Offshore platform motion response prediction method and device based on CEEMDAN-Transformer

Abstract

The present invention discloses a method and device for predicting the motion response of an offshore platform based on CEEMDAN-Transformer, which obtains the motion response monitoring data of an offshore floating platform, pre-processes the data to eliminate outliers and noise; and decomposes the pre-processed motion response data using CEEMDAN. Each decomposed subsequence is divided into a training set and a test set, which are standardized, and a Transformer neural network is built respectively, and the relevant structural parameters of the Transformer neural network are set. The prediction work of the subsequence is carried out to obtain a trained neural network model; the test set is used to test the Transformer neural network; the prediction results of all subsequences are reconstructed to obtain the prediction results of the original motion response, and then a comprehensive quantitative evaluation is carried out on the prediction results of the motion response of the offshore floating platform to realize the prediction work of the motion response of the offshore floating platform. The present invention realizes the accurate prediction work of the motion response of the offshore floating platform and can provide an auxiliary reference for on-site decision-making.

Description

Technical Field

[0001] The present invention belongs to the field of marine engineering technology, and in particular relates to a method and device for predicting motion response of an offshore platform based on CEEMDAN-Transformer. Background Art

[0002] Currently, offshore floating platforms face numerous challenges in operation, such as the harsh marine environment, including strong winds and waves, complex currents, and unpredictable marine weather conditions. Accurately predicting platform motion allows for proactive safety measures, such as adjusting operating parameters, reinforcing the platform structure, and evacuating non-essential personnel. This effectively reduces the risk of accidents and ensures the safety of personnel and the normal operation of equipment on board.

[0003] Offshore oil and gas development operations place high demands on platform stability. Accurate platform motion prediction allows for the optimal scheduling of operational schedules and workflows, thereby improving overall efficiency and reducing operating costs. Accurate platform motion prediction data can provide a crucial basis for offshore platform design. Currently, motion prediction algorithms used for offshore floating platforms suffer from poor generalization, making it difficult to accurately capture the nonlinear characteristics of platform motion in complex sea conditions and providing ineffective support for offshore operations. Summary of the Invention

[0004] To this end, the present invention provides a method and device for predicting the motion response of an offshore platform based on CEEMDAN-Transformer, which solves the problems of low prediction accuracy and poor generalization ability in traditional offshore floating platform motion response prediction.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a method for predicting offshore platform motion response based on CEEMDAN-Transformer, comprising:

[0006] Acquiring motion response monitoring data of an offshore floating platform and preprocessing the motion response monitoring data;

[0007] Performing empirical mode decomposition on the pre-processed motion response monitoring data using the CEEMDAN algorithm to obtain a plurality of subsequences, each of which includes a plurality of intrinsic mode functions (IMFs) and a residual sequence;

[0008] Dividing the obtained subsequences into a training set and a test set, and performing standardization on the data in the training set and the test set;

[0009] Constructing a Transformer neural network model, and configuring neural network training parameters and neural network structure for the Transformer neural network model; training the Transformer neural network model using the standardized training set to obtain the trained Transformer neural network model;

[0010] Testing the trained Transformer neural network model using the standardized test set, summarizing and reconstructing the prediction results of all the test sets to obtain the prediction results of the motion response monitoring data;

[0011] Conduct comprehensive quantitative evaluation on the prediction results of motion response of offshore floating platforms to realize the prediction of motion response of offshore floating platforms.

[0012] As a preferred solution for offshore platform motion response prediction method based on CEEMDAN-Transformer, motion response monitoring data of offshore floating platforms is obtained through differential global positioning system and motion reference unit system;

[0013] The pre-processing method for the motion response monitoring data includes filtering to remove abnormal values ​​and noise.

[0014] As a preferred solution of the offshore platform motion response prediction method based on CEEMDAN-Transformer, the steps of using the CEEMDAN algorithm to perform empirical mode decomposition on the pre-processed motion response monitoring data to obtain several subsequences include:

[0015] a1) Define operator E m (·) Perform m-order empirical mode decomposition on the signal; add multiple Gaussian white noises w that obey the standard normal distribution N(0,1) to the original motion response signal Y(t) i (t), get the motion response signal Y i (t):

[0016] Y i (t)=Y(t)+ε i w i (t),i=1,2,…I

[0017] Where, ε i is the noise coefficient, which is used to determine the signal-to-noise ratio of each step; I is the number of Gaussian white noise added;

[0018] a2) Calculating the obtained multiple motion response signals Y i (t) Carry out empirical mode decomposition to obtain multiple subsequences Define the first IMF as:

[0019]

[0020] The first residual signal R1(t) is:

[0021]

[0022] Where, is the first intrinsic mode function obtained by the CEEMDAN algorithm;

[0023] a3) For the decomposition result R1(t)+ε1E1(w i (t)) signal is subjected to empirical mode decomposition, and the second IMF is obtained as:

[0024]

[0025] Where, is the second eigenmode function obtained by the CEEMDAN algorithm; is the intrinsic mode function of the second subsequence obtained by decomposing multiple Gaussian white noises into the residual R1(t);

[0026] a4) Repeat step a3) to obtain the kth IMF:

[0027]

[0028] Where, is the kth eigenmode function obtained by the CEEMDAN algorithm; is the residual R k-1 (t) After adding multiple Gaussian white noises, the second subsequence intrinsic mode function obtained by each decomposition; R k-1 (t) is the k-1th residual signal left after decomposition; ε k-1 is the noise coefficient of the k-1th step; E k-1 (w i (t)) is the k-1 order empirical mode decomposition of the added Gaussian white noise;

[0029] a5) Determine the k-th order residual signal R k (t) whether it contains at least two extreme values. If it does, continue to decompose and repeat step a3) to obtain the k+1th IMF; if it does not contain at least two extreme values, the decomposition process ends; finally, the time series signal Y(t) is decomposed into all subsequence IMFs that meet the conditions and the residual R representing the signal trend K (t) and.

[0030] As a preferred solution of the offshore platform motion response prediction method based on CEEMDAN-Transformer, the formula for normalizing the data in the training set and the test set is:

[0031]

[0032] Where y i is the motion monitoring value of the offshore floating platform, y norm is the result after standardization, y max is the maximum value of the input data, y min is the minimum value of the input data.

