Submarine cable extrusion pressure prediction method and device and computer equipment

Abstract

The invention relates to a submarine cable extrusion pressure prediction method and device and computer equipment. The method comprises the following steps: collecting historical extruder parameter information and test sample set information of a submarine cable extruder, identifying correlation information between the submarine cable extruder and insulation melt pressure so as to adjust to obtain a parameter optimization model, and based on the correlation information, through a parameter optimization algorithm and the parameter optimization model, calculating the parameter of the submarine cable extruder according to the parameter optimization model. Screening target parameter combination information; initial sample extrusion pressure information of the submarine cable extruder is generated through an extrusion pressure prediction model, and based on the initial sample extrusion pressure information, a target extrusion pressure prediction model is obtained through adjustment through an extrusion pressure evaluation strategy; and obtaining current parameter data of the target parameter combination information, and predicting target extrusion pressure information of the submarine cable extruder. By adopting the method, the prediction accuracy of the submarine cable extrusion pressure can be comprehensively improved.

Description

Technical Field

[0001] The present application relates to the technical field of binary code file homology analysis, and in particular to a method, device and computer equipment for predicting submarine cable extrusion pressure. Background Art

[0002] With the transformation of the global energy structure, the development and utilization of renewable energy has received increasing attention. As a clean and sustainable form of energy, offshore wind power occupies an important position in the current energy market. Since offshore wind farms are mostly located in sea areas far away from the coastline, higher requirements are placed on the power transmission system. In order to achieve long-distance and efficient power transmission, submarine cables, as the key link connecting offshore wind farms with land power grids, must have sufficient length and corresponding high voltage levels. In the manufacturing process of submarine cables, extrusion process control is a crucial link. In order to improve the overall stability and reliability of submarine cables, a manufacturing process that can adapt to long-distance continuous extrusion must be adopted to reduce the number of submarine cable joints. In addition, the optimization of the extrusion process is crucial to ensure the uniformity and roundness of the insulation layer, which directly affects the transmission efficiency and safety performance of the submarine cable. Therefore, how to improve the accurate prediction of submarine cable extrusion pressure is the current research focus.

[0003] The traditional method of predicting the submarine cable extrusion pressure is through manual prediction, but the manual prediction is slow and has a high error rate, resulting in low prediction accuracy of the submarine cable extrusion pressure. Summary of the invention

[0004] Based on this, it is necessary to provide a method, device, computer equipment, computer-readable storage medium and computer program product for predicting the extrusion pressure of a submarine cable in order to solve the above technical problems.

[0005] In a first aspect, the present application provides a method for predicting submarine cable extrusion pressure, comprising:

[0006] Collecting historical extruder parameter information and test sample set information of a submarine cable extruder, and identifying correlation information between the submarine cable extruder and insulation melting pressure based on the historical extruder parameter information;

[0007] Based on the correlation information, an initial parameter optimization model is adjusted to obtain a parameter optimization model, and based on the correlation information, target parameter combination information of the submarine cable extruder is screened through a parameter optimization algorithm and the parameter optimization model;

[0008] Based on the target parameter combination information, the test sample set information, and the extrusion pressure prediction model, initial sample extrusion pressure information of the submarine cable extruder is generated, and based on the initial sample extrusion pressure information, the extrusion pressure prediction model is adjusted through an extrusion pressure evaluation strategy to obtain a target extrusion pressure prediction model;

[0009] The current parameter data of the target parameter combination information are obtained, and based on the current parameter data, the target extrusion pressure information of the submarine cable extruder is predicted by the target extrusion pressure prediction model.

[0010] Optionally, the identifying, based on the historical extruder parameter information, the correlation information between the submarine cable extruder and the insulation melting pressure includes:

[0011] Splitting the historical extruder parameter information into historical parameter sets of each parameter type and each historical insulation melting pressure data;

[0012] For each parameter type, based on the historical parameter set of the parameter type and the historical insulation melting pressure data, the correlation coefficient between the parameter type and the insulation melting pressure is calculated through the insulation melting pressure correlation algorithm, and the target parameter type greater than the correlation coefficient threshold is screened as the correlation information between the submarine cable extruder and the insulation melting pressure.

