the tension first increases and then stabilizes;

[0126] When the water depth increases, the tension may increase or decrease (depending on the changes in the vessel speed and the angle).

[0127] Based on the above force analysis, this paper designs a corresponding control scheme to optimize the submarine cable laying process. By adjusting the ship speed and the cable lowering speed, the tension of the cable can be effectively controlled to ensure the stability and safety of the laying process.

[0128] (3) According to the current ship speed v(t) and the target ship speed V(t + 1), a feedback adjustment algorithm is used to control the motor speed and update the target cable laying speed V c (t + 1). The target cable laying speed V c (t + 1) maintains a fixed differential speed with the target ship speed V(t + 1), that is: V c (t + 1) = γV(t + 1), where γ is a fixed differential speed ratio, taking a value greater than 1.

[0129] In this embodiment, a fuzzy PID controller is used to control the motor speed. Specifically, according to the current ship speed v(t) and the target ship speed V(t + 1), reasoning is carried out according to fuzzy rules and defuzzification is performed to obtain the exact value k(t) of the differential coefficient, and a PID controller is used to control the motor speed.

[0130] Fuzzy PID control is a feedback adjustment algorithm that combines fuzzy control and PID control. By inputting the error and its change rate into the fuzzy PID control and using fuzzy rules for parameter tuning, online real-time control of the system can be achieved. Fuzzy PID control can adjust the system online in real time and has good stability. Since the target tension is based on the prediction of the regression model, when the data volume is small or incomplete, the jitter amplitude is large, and it is easy to accelerate or decelerate too quickly when controlling the ship speed according to the target tension. Through fuzzy reasoning, the control is made more stable to balance the jitter phenomenon caused by the prediction of the target tension based on the regression model.

[0131] Among them, the fuzzy PID controller includes a fuzzy inference module and a PID control module;

[0132] Fuzzy inference module: Integrated with a fuzzy set and a defuzzy set, according to the current ship speed v(t) and the target ship speed V(t + 1), a speed adjustment rule is obtained according to fuzzy rules. The defuzzy set calculates the exact differential control coefficient using the centroid method;

[0133] PID control module, according to the current ship speed v(t) and the target ship speed V(t + 1), according to the differential control coefficient k(t) output by the fuzzy inference module, controls the motor speed according to the PID algorithm.

[0134] In the control of submarine cable laying, the selection of fuzzy inference rules is crucial because the submarine cable laying process involves complex non-linear dynamic characteristics, environmental uncertainties, and multi-variable coupling. The Fuzzy Inference System (FIS) can effectively handle these uncertainties and design control rules based on expert experience.

[0135] Fuzzy rules are formulated based on the relationship between the ship speed error (ev) and the ship speed adjustment amount (Δv). The ship speed error (ev), which is the difference between the current ship speed and the target ship speed and the error change rate, and the ship speed adjustment amount (Δv), which is the difference between the target ship speed V(t + 1) and the ship speed v(t) of the cable laying ship. The following rules are formulated:

[0136]

[0137] Among them, the fuzzy sets are: NB (Negative Big), NM (Negative Medium), NS (Negative Small), ZO (Zero), PS (Positive Small), PM (Positive Medium), PB (Positive Big).

[0138] The Mamdani fuzzy inference method is used to solve the fuzzy implication relationship because its rule expression is intuitive and suitable for systems based on expert experience.

[0139] Defuzzification method: The Centroid Method is used to calculate the exact output value.

[0140] Assume the fuzzy control object is After building the fuzzy controller, enter fuzzy in the matlab command window, and the corresponding operation interface will pop up as shown in the figure. Click add variable in edit to add the input variable input. Then double-click the input module, first remove all membership functions, then add the corresponding number of membership functions, and reset the names of the input and output modules and the corresponding function forms. Establish the corresponding fuzzy rules one by one according to 7 for the error, 7 for the error change, and 49 for the corresponding output. After completion, click Rules in View to get the two-dimensional visualization of the rules and the corresponding three-dimensional surface plot shown in the following figure. Save the built fuzzy controller for future use, and import the fuzzy controller into the workspace for use in the model. After that, the Simulink simulation model established in Matlab is shown in the following figure:

[0141] We add traditional PID control on this basis. To implement fuzzy PID control, first modify the fuzzy controller to change the original single output to three outputs Kp, Ki, Kd, and their corresponding membership functions are as follows:

[0142]

[0143]

[0144] Given a step input in [0, 150], the result after Simulink fuzzy PID control is as follows Figure 5 shown, where orange represents the input and blue represents the output of fuzzy PID control:

[0145] Combining BP neural network and fuzzy PID control, the situation of controlling during the simulation of submarine cable laying is obtained. The prediction result and control result are as follows Figure 6 shown: The variation law of the mean square error with the number of training times is as follows Figure 7 shown.

[0146] The experiment shows that this embodiment can ensure relatively accurate control in a short time, ensure that when the force on the cable caused by environmental factors is encountered each time, the ship speed control during the cable laying of the cable laying ship can be well achieved through the input pressure value, and relatively stable and reliable ship speed adjustment control can be realized.