[0033] As a preferred solution of the offshore platform motion response prediction method based on CEEMDAN-Transformer, the neural network training parameter configuration objects of the Transformer neural network model include historical time step, prediction time step, data batch size and number of training rounds;

[0034] The neural network structure configuration objects of the Transformer neural network model include embedding layer, multi-head attention layer, normalization layer, feedforward layer, residual connection layer, and linearization layer; Softmax is used to calculate the probability distribution of data, MSE is selected as the training loss function, and Adam optimizer is selected to update the neural network parameters.

[0035] As a preferred solution of the offshore platform motion response prediction method based on CEEMDAN-Transformer, the steps of training the Transformer neural network model using the standardized training set to obtain the trained Transformer neural network model include:

[0036] b1) Input the data in the training set into the Transformer neural network model to generate the corresponding query vector Q, key vector K and value vector V:

[0037]

[0038] Where W Q 、W K and W V are the weight matrices for query, key, and value respectively, and X is the input data;

[0039] b2) Calculate the attention score using the dot product of the query vector Q and the key vector K:

[0040]

[0041] Where Attention(Q,K,V) is the attention score of the input data; d k is the dimension of the key vector K;

[0042] b3) Calculate the multi-head attention matrix:

[0043]

[0044] MultiHead(Q,K,V)=(head1,head2,…,head h )W O

[0045] In the formula, head i is the i-th attention weight; Corresponding to the weight matrix of the i-th Q, K, V, W O is the final attention matrix after linear transformation; MultiHead(Q,K,V) is the multi-head attention score of the input data;

[0046] b4) Repeat steps b1), b2), and b3) to generate multiple query vectors Q, key vectors K, and value vectors V, and calculate the attention scores and weighted sums respectively; concatenate the results of all attention weights and perform a linear transformation to obtain the final output, thus obtaining the trained Transformer neural network.

[0047] As a preferred solution for the offshore platform motion response prediction method based on CEEMDAN-Transformer, a comprehensive quantitative evaluation of the prediction results of the offshore floating platform motion response is conducted. In the process of predicting the offshore floating platform motion response, the evaluation indicators selected are the root mean square error, mean absolute error and determination coefficient of the motion.

[0048] The formula for the root mean square error RMSE is:

[0049]

[0050] The formula for mean absolute error (MAE) is:

[0051]

[0052] Coefficient of determination R 2 The formula is:

[0053]

[0054] Where y i is the platform motion monitoring value, is the platform motion prediction value, is the average value of the platform motion monitoring value; n is the number of motion data points.

[0055] The present invention also provides a CEEMDAN-Transformer-based offshore platform motion response prediction device, comprising:

[0056] A motion response monitoring data processing module is used to obtain motion response monitoring data of an offshore floating platform and pre-process the motion response monitoring data;

[0057] A monitoring data empirical mode decomposition module is used to perform empirical mode decomposition on the pre-processed motion response monitoring data using a CEEMDAN algorithm to obtain a plurality of subsequences, each of which includes a plurality of intrinsic mode functions (IMFs) and a residual sequence;

[0058] a subsequence division processing module, configured to divide the obtained subsequence into a training set and a test set, and perform standardization processing on the data in the training set and the test set;

[0059] A Transformer neural network model construction module, which is used to construct a Transformer neural network model and configure neural network training parameters and neural network structure for the Transformer neural network model;

[0060] A Transformer neural network model training module is used to train the Transformer neural network model using the standardized training set to obtain the trained Transformer neural network model;

[0061] A Transformer neural network model testing module is used to test the trained Transformer neural network model using the standardized test set;

[0062] A forecast result aggregation and reconstruction module, used to aggregate and reconstruct the forecast results of all the test sets to obtain the forecast results of the motion response monitoring data;

[0063] The forecast result quantitative evaluation module is used to conduct a comprehensive quantitative evaluation of the forecast results of the motion response of the offshore floating platform, so as to realize the forecast of the motion response of the offshore floating platform.

[0064] As a preferred solution of the offshore platform motion response prediction device based on CEEMDAN-Transformer, in the motion response monitoring data processing module:

[0065] Acquire motion response monitoring data of offshore floating platforms through differential global positioning system and motion reference unit system;

[0066] The pre-processing method for the motion response monitoring data includes filtering to remove abnormal values ​​and noise.

[0067] As a preferred solution of the offshore platform motion response prediction device based on CEEMDAN-Transformer, the monitoring data empirical mode decomposition module includes:

[0068] Operator definition submodule, used to define operator E m (·) Perform m-order empirical mode decomposition on the signal; add multiple Gaussian white noises w that obey the standard normal distribution N(0,1) to the original motion response signal Y(t) i (t), get the motion response signal Y i (t):

[0069] Y i (t)=Y(t)+ε i w i (t),i=1,2,…I

[0070] Where, ε i is the noise coefficient, which is used to determine the signal-to-noise ratio of each step; I is the number of Gaussian white noise added;

[0071] The first IMF definition submodule is used to obtain multiple motion response signals Y i (t) Carry out empirical mode decomposition to obtain multiple subsequences Define the first IMF as:

[0072]

[0073] The first residual signal R1(t) is:

[0074]

[0075] Where, is the first intrinsic mode function obtained by the CEEMDAN algorithm;

[0076] The second IMF definition submodule is used to decompose the result R1(t)+ε1E1(w i (t)) signal is subjected to empirical mode decomposition, and the second IMF is obtained as:

[0077]

[0078] Where, is the second eigenmode function obtained by the CEEMDAN algorithm; is the intrinsic mode function of the second subsequence obtained by decomposing multiple Gaussian white noises into the residual R1(t);

[0079] The k-th IMF definition submodule is used to obtain the k-th IMF. The obtained k-th IMF is:

[0080]

[0081] Where, is the kth eigenmode function obtained by the CEEMDAN algorithm; is the residual R k-1 (t) After adding multiple Gaussian white noises, the second subsequence intrinsic mode function obtained by each decomposition; R k-1 (t) is the k-1th residual signal left after decomposition; ε k-1 is the noise coefficient of the k-1th step; E k-1 (w i (t)) is the k-1 order empirical mode decomposition of the added Gaussian white noise;

[0082] The extreme value judgment analysis submodule is used to judge the k-th order residual signal R k (t) whether it contains at least two extreme values. If it does, continue to decompose and get the k+1th IMF; if it does not contain at least two extreme values, the decomposition process ends; finally, the time series signal Y(t) is decomposed into all subsequence IMFs that meet the conditions and the residual R representing the signal trend K (t) and.