[0013] Optionally, adjusting the initial parameter optimization model based on the correlation information to obtain the parameter optimization model includes:

[0014] Obtain the initial parameter optimization model;

[0015] Based on the historical parameter sets of each target parameter type and each historical insulation melting pressure data, the initial parameter optimization model is trained to obtain a parameter optimization model.

[0016] Optionally, the screening of target parameter combination information of the submarine cable extruder based on the correlation information through a parameter optimization algorithm and the parameter optimization model includes:

[0017] Based on the historical parameter set of each target parameter type and the parameter optimization model, in each target parameter type, each initial target parameter information of each target parameter type is screened;

[0018] Through the parameter optimization algorithm, the target parameter information of each target parameter type is screened in each initial target parameter information of each target parameter type, and the target parameter information of all target parameter types is used as the target parameter combination information of the submarine cable extruder.

[0019] Optionally, adjusting the extrusion pressure prediction model based on the initial sample extrusion pressure information through an extrusion pressure evaluation strategy to obtain a target extrusion pressure prediction model includes:

[0020] Based on the initial sample extrusion pressure information, identifying the predicted value of each sample point, and based on the test sample set information, identifying the true value of each sample point;

[0021] Based on the predicted value of each sample point and the true value of each sample point, the evaluation index value of each evaluation index type is calculated by each evaluation algorithm, and the evaluation index value of each evaluation index type is used as the extrusion pressure evaluation value of the initial sample extrusion pressure information;

[0022] When the extrusion pressure evaluation value is lower than a preset evaluation value threshold, adjusting the extrusion pressure prediction model based on the extrusion pressure evaluation value of the initial sample extrusion pressure information to obtain a new extrusion pressure prediction model;

[0023] The new extrusion pressure prediction model replaces the extrusion pressure prediction model, and the step of generating the initial sample extrusion pressure information of the submarine cable extruder based on the target parameter combination information, the test sample set information, and the extrusion pressure prediction model is returned to execution, until the extrusion pressure evaluation value is lower than the preset evaluation value threshold, and the new extrusion pressure prediction model obtained in the last iteration is used as the target extrusion pressure prediction model.

[0024] Optionally, predicting the target extrusion pressure information of the submarine cable extruder through the target extrusion pressure prediction model based on each of the current parameter data includes:

[0025] Identify the target parameter type corresponding to each current parameter data;

[0026] In the target extrusion pressure prediction model, the input data location information corresponding to each target parameter type is identified, and the current parameter data of each target parameter type is input into the input data location information corresponding to each target parameter type in the target extrusion pressure prediction model to predict the target extrusion pressure information of the submarine cable extruder.

[0027] In a second aspect, the present application also provides a device for predicting the extrusion pressure of a submarine cable, comprising:

[0028] A collection module, used to collect historical extruder parameter information and test sample set information of a submarine cable extruder, and identify correlation information between the submarine cable extruder and insulation melting pressure based on the historical extruder parameter information;

[0029] A screening module, used to adjust the initial parameter optimization model based on the correlation information to obtain the parameter optimization model, and based on the correlation information, through the parameter optimization algorithm and the parameter optimization model, screen the target parameter combination information of the submarine cable extruder;

[0030] An adjustment module, configured to generate initial sample extrusion pressure information of the submarine cable extruder based on the target parameter combination information, the test sample set information, and an extrusion pressure prediction model, and to adjust the extrusion pressure prediction model based on the initial sample extrusion pressure information through an extrusion pressure evaluation strategy to obtain a target extrusion pressure prediction model;

[0031] The prediction module is used to obtain each current parameter data of the target parameter combination information, and based on each current parameter data, predict the target extrusion pressure information of the submarine cable extruder through the target extrusion pressure prediction model.

[0032] Optionally, the acquisition module is specifically used to:

[0033] Splitting the historical extruder parameter information into historical parameter sets of each parameter type and each historical insulation melting pressure data;

[0034] For each parameter type, based on the historical parameter set of the parameter type and the historical insulation melting pressure data, the correlation coefficient between the parameter type and the insulation melting pressure is calculated through the insulation melting pressure correlation algorithm, and the target parameter type greater than the correlation coefficient threshold is screened as the correlation information between the submarine cable extruder and the insulation melting pressure.

[0035] Optionally, the screening module is specifically used for:

[0036] Obtain the initial parameter optimization model;

[0037] Based on the historical parameter sets of each target parameter type and each historical insulation melting pressure data, the initial parameter optimization model is trained to obtain a parameter optimization model.