[0147] It is easy for those skilled in the art to understand that the above are only the preferred embodiments of the present invention and are not used to limit the present invention. Any modifications, equivalent replacements, and improvements made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for controlling the tension of submarine cable laying based on environmental judgment, characterized in that, For each adjustment time slot t, the following steps are included: (1) Obtain the cable-laying ship speed v(t) at the current moment, the cable-laying operation condition parameters of the cable-laying ship, and the environmental parameters; (2) Based on the cable-laying operation condition parameters obtained in step (1) and the environmental parameter condition parameters, use a regression model to predict the target tension margin T of the laying operation at the current moment s ; (3) Obtain the target tension margin T according to step (2). s (t + 1) Calculate the target tension T(t + 1), where the target tension is the maximum tension T that the cable can withstand. max And the target tension margin T s (t + 1) difference, and obtain the target ship speed V(t + 1) of the cable laying ship according to the relationship mapping between the cable tension and the ship speed. (4) According to the current ship speed v(t) and the target ship speed V(t + 1), a feedback regulation algorithm is used to control the motor speed, and the target cable laying speed V c (t + 1) is updated. The target cable laying speed V c (t + 1) maintains a fixed differential speed with the target ship speed V(t + 1).

2. The method for controlling the laying tension of submarine cables based on environmental judgment according to claim 1, wherein, The cable-laying ship speed v(t) described in step (1) is obtained according to the measured motor speed.

3. The method for controlling the tension of submarine cable laying based on environmental judgment according to claim 1, wherein The working condition parameters described in step (1) include the submarine cable tension, the entry angle α(t), and the cable laying speed v c (t). The working condition parameters reflect the working state of the cable laying vessel; among them: the actual tension of the submarine cable at the current moment is measured by a tension sensor through real-time detection and outputting the signal through a transmitter connected thereto, and after A / D conversion by a preamplifier filter and a multifunctional data acquisition card, it is input into a computer; the entry angle α(t) detects the change of the cable laying angle through an angle sensor, and the cable laying speed v c (t) monitors the cable laying speed through a speed sensor.

4. The method for controlling the tension of submarine cable laying based on environmental judgment according to claim 1, wherein The environmental parameters described in step (1) include wind speed, seawater depth, sea current speed v s (t), laying area category, and average slope; the environmental parameters reflect the environmental interference states including ocean currents, waves, and terrain mutations, and dynamically reflect the real-time laying construction impacts; among them: the wind speed is measured by an anemometer; the seawater depth is obtained through a seabed elevation map; the sea current speed is measured by an impeller; for the laying area category, edge coordinates are obtained by monitoring the edge contours in the image using an edge detection algorithm based on the seabed elevation image, and the elevation image blocks are divided into regions according to the edge contours and the category of the regions is determined.

5. The method for controlling the tension of submarine cable laying based on environmental judgment according to claim 4, characterized in that The category of the area is used to characterize the terrain category, and the category can be predefined or classified using a trained and converged classifier.

6. The method for controlling the tension of submarine cable laying based on environmental judgment according to claim 4, characterized in that, The average slope is the mean value of the change rate of the grid elevation of the grid where it is located in each direction, and the calculation method is as follows: Among them, S (i,j) is the slope of a grid of size d x ×d y relative to the surrounding grids. The relative coordinates of this grid are (0, 0), and the coordinates of the surrounding grids are (i, j), where i = -1, 0, 1, j = -1, 0, 1, and i and j are not both 0 at the same time. The calculation method is as follows: wherein is the average elevation of the grid at coordinates (i, j), is the average elevation of the grid, and the average elevation of the grid is calculated as follows: where h is the elevation value of the pixel in the grid.

7. The method for controlling the tension of submarine cable laying based on environmental judgment according to claim 1, wherein The regression model described in step (2) uses a BP neural network; The regression model has the following inputs: the working condition parameters of the cable laying operation of the cable laying vessel and the environmental parameter working condition parameters; and the following output: the target tension margin T s (t + 1); The regression model has the following inputs: the laying operation condition parameters of the cable laying vessel and the environmental condition parameters; and the following output: the target tension margin T s (t + 1).

8. The method for controlling the tension of submarine cable laying based on environmental judgment according to claim 1, wherein, The target tension T(t + 1) in step (3) is calculated as follows: T(t + 1)=T max -T s (t + 1) The target ship speed V(t + 1) is calculated according to the following method: Where ρ is the seawater density, g is the acceleration due to gravity, ρ1 is the cable density, r is the cable radius, h(t) is the seawater depth at the current moment, α(t) is the entry angle at the current moment, r is the submarine cable radius, C s is the resistance coefficient in the axial direction of seawater, v s (t) is the sea current velocity.

9. The method for controlling the tension of submarine cable laying based on environmental judgment according to claim 1, characterized in that, Step (4) uses a fuzzy PID controller to control the motor speed. Specifically, according to the ship speed v(t) at the current moment and the target ship speed V(t + 1), reasoning is performed according to the fuzzy rules and defuzzification is carried out to obtain the exact value k(t) of the differential coefficient, and the PID controller is used to control the motor speed.

10. The method for controlling the tension of submarine cable laying based on environmental judgment according to claim 9, wherein, The fuzzy PID controller includes a fuzzy inference module and a PID control module; Fuzzy inference module: Integrated with a fuzzy set and a defuzzy set, according to the ship speed v(t) at the current moment and the target ship speed V(t + 1), a speed adjustment rule is obtained according to the fuzzy rules, and the defuzzy set uses the centroid method to calculate the exact differential control coefficient; PID control module, according to the ship speed v(t) at the current moment and the target ship speed V(t + 1), according to the differential control coefficient k(t) output by the fuzzy inference module, controls the motor speed according to the PID algorithm.

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