[0083] As a preferred solution of the offshore platform motion response prediction device based on CEEMDAN-Transformer, in the subsequence division processing module:

[0084] The formula for normalizing the data in the training set and the test set is:

[0085]

[0086] Where y i is the motion monitoring value of the offshore floating platform, y norm is the result after standardization, y max is the maximum value of the input data, y min is the minimum value of the input data.

[0087] As a preferred solution of the offshore platform motion response prediction device based on CEEMDAN-Transformer, in the Transformer neural network model construction module:

[0088] Performing neural network training parameter configuration on the Transformer neural network model including historical time step, prediction time step, data batch size, and number of training rounds;

[0089] The neural network structure configuration objects of the Transformer neural network model include embedding layer, multi-head attention layer, normalization layer, feedforward layer, residual connection layer, and linearization layer; Softmax is used to calculate the probability distribution of data, MSE is selected as the training loss function, and Adam optimizer is selected to update the neural network parameters.

[0090] As a preferred solution for the offshore platform motion response prediction device based on CEEMDAN-Transformer, the Transformer neural network model training module includes:

[0091] The vector generation submodule is used to input the data in the training set into the Transformer neural network model to generate the corresponding query vector Q, key vector K and value vector V:

[0092]

[0093] Where W Q 、W K and W V are the weight matrices for query, key, and value respectively, and X is the input data;

[0094] The attention score calculation submodule is used to calculate the attention score using the dot product of the query vector Q and the key vector K:

[0095]

[0096] Where Attention(Q,K,V) is the attention score of the input data; d k is the dimension of the key vector K;

[0097] Multi-head attention matrix calculation submodule, used to calculate the multi-head attention matrix:

[0098]

[0099] MultiHead(Q,K,V)=(head1,head2,…,head h )W O

[0100] In the formula, head i is the i-th attention weight; Corresponding to the weight matrix of the i-th Q, K, V, W O is the final attention matrix after linear transformation; MultiHead(Q,K,V) is the multi-head attention score of the input data.

[0101] As a preferred solution for the offshore platform motion response prediction device based on CEEMDAN-Transformer, the prediction result summary and reconstruction module generates multiple query vectors Q, key vectors K and value vectors V, and calculates the attention scores and weighted sums respectively; the results of all attention weights are spliced ​​together and linearly transformed to obtain the final output, thereby obtaining the trained Transformer neural network.

[0102] As a preferred solution of the offshore platform motion response prediction device based on CEEMDAN-Transformer, the evaluation indicators selected in the prediction result quantitative evaluation module are the root mean square error, mean absolute error and determination coefficient of the motion;

[0103] The formula for the root mean square error RMSE is:

[0104]

[0105] The formula for mean absolute error (MAE) is:

[0106]

[0107] Coefficient of determination R 2 The formula is:

[0108]

[0109] Where y i is the platform motion monitoring value, is the platform motion prediction value, is the average value of the platform motion monitoring value; n is the number of motion data points.

[0110] The present invention has the following advantages: obtaining the motion response monitoring data of an offshore floating platform, pre-processing the data to remove outliers and noise; applying the CEEMDAN algorithm to decompose the pre-processed motion response data to obtain multiple subsequences including: multiple intrinsic mode functions (IMFs) and a residual sequence. Each decomposed subsequence is divided into a training set and a test set, and standardized, and a Transformer neural network is constructed respectively, and the relevant structural parameters of the Transformer neural network are set to carry out the prediction of the subsequence to obtain a trained neural network model; the test set of all subsequences is used to test the Transformer neural network; the prediction results of all subsequences are reconstructed to obtain the prediction results of the original motion response, and then a comprehensive quantitative evaluation is carried out on the prediction results of the motion response of the offshore floating platform to realize the prediction of the motion response of the offshore floating platform. The present invention realizes the accurate prediction of the motion response of the offshore floating platform and can provide an auxiliary reference for on-site decision-making. BRIEF DESCRIPTION OF THE DRAWINGS

[0111] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for the embodiments or the description of the prior art. Obviously, the drawings described below are merely exemplary, and those skilled in the art can, without inventive effort, derive other implementation drawings based on the provided drawings.

[0112] Figure 1 A schematic flow chart of a method for predicting motion response of an offshore platform based on CEEMDAN-Transformer provided in an embodiment of the present invention;

[0113] Figure 2 Schematic diagram of the technical architecture of the offshore platform motion response prediction method based on CEEMDAN-Transformer provided in an embodiment of the present invention;

[0114] Figure 3 A comparison diagram of the IMF5 monitoring value and the predicted value of the offshore floating platform motion response subsequence provided in an embodiment of the present invention;

[0115] Figure 4 A comparison diagram of the monitored and predicted values ​​of the IMF6 subsequence of motion response of an offshore floating platform provided in an embodiment of the present invention;

[0116] Figure 5 A comparison diagram of the reconstructed motion response monitoring value and the predicted value of the offshore floating platform provided in an embodiment of the present invention;

[0117] Figure 6 A comparison chart of the reconstructed offshore floating platform motion response monitoring value provided in an embodiment of the present invention and the predicted value using the traditional LSTM method;

[0118] Figure 7 This is a schematic diagram of the architecture of an offshore platform motion response prediction device based on CEEMDAN-Transformer provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0119] The following describes the implementation of the present invention using specific embodiments. Those skilled in the art will readily understand the other advantages and benefits of the present invention from the disclosure herein. Obviously, the embodiments described are only a portion of the present invention, not all of it. All other embodiments derived by persons of ordinary skill in the art based on the embodiments of the present invention without inventive effort are intended to fall within the scope of protection of the present invention.