[0038] Optionally, the screening module is specifically used to:

[0039] Based on the historical parameter set of each target parameter type and the parameter optimization model, in each target parameter type, each initial target parameter information of each target parameter type is screened;

[0040] Through the parameter optimization algorithm, the target parameter information of each target parameter type is screened in each initial target parameter information of each target parameter type, and the target parameter information of all target parameter types is used as the target parameter combination information of the submarine cable extruder.

[0041] Optionally, the adjustment module is specifically used to:

[0042] Based on the initial sample extrusion pressure information, identifying the predicted value of each sample point, and based on the test sample set information, identifying the true value of each sample point;

[0043] Based on the predicted value of each sample point and the true value of each sample point, the evaluation index value of each evaluation index type is calculated by each evaluation algorithm, and the evaluation index value of each evaluation index type is used as the extrusion pressure evaluation value of the initial sample extrusion pressure information;

[0044] When the extrusion pressure evaluation value is lower than a preset evaluation value threshold, adjusting the extrusion pressure prediction model based on the extrusion pressure evaluation value of the initial sample extrusion pressure information to obtain a new extrusion pressure prediction model;

[0045] The new extrusion pressure prediction model replaces the extrusion pressure prediction model, and the step of generating the initial sample extrusion pressure information of the submarine cable extruder based on the target parameter combination information, the test sample set information, and the extrusion pressure prediction model is returned to execution, until the extrusion pressure evaluation value is lower than the preset evaluation value threshold, and the new extrusion pressure prediction model obtained in the last iteration is used as the target extrusion pressure prediction model.

[0046] Optionally, the prediction module is specifically used to:

[0047] Identify the target parameter type corresponding to each current parameter data;

[0048] In the target extrusion pressure prediction model, the input data location information corresponding to each target parameter type is identified, and the current parameter data of each target parameter type is input into the input data location information corresponding to each target parameter type in the target extrusion pressure prediction model to predict the target extrusion pressure information of the submarine cable extruder.

[0049] In a third aspect, the present application provides a computer device, wherein the computer device comprises a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of any one of the methods in the first aspect are implemented.

[0050] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the steps of any one of the methods in the first aspect are implemented.

[0051] In a fifth aspect, the present application provides a computer program product. The computer program product includes a computer program, and when the computer program is executed by a processor, the steps of any one of the methods in the first aspect are implemented.

[0052] The above-mentioned method, device and computer equipment for predicting the extrusion pressure of the submarine cable collect the historical extruder parameter information and test sample set information of the submarine cable extruder, and identify the correlation information between the submarine cable extruder and the insulation melting pressure based on the historical extruder parameter information; based on the correlation information, adjust the initial parameter optimization model to obtain the parameter optimization model, and based on the correlation information, screen the target parameter combination information of the submarine cable extruder through the parameter optimization algorithm and the parameter optimization model; based on the target parameter combination information, the test sample set information, and the extrusion pressure prediction model, generate the initial sample extrusion pressure information of the submarine cable extruder, and based on the initial sample extrusion pressure information, adjust the extrusion pressure prediction model through the extrusion pressure evaluation strategy to obtain the target extrusion pressure prediction model; obtain each current parameter data of the target parameter combination information, and based on each current parameter data, predict the target extrusion pressure information of the submarine cable extruder through the target extrusion pressure prediction model. This solution identifies the historical extruder parameters and the correlation information between them and the insulation melting pressure, and then uses a parameter optimization model that includes a parameter optimization algorithm to screen the target parameter combination information from each historical extruder parameter, thereby significantly improving the accuracy of the extrusion pressure prediction of long-length submarine cables and reducing the uncertainty in production. Then, based on the target parameter combination information, the solution optimizes the extrusion pressure prediction model by using the historical extruder parameter information of the submarine cable extruder and the test sample set information, thereby effectively improving the prediction accuracy of the extrusion pressure prediction model for the submarine cable extruder. Finally, based on the current parameter data of the screened target parameter combination information and the trained target extrusion pressure prediction model, the target extrusion pressure information of the submarine cable extruder is predicted, thereby comprehensively improving the prediction accuracy of the submarine cable extrusion pressure. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related technologies, the drawings required for use in the embodiments or the related technical descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0054] Figure 1 It is a schematic flow chart of a method for predicting the extrusion pressure of a submarine cable in one embodiment;