[0120] Example 1

[0121] See also Figure 1 and Figure 2 The embodiment of the present invention provides a method for predicting the motion response of an offshore platform based on CEEMDAN-Transformer, comprising the following steps:

[0122] S1. Acquire motion response monitoring data of an offshore floating platform and preprocess the motion response monitoring data;

[0123] S2. Performing empirical mode decomposition on the pre-processed motion response monitoring data using a CEEMDAN algorithm to obtain a plurality of subsequences, each of which includes a plurality of intrinsic mode functions (IMFs) and a residual sequence;

[0124] S3, dividing the obtained subsequence into a training set and a test set, and performing standardization processing on the data in the training set and the test set;

[0125] S4, constructing a Transformer neural network model, configuring neural network training parameters and neural network structure for the Transformer neural network model; training the Transformer neural network model using the standardized training set to obtain the trained Transformer neural network model;

[0126] S5. Testing the trained Transformer neural network model using the standardized test set, summarizing and reconstructing the prediction results of all the test sets to obtain the prediction results of the motion response monitoring data;

[0127] S6. Conduct comprehensive quantitative evaluation on the prediction results of the motion response of the offshore floating platform to achieve the prediction of the motion response of the offshore floating platform.

[0128] In this embodiment, in step S1, motion response monitoring data of an offshore floating platform is obtained through a differential global positioning system and a motion reference unit system. The implementation process uses sway motion response data obtained by monitoring an offshore floating platform for three hours from 5:35 to 8:35 on September 1, 2023, with a data sampling frequency of 1 Hz and a total of 10,800 data points.

[0129] The pre-processing method for the motion response monitoring data includes filtering to remove abnormal values ​​and noise. There are related technologies for filtering and removing abnormal values ​​and noise, which will not be described in detail here.

[0130] In this embodiment, in step S2, the steps of performing empirical mode decomposition on the pre-processed motion response monitoring data using the CEEMDAN algorithm to obtain a plurality of subsequences include:

[0131] a1) Define operator E m (·) Perform m-order empirical mode decomposition on the signal; add multiple Gaussian white noises w that obey the standard normal distribution N(0,1) to the original motion response signal Y(t) i (t), get the motion response signal Y i (t):

[0132] Y i (t)=Y(t)+ε i w i (t),i=1,2,…I

[0133] Where, ε i is the noise coefficient, which is used to determine the signal-to-noise ratio of each step; I is the number of Gaussian white noise added;

[0134] Among them, Gaussian white noise is a random process with zero mean, constant variance and uniform distribution of power spectrum density. The amplitude is an independent random variable at any time, and its probability density function P(a) can be expressed as:

[0135]

[0136] Where: a is a random variable, indicating the amplitude of the noise; σ 2 is the variance of the noise, which represents the power of the noise.

[0137] In order to generate Gaussian white noise in practice, a normally distributed random number generator can be used. Specifically, the generation method is based on the Box-Muller transformation method, and the formula is as follows:

[0138]

[0139]

[0140] Where u1 and u2 are uniformly distributed random numbers in the interval [0,1]. The Box-Muller transformation is used to generate standard normally distributed random numbers z1 and z2: z1 and z2 are random variables with standard normally distribution and are two independent samples of Gaussian white noise. By changing the values ​​of u1 and u2, different Gaussian white noise can be obtained.

[0141] a2) Calculating the obtained multiple motion response signals Y i (t) Carry out empirical mode decomposition to obtain multiple subsequences Define the first IMF as:

[0142]

[0143] The first residual signal R1(t) is:

[0144]

[0145] Where, is the first intrinsic mode function obtained by the CEEMDAN algorithm;

[0146] a3) For the decomposition result R1(t)+ε1E1(w i (t)) signal is subjected to empirical mode decomposition, and the second IMF is obtained as:

[0147]

[0148] Where, is the second eigenmode function obtained by the CEEMDAN algorithm; is the intrinsic mode function of the second subsequence obtained by decomposing multiple Gaussian white noises into the residual R1(t);

[0149] a4) Repeat step a3) to obtain the kth IMF:

[0150]

[0151] Where, is the kth eigenmode function obtained by the CEEMDAN algorithm; is the residual R k-1 (t) After adding multiple Gaussian white noises, the second subsequence intrinsic mode function obtained by each decomposition; R k-1 (t) is the k-1th residual signal left after decomposition; ε k-1 is the noise coefficient of the k-1th step; E k-1 (w i (t)) is the k-1 order empirical mode decomposition of the added Gaussian white noise;

[0152] a5) Determine the k-th order residual signal R k (t) whether it contains at least two extreme values. If it does, continue to decompose and repeat step a3) to obtain the k+1th IMF; if it does not contain at least two extreme values, the decomposition process ends; finally, the time series signal Y(t) is decomposed into all subsequence IMFs that meet the conditions and the residual R representing the signal trend K (t) Sum:

[0153]

[0154] Finally, the motion response monitoring data is decomposed into 9 IMF subsequences and 1 residual subsequence.

[0155] In this embodiment, in step S3, the formula for normalizing the data in the training set and the test set is:

[0156]

[0157] Where y i is the motion monitoring value of the offshore floating platform, y norm is the result after standardization, y max is the maximum value of the input data, y min The first 80% of the data is divided into the training set, and the last 20% of the data is divided into the test set for training the Transformer neural network model.

[0158] In this embodiment, in step S4, the neural network training parameter configuration objects for the Transformer neural network model include the historical time step, the prediction time step, the data batch size and the number of training rounds;

[0159] Among them, the neural network training parameters are set as shown in Table 1:

[0160] Table 1 Neural network training parameters set

[0161]

[0162] In step S4, the neural network structure configuration objects of the Transformer neural network model include an embedding layer, a multi-head attention layer, a normalization layer, a feedforward layer, a residual connection layer, and a linearization layer; Softmax is used to calculate the probability distribution of the data, MSE is selected as the training loss function, and Adam optimizer is selected to update the neural network parameters.

[0163] Among them, the neural network structure parameters are set as shown in Table 2:

[0164] Table 2 Neural network structure parameters set

[0165]

[0166] Among them, the loss function MSE formula selected is as follows:

[0167]

[0168] Where: y i is the platform motion monitoring value, is the platform motion prediction value.

[0169] In this embodiment, in step S5, the step of training the Transformer neural network model using the standardized training set to obtain the trained Transformer neural network model includes:

[0170] b1) Input the data in the training set into the Transformer neural network model to generate the corresponding query vector Q, key vector K and value vector V:

[0171]

[0172] Where W Q 、W K and W V are the weight matrices for query, key, and value respectively, and X is the input data;

[0173] b2) Calculate the attention score using the dot product of the query vector Q and the key vector K:

[0174]

[0175] Where Attention(Q,K,V) is the attention score of the input data; d k is the dimension of the key vector K;

[0176] b3) Calculate the multi-head attention matrix:

[0177]

[0178] MultiHead(Q,K,V)=(head1,head2,…,head h )W O

[0179] In the formula, head i is the i-th attention weight; Corresponding to the weight matrix of the i-th Q, K, V, W O is the final attention matrix after linear transformation; MultiHead(Q,K,V) is the multi-head attention score of the input data;

[0180] b4) Repeat steps b1), b2), and b3) to generate multiple query vectors Q, key vectors K, and value vectors V, and calculate the attention scores and weighted sums respectively; concatenate the results of all attention weights and perform a linear transformation to obtain the final output, thus obtaining the trained Transformer neural network.

[0181] In this embodiment, in step S5, the test set obtained in S3 is used to test the Transformer neural network obtained in S4, and the prediction results of all subsequences are reconstructed to obtain the prediction results of the original motion response.

[0182] Specifically, the standardized subsequence test set obtained in step S3 is used to test the neural network obtained in step S4. The prediction results of each subsequence test set are obtained, such as Figure 3 、 Figure 4 The following are comparisons of the monitoring and prediction values ​​of the offshore floating platform motion response subsequences IMF5 and IMF6, where the black curve is the subsequence value obtained by decomposing the offshore floating platform motion response, and the red curve is the subsequence prediction value of the method provided by the present invention. The prediction results of all subsequence test sets are summarized, the prediction results of the original motion response signal are reconstructed, and the relevant evaluation indicators are calculated to achieve the prediction of the offshore floating platform motion response. Among them, the prediction results of the original monitoring motion response are as follows: Figure 5 As shown, the black curve is the monitoring value of the motion response of the offshore floating platform, and the red curve is the predicted value of the method provided by the present invention.

[0183] In this embodiment, in step S6, in the process of realizing the prediction of the motion response of the offshore floating platform, the evaluation indicators selected are the root mean square error, mean absolute error and determination coefficient of the motion;

[0184] The formula for the root mean square error RMSE is:

[0185]

[0186] The formula for mean absolute error (MAE) is:

[0187]

[0188] Coefficient of determination R 2 The formula is:

[0189]

[0190] Where y i is the platform motion monitoring value, is the platform motion prediction value, is the average value of the platform motion monitoring value; n is the number of motion data points.

[0191] Specifically, RMSE and MAE can better evaluate the error level of the prediction results. The smaller the value, the better the model prediction performance. 2 The fitting effect between the prediction results and the original data can be better evaluated. The value is distributed between [0,1]. The larger the value, the better the model performance. For comparison, the LSTM neural network is selected to carry out the prediction work on the same offshore floating platform motion response monitoring value. The results are as follows Figure 6 As shown in Figure 3, the black curve is the monitoring value of the offshore floating platform motion response, and the blue curve is the prediction value of the LSTM neural network. The prediction performance of the two methods is shown in Table 3:

[0192] Table 3 Prediction performance of the two methods

[0193]

[0194] The results obtained from the examples show that the prediction error of the method of the present invention is significantly smaller than that of the LSTM neural network, and the determination coefficient R 2 The larger the value, the better the fitting effect of the motion response of the floating platform, and the better the nonlinear characteristics of the monitoring data. It verifies that the method of the present invention has a significant improvement effect on the prediction accuracy of the motion response of the offshore floating platform.

[0195] In summary, the present invention obtains motion response monitoring data of an offshore floating platform, preprocesses the motion response monitoring data, performs empirical mode decomposition on the preprocessed motion response monitoring data using the CEEMDAN algorithm, and obtains several subsequences, each of which includes multiple intrinsic mode functions (IMFs) and a residual sequence. The obtained subsequences are divided into a training set and a test set, and the data in the training set and the test set are standardized. A Transformer neural network model is constructed, and neural network training parameters and a neural network structure are configured for the Transformer neural network model. The Transformer neural network model is trained using the standardized training set to obtain the trained Transformer neural network model. The trained Transformer neural network model is tested using the standardized test set, and the prediction results of all the test sets are summarized and reconstructed to obtain the prediction results of the motion response monitoring data. A comprehensive quantitative evaluation is performed on the prediction results of the motion response of the offshore floating platform to achieve the prediction of the motion response of the offshore floating platform. The present invention achieves accurate prediction of the motion response of the offshore floating platform and can provide auxiliary reference for on-site decision-making.

[0196] Example 2

[0197] See also Figure 7 Embodiment 2 of the present invention further provides a CEEMDAN-Transformer-based offshore platform motion response prediction device, comprising:

[0198] The motion response monitoring data processing module 100 is used to obtain the motion response monitoring data of the offshore floating platform and pre-process the motion response monitoring data;

[0199] The monitoring data empirical mode decomposition module 200 is used to perform empirical mode decomposition on the pre-processed motion response monitoring data using the CEEMDAN algorithm to obtain a plurality of subsequences, each of which includes a plurality of intrinsic mode functions (IMFs) and a residual sequence.

[0200] A subsequence division processing module 300 is used to divide the obtained subsequence into a training set and a test set, and perform standardization processing on the data in the training set and the test set;

[0201] A Transformer neural network model construction module 400 is used to construct a Transformer neural network model and configure neural network training parameters and neural network structure for the Transformer neural network model;

[0202] A Transformer neural network model training module 500 is configured to train the Transformer neural network model using the standardized training set to obtain the trained Transformer neural network model;

[0203] A Transformer neural network model testing module 600 is used to test the trained Transformer neural network model using the standardized test set;

[0204] A forecast result aggregation and reconstruction module 700 is used to aggregate and reconstruct the forecast results of all the test sets to obtain the forecast results of the motion response monitoring data;

[0205] The prediction result quantitative evaluation module 800 is used to perform comprehensive quantitative evaluation on the prediction results of the motion response of the offshore floating platform, so as to realize the prediction of the motion response of the offshore floating platform.

[0206] In this embodiment, in the motion response monitoring data processing module 100:

[0207] Acquire motion response monitoring data of offshore floating platforms through differential global positioning system and motion reference unit system;

[0208] The pre-processing method for the motion response monitoring data includes filtering to remove abnormal values ​​and noise.