[0055] Figure 2 A schematic diagram of the structure of a BiLSTM network in one embodiment;

[0056] Figure 3 A schematic diagram of the structure of an LSTM network in one embodiment;

[0057] Figure 4 is a distribution diagram of the Loss function training results in one embodiment;

[0058] Figure 5 A result comparison diagram of SSA-BiLSTM prediction results in one embodiment;

[0059] Figure 6 A schematic diagram of a flow chart of an example of predicting the extrusion pressure of a submarine cable in one embodiment;

[0060] Figure 7 It is a structural block diagram of a device for predicting the extrusion pressure of a submarine cable in one embodiment;

[0061] Figure 8 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0062] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0063] The prediction method of the submarine cable extrusion pressure provided by the embodiment of the present application can be applied to the prediction application environment of the submarine cable extrusion pressure. Among them, the method can be applied to the terminal, can also be applied to the server, can also be applied to the system including the terminal and the server, and is realized by the interaction between the terminal and the server. Among them, the terminal can be, but not limited to, various personal computers, laptops, etc. Among them, the terminal identifies the historical extruder parameters and the correlation information between the insulation melting pressure, and then through the parameter optimization model containing the parameter optimization algorithm, in each historical extruder parameter, the target parameter combination information is screened, which significantly improves the accuracy of the prediction of the extrusion pressure of the long-length submarine cable and reduces the uncertainty in production. Then, based on the target parameter combination information, the scheme optimizes the model training of the extrusion pressure prediction model by using the historical extruder parameter information of the submarine cable extruder and the test sample set information, thereby effectively improving the prediction accuracy of the extrusion pressure prediction model for the submarine cable extruder, and finally, based on the current parameter data of the screened target parameter combination information and the trained target extrusion pressure prediction model, the target extrusion pressure information of the submarine cable extruder is predicted, thereby comprehensively improving the prediction accuracy of the submarine cable extrusion pressure.

[0064] In an exemplary embodiment, Figure 1 As shown, a method for predicting the extrusion pressure of a submarine cable is provided, and the method is applied to a terminal as an example for explanation, including the following steps S101 to S104. Among them:

[0065] Step S101, collecting historical extruder parameter information and test sample set information of the submarine cable extruder, and identifying the correlation information between the submarine cable extruder and the insulation melting pressure based on the historical extruder parameter information.

[0066] In this embodiment, the terminal obtains parameter data of various parameter types preset by the staff in the historical database of the submarine cable extruder, obtains the initial historical extruder parameter information, and pre-processes the initial historical extruder parameter information to obtain the historical extruder parameter information of the submarine cable extruder, wherein the preset parameter types include but are not limited to production line speed type, insulation melting temperature type, insulation melting pressure type, insulation rotation speed type, inner screen melting temperature type, inner screen melting pressure type, inner screen rotation speed type, outer screen melting temperature type, outer screen melting pressure type, and outer screen rotation speed type. Then, after the data collection is completed, it is necessary to pre-process the collected data, including data cleaning and normalization. The content of data cleaning mainly includes processing missing values, deleting duplicates, processing outliers, etc. Normalization mainly standardizes the data so that it has the same dimension and relative size relationship, which helps to prevent specific features from having too much influence in the model and improve the robustness and accuracy of the model. The min-max normalization is used to normalize the extrusion data, and the specific formula is as follows:

[0067]

[0068] in, is the normalized data, x is the original extrusion data, and x max is the maximum value in the original extrusion data, x min is the minimum value in the original extrusion data.

[0069] Finally, the terminal obtains the data set used to optimize the basic pressure prediction model and obtains the test sample set information.

[0070] The terminal identifies the correlation information between the submarine cable extruder and the insulation melting pressure based on the historical extruder parameter information. The correlation information is a parameter type with a high correlation with the insulation melting pressure. The specific identification process will be described in detail later.

[0071] Step S102, based on the correlation information, adjusting the initial parameter optimization model to obtain the parameter optimization model, and based on the correlation information, through the parameter optimization algorithm and the parameter optimization model, screening the target parameter combination information of the submarine cable extruder.