[0209] In this embodiment, the monitoring data empirical mode decomposition module 200 includes:

[0210] Operator definition submodule 201, used to define operator E m(·) Perform m-order empirical mode decomposition on the signal; add multiple Gaussian white noises w that obey the standard normal distribution N(0,1) to the original motion response signal Y(t) i (t), get the motion response signal Y i (t):

[0211] Y i (t)=Y(t)+ε i w i (t),i=1,2,…I

[0212] Where, ε i is the noise coefficient, which is used to determine the signal-to-noise ratio of each step; I is the number of Gaussian white noise added;

[0213] The first IMF definition submodule 202 is used to obtain multiple motion response signals Y i (t) Carry out empirical mode decomposition to obtain multiple subsequences Define the first IMF as:

[0214]

[0215] The first residual signal R1(t) is:

[0216]

[0217] Where, is the first intrinsic mode function obtained by the CEEMDAN algorithm;

[0218] The second IMF definition submodule 203 is used to define the decomposition result R1(t)+ε1E1(w i (t)) signal is subjected to empirical mode decomposition, and the second IMF is obtained as:

[0219]

[0220] Where, is the second eigenmode function obtained by the CEEMDAN algorithm; is the intrinsic mode function of the second subsequence obtained by decomposing multiple Gaussian white noises into the residual R1(t);

[0221] The k-th IMF definition submodule 204 is used to obtain the k-th IMF. The obtained k-th IMF is:

[0222]

[0223] Where, is the kth eigenmode function obtained by the CEEMDAN algorithm; is the residual Rk-1 (t) After adding multiple Gaussian white noises, the second subsequence intrinsic mode function obtained by each decomposition; R k-1 (t) is the k-1th residual signal left after decomposition; ε k-1 is the noise coefficient of the k-1th step; E k-1 (w i (t)) is the k-1 order empirical mode decomposition of the added Gaussian white noise;

[0224] The extreme value judgment and analysis submodule 205 is used to judge the k-th order residual signal R k (t) whether it contains at least two extreme values. If it does, continue to decompose and get the k+1th IMF; if it does not contain at least two extreme values, the decomposition process ends; finally, the time series signal Y(t) is decomposed into all subsequence IMFs that meet the conditions and the residual R representing the signal trend K (t) and.

[0225] In this embodiment, in the subsequence division processing module 300:

[0226] The formula for normalizing the data in the training set and the test set is:

[0227]

[0228] Where y i is the motion monitoring value of the offshore floating platform, y norm is the result after standardization, y max is the maximum value of the input data, y min is the minimum value of the input data.

[0229] In this embodiment, in the Transformer neural network model construction module 400:

[0230] Performing neural network training parameter configuration on the Transformer neural network model including historical time step, prediction time step, data batch size, and number of training rounds;

[0231] The neural network structure configuration objects of the Transformer neural network model include embedding layer, multi-head attention layer, normalization layer, feedforward layer, residual connection layer, and linearization layer; Softmax is used to calculate the probability distribution of data, MSE is selected as the training loss function, and Adam optimizer is selected to update the neural network parameters.

[0232] In this embodiment, the Transformer neural network model training module 500 includes:

[0233] The vector generation submodule 501 is used to input the data in the training set into the Transformer neural network model to generate the corresponding query vector Q, key vector K and value vector V:

[0234]

[0235] Where W Q 、W K and W V are the weight matrices for query, key, and value respectively, and X is the input data;

[0236] The attention score calculation submodule 502 is used to calculate the attention score using the dot product of the query vector Q and the key vector K:

[0237]

[0238] Where Attention(Q,K,V) is the attention score of the input data; d k is the dimension of the key vector K;

[0239] The multi-head attention matrix calculation submodule 503 is used to calculate the multi-head attention matrix:

[0240]

[0241] MultiHead(Q,K,V)=(head1,head2,…,head h )W O

[0242] In the formula, head i is the i-th attention weight; Corresponding to the weight matrix of the i-th Q, K, V, W O is the final attention matrix after linear transformation; MultiHead(Q,K,V) is the multi-head attention score of the input data.

[0243] In this embodiment, in the forecast result summary and reconstruction module 700, multiple query vectors Q, key vectors K and value vectors V are generated, and the attention scores and weighted sums are calculated respectively; the results of all attention weights are spliced ​​together and linearly transformed to obtain the final output, thereby obtaining the trained Transformer neural network.

[0244] In this embodiment, in the forecast result quantitative evaluation module 800, the evaluation indicators selected are the root mean square error, mean absolute error and determination coefficient of the motion;

[0245] The formula for the root mean square error RMSE is:

[0246]

[0247] The formula for mean absolute error (MAE) is:

[0248]

[0249] Coefficient of determination R 2 The formula is:

[0250]

[0251] Where y i is the platform motion monitoring value, is the platform motion prediction value, is the average value of the platform motion monitoring value; n is the number of motion data points.

[0252] It should be noted that the information interaction, execution process, etc. between the modules of the above-mentioned device are based on the same concept as the method embodiment in Example 1 of the present application, and the technical effects they bring are the same as those of the method embodiment of the present application. For specific contents, please refer to the description in the method embodiment shown above in the present application, and will not be repeated here.

[0253] Example 3

[0254] Embodiment 3 of the present invention provides a non-transitory computer-readable storage medium, in which the program code of the offshore platform motion response prediction method based on CEEMDAN-Transformer is stored. The program code includes instructions for executing the offshore platform motion response prediction method based on CEEMDAN-Transformer of embodiment 1 or any possible implementation thereof.

[0255] Computer-readable storage media can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that includes one or more available media. The available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media (e.g., solid-state drives (SSDs)).

[0256] Example 4

[0257] Embodiment 4 of the present invention provides an electronic device, including: a memory and a processor;

[0258] The processor and the memory communicate with each other via a bus; the memory stores program instructions that can be executed by the processor, and the processor calls the program instructions to execute the offshore platform motion response prediction method based on CEEMDAN-Transformer of Example 1 or any possible implementation thereof.

[0259] Specifically, the processor can be implemented by hardware or by software. When implemented by hardware, the processor can be a logic circuit, an integrated circuit, etc.; when implemented by software, the processor can be a general-purpose processor, which is implemented by reading software code stored in a memory. The memory can be integrated into the processor or located outside the processor and exist independently.

[0260] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from a website, computer, server or data center to another website, computer, server or data center via a wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode.

[0261] Obviously, those skilled in the art will appreciate that the various modules or steps of the present invention described above can be implemented using a general-purpose computing device, centralized on a single computing device, or distributed across a network of multiple computing devices. Alternatively, they can be implemented using program code executable by a computing device, which can then be stored in a storage device and executed by the computing device. In some cases, the steps shown or described can be performed in a different order than that shown, or can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Thus, the present invention is not limited to any particular combination of hardware and software.

[0262] Although the present invention has been described in detail above using general descriptions and specific embodiments, it will be apparent to those skilled in the art that modifications and improvements may be made thereto. Therefore, such modifications and improvements, without departing from the spirit of the present invention, are intended to be within the scope of protection claimed herein.

Claims

1. The offshore platform motion response prediction method based on CEEMDAN-Transformer is characterized by: include: Acquiring motion response monitoring data of an offshore floating platform and preprocessing the motion response monitoring data; Performing empirical mode decomposition on the pre-processed motion response monitoring data using the CEEMDAN algorithm to obtain a plurality of subsequences, each of which includes a plurality of intrinsic mode functions (IMFs) and a residual sequence; Dividing the obtained subsequences into a training set and a test set, and performing standardization on the data in the training set and the test set; Constructing a Transformer neural network model, and configuring neural network training parameters and neural network structure for the Transformer neural network model; training the Transformer neural network model using the standardized training set to obtain the trained Transformer neural network model; Testing the trained Transformer neural network model using the standardized test set, summarizing and reconstructing the prediction results of all the test sets to obtain the prediction results of the motion response monitoring data; Conduct comprehensive quantitative evaluation on the prediction results of motion response of offshore floating platforms to realize the prediction of motion response of offshore floating platforms.

2. The offshore platform motion response prediction method based on CEEMDAN-Transformer according to claim 1 is characterized in that: Acquire motion response monitoring data of offshore floating platforms through differential global positioning system and motion reference unit system; The pre-processing method for the motion response monitoring data includes filtering to remove abnormal values ​​and noise.

3. The offshore platform motion response prediction method based on CEEMDAN-Transformer according to claim 1 is characterized in that: The steps of performing empirical mode decomposition on the pre-processed motion response monitoring data using the CEEMDAN algorithm to obtain a plurality of subsequences include: a1) Define operator E m (·) Perform m-order empirical mode decomposition on the signal; add multiple Gaussian white noises w that obey the standard normal distribution N(0,1) to the original motion response signal Y(t) i (t), get the motion response signal Y i (t): Y i (t)=Y(t)+ε i w i (t),i=1,2,…I Where, ε i is the noise coefficient, which is used to determine the signal-to-noise ratio of each step of Gaussian white noise; I is the number of Gaussian white noise added; a2) Calculating the obtained multiple motion response signals Y i (t) Carry out empirical mode decomposition to obtain multiple subsequences Define the first IMF as: The first residual signal R1(t) is: Where, is the first intrinsic mode function obtained by the CEEMDAN algorithm; a3) For the decomposition result R1(t)+ε1E1(w i (t)) signal is subjected to empirical mode decomposition, and the second IMF is obtained as: Where, is the second eigenmode function obtained by the CEEMDAN algorithm; is the intrinsic mode function of the second subsequence obtained by decomposing multiple Gaussian white noises into the residual R1(t); a4) Repeat step a3) to obtain the kth IMF: Where, is the kth eigenmode function obtained by the CEEMDAN algorithm; is the residual R k-1 (t) After adding multiple Gaussian white noises, the second subsequence intrinsic mode function obtained by each decomposition; R k-1 (t) is the k-1th residual signal left after decomposition; ε k-1 is the noise coefficient of the k-1th step; E k-1 (w i (t)) is the k-1 order empirical mode decomposition of the added Gaussian white noise; a5) Determine the k-th order residual signal R k (t) whether it contains at least two extreme values. If it does, continue to decompose and repeat step a3) to obtain the k+1th IMF; if it does not contain at least two extreme values, the decomposition process ends; finally, the time series signal Y(t) is decomposed into all subsequence IMFs that meet the conditions and the residual R representing the signal trend K (t) and.

4. The offshore platform motion response prediction method based on CEEMDAN-Transformer according to claim 1 is characterized in that: The formula for normalizing the data in the training set and the test set is: Where y i is the motion monitoring value of the offshore floating platform, y norm is the result after standardization, y max is the maximum value of the input data, y min is the minimum value of the input data.

5. The offshore platform motion response prediction method based on CEEMDAN-Transformer according to claim 1 is characterized in that: Performing neural network training parameter configuration on the Transformer neural network model including historical time step, prediction time step, data batch size, and number of training rounds; The neural network structure configuration objects of the Transformer neural network model include embedding layer, multi-head attention layer, normalization layer, feedforward layer, residual connection layer, and linearization layer; Softmax is used to calculate the probability distribution of data, MSE is selected as the training loss function, and Adam optimizer is selected to update the neural network parameters.

6. The offshore platform motion response prediction method based on CEEMDAN-Transformer according to claim 5 is characterized in that: The step of training the Transformer neural network model using the standardized training set to obtain the trained Transformer neural network model includes: b1) Input the data in the training set into the Transformer neural network model to generate the corresponding query vector Q, key vector K and value vector V: Where W Q 、W K and W V are the weight matrices for query, key, and value respectively, and X is the input data; b2) Calculate the attention score using the dot product of the query vector Q and the key vector K: Where Attention(Q,K,V) is the attention score of the input data; d k is the dimension of the key vector K; b3) Calculate the multi-head attention matrix: MultiHead(Q,K,V)=(head1,head2,…,head h )W O Where, head i is the i-th attention weight; Corresponding to the weight matrix of the i-th Q, K, V, W O is the final attention matrix after linear transformation; MultiHead(Q,K,V) is the multi-head attention score of the input data; b4) Repeat steps b1), b2), and b3) to generate multiple query vectors Q, key vectors K, and value vectors V, and calculate the attention scores and weighted sums respectively; concatenate the results of all attention weights and perform a linear transformation to obtain the final output, thus obtaining the trained Transformer neural network.