[0072] In this embodiment, the terminal adjusts the initial parameter optimization model based on the correlation information to obtain the parameter optimization model, and based on the correlation information, screens the target parameter combination information of the submarine cable extruder through the parameter optimization algorithm and the parameter optimization model. Among them, the parameter optimization model and the extrusion pressure prediction model are the SSA module and the BiLSTM module respectively. The SSA module is used to find the best BiLSTM hyperparameter combination, and the BiLSTM module is used to complete the prediction of the insulation melting pressure of long-length and long-time extrusion. The parameter optimization algorithm is the sparrow algorithm. The specific optimization process will be described in detail later.

[0073] Step S103, based on the target parameter combination information, the test sample set information, and the extrusion pressure prediction model, the initial sample extrusion pressure information of the submarine cable extruder is generated, and based on the initial sample extrusion pressure information, the extrusion pressure prediction model is adjusted through the extrusion pressure evaluation strategy to obtain the target extrusion pressure prediction model.

[0074] In this embodiment, the terminal generates the initial sample extrusion pressure information of the submarine cable extruder based on the target parameter combination information, the test sample set information, and the extrusion pressure prediction model, and adjusts the extrusion pressure prediction model based on the initial sample extrusion pressure information through the extrusion pressure evaluation strategy to obtain the target extrusion pressure prediction model. Figure 2 As shown in the figure, the BiLSTM neural network has a bidirectional mechanism of forward propagation LSTM network and backward propagation LSTM network, which helps to comprehensively utilize the time dimension characteristics in the extrusion parameter time series data and improve the accuracy of long-term extrusion pressure prediction of long-length submarine cables. The forward propagation LSTM network predicts the future extrusion pressure through the past extrusion data, while the backward propagation LSTM network fits the past extrusion data through the future extrusion pressure data. The specific optimization process will be explained in detail later. The output of the BiLSTM neural network is as follows:

[0075]

[0076] in, is the hidden state of the forward LSTM at the tth moment, They are the hidden states of the backward LSTM at the tth moment; is the hidden state of the forward LSTM at the t-1th moment, They are the hidden states of the backward LSTM at the t-1th moment; is the output of the BiLSTM neural network; α is the forward factor and β is the backward factor, whose values ​​are determined by the training sample data, and α+β=1; T is the length of the squeezed data time series.

[0077] like Figure 3As shown, the LSTM structure of each layer includes a forget gate, an input gate, an output gate, and a cell state, and its calculation formula is as follows:

[0078]

[0079]

[0080]

[0081]

[0082]

[0083]

[0084] Among them, f t is the output of the forget gate at time t; i t is the output of the input gate at time t, is the cell update state at time t; C t is the cell state at time t; o t is the input of the output gate at time t; h t is the extrusion pressure data output at time t; W f , W t , W C , W o is the weight vector matrix; b f , b t , b C , b o is the offset vector matrix; h t-1 is the predicted value of extrusion pressure output at time t-1, x t is the extrusion data input at time t.

[0085] Step S104, obtaining each current parameter data of the target parameter combination information, and based on each current parameter data, predicting the target extrusion pressure information of the submarine cable extruder through a target extrusion pressure prediction model.

[0086] In this embodiment, the terminal obtains each current parameter data of the target parameter combination information, and based on each current parameter data, predicts the target extrusion pressure information of the submarine cable extruder through a target extrusion pressure prediction model.

[0087] Based on the above scheme, by identifying the historical extruder parameters and the correlation information between the insulation melting pressure, and then using the parameter optimization model containing the parameter optimization algorithm, the target parameter combination information is screened in each historical extruder parameter, which significantly improves the accuracy of the prediction of the extrusion pressure of the long-length submarine cable and reduces the uncertainty in production. Then, based on the target parameter combination information, the scheme optimizes the extrusion pressure prediction model by using the historical extruder parameter information of the submarine cable extruder and the test sample set information, thereby effectively improving the prediction accuracy of the extrusion pressure prediction model for the submarine cable extruder. Finally, based on the current parameter data of the screened target parameter combination information and the trained target extrusion pressure prediction model, the target extrusion pressure information of the submarine cable extruder is predicted, thereby comprehensively improving the prediction accuracy of the submarine cable extrusion pressure.

[0088] Optionally, based on historical extruder parameter information, correlation information between the submarine cable extruder and the insulation melting pressure is identified, including: splitting the historical extruder parameter information into a historical parameter set of each parameter type, and each historical insulation melting pressure data; for each parameter type, based on the historical parameter set of the parameter type, and each historical insulation melting pressure data, using an insulation melting pressure correlation algorithm, calculating the correlation coefficient between the parameter type and the insulation melting pressure, and screening the target parameter type greater than the correlation coefficient threshold as the correlation information between the submarine cable extruder and the insulation melting pressure.

[0089] In this embodiment, the terminal splits the historical extruder parameter information into a historical parameter set of each parameter type and each historical insulation melting pressure data. Then, for each parameter type, the terminal calculates the correlation coefficient between the parameter type and the insulation melting pressure based on the historical parameter set of the parameter type and each historical insulation melting pressure data through the insulation melting pressure correlation algorithm, and selects the target parameter type greater than the correlation coefficient threshold as the correlation information between the submarine cable extruder and the insulation melting pressure. Among them, the insulation melting pressure correlation algorithm is the spearman correlation coefficient, and the calculation formula of the spearman correlation coefficient is as follows:

[0090]

[0091] Among them, R(x i ) is the position of the i-th data point in the sequence x, R(y i ) is the rank of the i-th data point in the sequence y, is the average rank of sequence x, is the average rank of sequence y.

[0092] Based on the above scheme, the parameters with relatively large correlation coefficients are selected as model inputs through the spearman correlation coefficient to predict the insulation melting pressure in order to improve the prediction accuracy.

[0093] Optionally, based on the correlation information, the initial parameter optimization model is adjusted to obtain the parameter optimization model, including: obtaining the initial parameter optimization model; training the initial parameter optimization model based on the historical parameter set of each target parameter type and each historical insulation melting pressure data to obtain the parameter optimization model.

[0094] In this embodiment, the terminal obtains an initial parameter optimization model. Then, the terminal trains the initial parameter optimization model based on the historical parameter set of each target parameter type and each historical insulation melting pressure data to obtain the parameter optimization model.

[0095] Based on the above scheme, the initial parameter optimization model is trained by collecting historical data of the submarine cable extruder, thereby improving the recognition accuracy of the target parameter information set of the submarine cable extruder.

[0096] Optionally, based on the correlation information, the target parameter combination information of the submarine cable extruder is filtered through a parameter optimization algorithm and a parameter optimization model, including: based on the historical parameter set of each target parameter type and a parameter optimization model, in each target parameter type, each initial target parameter information of each target parameter type is filtered; through a parameter optimization algorithm, in each initial target parameter information of each target parameter type, the target parameter information of each target parameter type is filtered, and the target parameter information of all target parameter types is used as the target parameter combination information of the submarine cable extruder.

[0097] In this embodiment, the terminal screens each initial target parameter information of each target parameter type in each target parameter type based on the historical parameter set of each target parameter type and the parameter optimization model. Then, the terminal screens the target parameter information of each target parameter type in each initial target parameter information of each target parameter type through the parameter optimization algorithm, and uses the target parameter information of all target parameter types as the target parameter combination information of the submarine cable extruder. Specifically, the terminal uses the insulation melting temperature, insulation melting pressure, and insulation rotation speed as input data, and the insulation melting pressure as output data, and substitutes them into the SSA module to find the best hyperparameter combination. The sparrow algorithm is a swarm intelligence optimization algorithm proposed by the inspiration of the foraging behavior and anti-predator behavior of sparrows. The algorithm has the advantages of fast convergence speed, strong global search ability and few setting parameters. In the search process, the sparrow population is divided into two categories according to a certain proportion: discoverer (Producer) and follower (Scrounger), in which the discoverer is responsible for adjusting the search direction and range of the entire population, and the follower searches by using the search information of the discoverer. A population composed of n sparrows can be expressed as follows:

[0098]

[0099] Among them, d represents the dimension of the variable to be optimized, and n is the number of sparrows. Then, the fitness value of all sparrows can be expressed as follows:

[0100]

[0101] Where f is the fitness value. During the search process, the finder with better fitness will get food first and provide foraging directions for other followers. The iterative update formula of the finder's position is:

[0102]