7. The offshore platform motion response prediction method based on CEEMDAN-Transformer according to claim 6 is characterized in that: The prediction results of the motion response of the offshore floating platform are comprehensively and quantitatively evaluated. In the process of predicting the motion response of the offshore floating platform, the evaluation indicators selected are the root mean square error, mean absolute error and determination coefficient of the motion; The formula for the root mean square error RMSE is: The formula for mean absolute error (MAE) is: Coefficient of determination R 2 The formula is: Where y i is the platform motion monitoring value, is the platform motion prediction value, is the average value of the platform motion monitoring value; n is the number of motion data points.

8. The offshore platform motion response prediction device based on CEEMDAN-Transformer is characterized by: include: A motion response monitoring data processing module is used to obtain motion response monitoring data of an offshore floating platform and pre-process the motion response monitoring data; A monitoring data empirical mode decomposition module is used to perform empirical mode decomposition on the pre-processed motion response monitoring data using a CEEMDAN algorithm to obtain a plurality of subsequences, each of which includes a plurality of intrinsic mode functions (IMFs) and a residual sequence; a subsequence division processing module, configured to divide the obtained subsequence into a training set and a test set, and perform standardization processing on the data in the training set and the test set; A Transformer neural network model construction module, which is used to construct a Transformer neural network model and configure neural network training parameters and neural network structure for the Transformer neural network model; A Transformer neural network model training module is used to train the Transformer neural network model using the standardized training set to obtain the trained Transformer neural network model; A Transformer neural network model testing module is used to test the trained Transformer neural network model using the standardized test set; A forecast result aggregation and reconstruction module, used to aggregate and reconstruct the forecast results of all the test sets to obtain the forecast results of the motion response monitoring data; The forecast result quantitative evaluation module is used to conduct a comprehensive quantitative evaluation of the forecast results of the motion response of the offshore floating platform, so as to realize the forecast of the motion response of the offshore floating platform.

9. The offshore platform motion response prediction device based on CEEMDAN-Transformer according to claim 8, characterized in that: In the motion response monitoring data processing module: Acquire motion response monitoring data of offshore floating platforms through differential global positioning system and motion reference unit system; The motion response monitoring data is preprocessed by filtering to remove abnormal values ​​and noise; The monitoring data empirical mode decomposition module includes: Operator definition submodule, used to define operator E m (·) Perform m-order empirical mode decomposition on the signal; add multiple Gaussian white noises w that obey the standard normal distribution N(0,1) to the original motion response signal Y(t) i (t), get the motion response signal Y i (t): Y i (t)=Y(t)+ε i w i (t),i=1,2,…I Where, ε i is the noise coefficient, which is used to determine the signal-to-noise ratio of each step; I is the number of Gaussian white noise added; The first IMF definition submodule is used to obtain multiple motion response signals Y i (t) Carry out empirical mode decomposition to obtain multiple subsequences Define the first IMF as: The first residual signal R1(t) is: Where, is the first intrinsic mode function obtained by the CEEMDAN algorithm; The second IMF definition submodule is used to decompose the result R1(t)+ε1E1(w i (t)) signal is subjected to empirical mode decomposition, and the second IMF is obtained as: Where, is the second eigenmode function obtained by the CEEMDAN algorithm; is the intrinsic mode function of the second subsequence obtained by decomposing multiple Gaussian white noises into the residual R1(t); The k-th IMF definition submodule is used to obtain the k-th IMF. The obtained k-th IMF is: Where, is the kth eigenmode function obtained by the CEEMDAN algorithm; is the residual R k-1 (t) After adding multiple Gaussian white noises, the second subsequence intrinsic mode function obtained by each decomposition; R k-1 (t) is the k-1th residual signal left after decomposition; ε k-1 is the noise coefficient of the k-1th step; E k-1 (w i (t)) is the k-1 order empirical mode decomposition of the added Gaussian white noise; The extreme value judgment analysis submodule is used to judge the k-th order residual signal R k (t) whether it contains at least two extreme values. If it does, continue to decompose and get the k+1th IMF; if it does not contain at least two extreme values, the decomposition process ends; finally, the time series signal Y(t) is decomposed into all subsequence IMFs that meet the conditions and the residual R representing the signal trend K (t) the sum; In the subsequence division processing module: The formula for normalizing the data in the training set and the test set is: Where y i is the motion monitoring value of the offshore floating platform, y norm is the result after standardization, y max is the maximum value of the input data, y min is the minimum value of the input data; In the Transformer neural network model building module: Performing neural network training parameter configuration on the Transformer neural network model including historical time step, prediction time step, data batch size, and number of training rounds; The Transformer neural network model is configured with a neural network structure including an embedding layer, a multi-head attention layer, a normalization layer, a feedforward layer, a residual connection layer, and a linearization layer; Softmax is used to calculate the probability distribution of the data, MSE is selected as the training loss function, and Adam optimizer is selected to update the neural network parameters; The Transformer neural network model training module includes: The vector generation submodule is used to input the data in the training set into the Transformer neural network model to generate the corresponding query vector Q, key vector K and value vector V: Where W Q 、W K and W V are the weight matrices for query, key, and value respectively, and X is the input data; The attention score calculation submodule is used to calculate the attention score using the dot product of the query vector Q and the key vector K: Where Attention(Q,K,V) is the attention score of the input data; d k is the dimension of the key vector K; Multi-head attention matrix calculation submodule, used to calculate the multi-head attention matrix: MultiHead(Q,K,V)=(head1,head2,…,head h )W O Where, head i is the i-th attention weight; Corresponding to the weight matrix of the i-th Q, K, V, W O is the final attention matrix after linear transformation; MultiHead(Q,K,V) is the multi-head attention score of the input data; In the forecast result summary and reconstruction module, multiple query vectors Q, key vectors K and value vectors V are generated, and the attention scores and weighted sums are calculated respectively; the results of all attention weights are concatenated and linearly transformed to obtain the final output, thereby obtaining the trained Transformer neural network.

10. The offshore platform motion response prediction device based on CEEMDAN-Transformer according to claim 8, characterized in that: In the forecast result quantitative evaluation module, the evaluation indicators selected are the root mean square error, mean absolute error and determination coefficient of motion; The formula for the root mean square error RMSE is: The formula for mean absolute error (MAE) is: Coefficient of determination R 2 The formula is: Where y i is the platform motion monitoring value, is the platform motion prediction value, is the average value of the platform motion monitoring value; n is the number of motion data points.

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