[0103] Where t represents the current iteration number, j=1,2,3,···,α.iter max Is a constant indicating the maximum number of iterations. i Represents the position information of the i-th sparrow in the j-th dimension. α∈(0,1) is a random number. R2(R2∈[0,1]) and ST(ST∈[0.5,1]) represent the warning value and safety value respectively. Q is a random number that follows a normal distribution. L represents a 1×d matrix, in which all elements in the matrix are 1. When R2<ST, it means that there are no predators around, and the discoverer continues to search; when R2≥ST, it means that there are predators around, and all sparrows search for other safe areas. The follower position is updated as follows:

[0104]

[0105] Among them, X p is the optimal position currently occupied by the discoverer, X worst It represents the current global worst position. A represents a 1×d matrix, in which each element is randomly assigned a value of 1 or -1, and A + =A T (AA T ) -1 When i > n / 2, this indicates that the i-th participant with a lower fitness value has not obtained food and is in a very hungry state. At this time, it needs to fly to other places to find food in order to obtain more energy.

[0106] In addition, assuming that the number of sparrows that can detect predators in the entire population is 10% to 20%, the initial positions of these sentinels are randomly generated, expressed as follows:

[0107]

[0108] Among them, β is the step size control parameter, which is a random number that follows the N(0,1) distribution; X best represents the current global optimal position; f irepresents the current individual fitness value, f g 、f w Indicates the current global best and worst fitness values, f i >f g Indicates that sparrows are on the edge and are most threatened; i =f g It means that individuals are aware of danger and move closer to other sparrows to reduce their risk of being preyed upon; ε is the minimum constant to avoid zero-point error; K is the direction in which the sparrow moves.

[0109] Based on the above solution, the parameter information is optimized through the sparrow algorithm, which improves the accuracy of the selected target parameter combination.

[0110] Optionally, based on the initial sample extrusion pressure information, the extrusion pressure prediction model is adjusted through the extrusion pressure evaluation strategy to obtain a target extrusion pressure prediction model, including: based on the initial sample extrusion pressure information, the predicted value of each sample point is identified, and based on the test sample set information, the true value of each sample point is identified; based on the predicted value of each sample point and the true value of each sample point, the evaluation index value of each evaluation index type is calculated through each evaluation algorithm, and the evaluation index value of each evaluation index type is used as the extrusion pressure evaluation value of the initial sample extrusion pressure information; when the extrusion pressure evaluation value is lower than the preset evaluation value threshold, the extrusion pressure prediction model is adjusted based on the extrusion pressure evaluation value of the initial sample extrusion pressure information to obtain a new extrusion pressure prediction model; the new extrusion pressure prediction model is replaced by the extrusion pressure prediction model, and the step of generating the initial sample extrusion pressure information of the submarine cable extruder based on the target parameter combination information, the test sample set information, and the extrusion pressure prediction model is returned to execute, until the extrusion pressure evaluation value is lower than the preset evaluation value threshold, the new extrusion pressure prediction model obtained in the last iteration is used as the target extrusion pressure prediction model.

[0111] In this embodiment, the terminal identifies the predicted value of each sample point based on the initial sample extrusion pressure information, and identifies the true value of each sample point based on the test sample set information. The true value of each sample point is the historical extrusion pressure value corresponding to each historical moment. The sample point is a historical moment point. The mean square error (MSE) is selected as the loss function in the extrusion pressure prediction model, and the calculation formula of MSE is as follows:

[0112]

[0113] in, is the predicted value of the i-th sample point, is the true value of the i-th sample point, and n is the number of sample points. Figure 4 This is the loss function training result of SSA-BiLSTM.

[0114] Then, the terminal calculates the evaluation index value of each evaluation index type based on the predicted value of each sample point and the true value of each sample point through each evaluation algorithm, and uses the evaluation index value of each evaluation index type as the extrusion pressure evaluation value of the initial sample extrusion pressure information.

[0115] Specifically, the test set samples are input into the trained model for prediction. Figure 5 is the prediction result of SSA-BiLSTM. Select root mean square error (RMSE), mean absolute percentage error (MAPE), goodness of fit (R 2 ) are used as evaluation indicators of prediction results, RMSE, MAPE and R 2 The calculation expression is as follows:

[0116]

[0117]

[0118]

[0119] in, is the predicted value of the i-th sample point, is the true value of the i-th sample point, is the average value of the actual values ​​of all sample points, and n is the number of sample points.

[0120] Then, when the extrusion pressure evaluation value is lower than the preset evaluation value threshold, the terminal adjusts the extrusion pressure prediction model based on the extrusion pressure evaluation value of the initial sample extrusion pressure information to obtain a new extrusion pressure prediction model. After that, the terminal replaces the extrusion pressure prediction model with the new extrusion pressure prediction model, and returns to execute the step of generating the initial sample extrusion pressure information of the submarine cable extruder based on the target parameter combination information, the test sample set information, and the extrusion pressure prediction model, until the extrusion pressure evaluation value is lower than the preset evaluation value threshold, and the new extrusion pressure prediction model obtained by the last iteration is used as the target extrusion pressure prediction model.

[0121] Based on the above scheme, the extrusion pressure prediction model was adjusted through evaluation and optimization, thereby improving the prediction accuracy of the extrusion pressure prediction model.

[0122] Optionally, based on each current parameter data, the target extrusion pressure information of the submarine cable extruder is predicted through a target extrusion pressure prediction model, including: identifying the target parameter type corresponding to each current parameter data; in the target extrusion pressure prediction model, identifying the input data position information corresponding to each target parameter type, and inputting the current parameter data of each target parameter type into the input data position information corresponding to each target parameter type in the target extrusion pressure prediction model to predict the target extrusion pressure information of the submarine cable extruder.

[0123] In this embodiment, the terminal identifies the target parameter type corresponding to each current parameter data. Then, in the target extrusion pressure prediction model, the terminal identifies the input data location information corresponding to each target parameter type, and inputs the current parameter data of each target parameter type into the input data location information corresponding to each target parameter type in the target extrusion pressure prediction model to predict the target extrusion pressure information of the submarine cable extruder. Figure 5 The figure shows the prediction result of SSA-BiLSTM. That is, the terminal optimizes the SSA module based on the sparrow algorithm and optimizes the BiLSTM module, which effectively improves the prediction accuracy of the target extrusion pressure information of the submarine cable extruder.

[0124] Based on the above scheme, the target extrusion pressure information of the submarine cable extruder is predicted by using the current parameter data based on the screened target parameter combination information and the trained target extrusion pressure prediction model, thereby comprehensively improving the prediction accuracy of the submarine cable extrusion pressure.

[0125] This application also provides an example of predicting the extrusion pressure of a submarine cable, such as Figure 6 As shown, the specific processing process includes the following steps:

[0126] Step S601, collecting historical extruder parameter information and test sample set information of the submarine cable extruder.

[0127] Step S602, splitting the historical extruder parameter information into historical parameter sets of each parameter type and each historical insulation melting pressure data.

[0128] Step S603, for each parameter type, based on the historical parameter set of the parameter type and the historical insulation melting pressure data, the insulation melting pressure correlation algorithm is used to calculate the correlation coefficient between the parameter type and the insulation melting pressure, and the target parameter type greater than the correlation coefficient threshold is selected as the correlation information between the submarine cable extruder and the insulation melting pressure.

[0129] Step S604, obtaining an initial parameter optimization model.

[0130] Step S605 , based on the historical parameter sets of each target parameter type and each historical insulation melting pressure data, an initial parameter optimization model is trained to obtain a parameter optimization model.

[0131] Step S606 , based on the historical parameter set of each target parameter type and the parameter optimization model, the initial target parameter information of each target parameter type is screened in each target parameter type.

[0132] Step S607, using a parameter optimization algorithm, select target parameter information of each target parameter type from each initial target parameter information of each target parameter type, and use the target parameter information of all target parameter types as target parameter combination information of the submarine cable extruder.

[0133] Step S608: generating initial sample extrusion pressure information of the submarine cable extruder based on the target parameter combination information, the test sample set information, and the extrusion pressure prediction model.

[0134] Step S609, based on the initial sample extrusion pressure information, identify the predicted value of each sample point, and based on the test sample set information, identify the true value of each sample point.

[0135] Step S610, based on the predicted value of each sample point and the true value of each sample point, the evaluation index value of each evaluation index type is calculated through each evaluation algorithm, and the evaluation index value of each evaluation index type is used as the extrusion pressure evaluation value of the initial sample extrusion pressure information.

[0136] Step S611, when the extrusion pressure evaluation value is lower than the preset evaluation value threshold, the extrusion pressure prediction model is adjusted based on the extrusion pressure evaluation value of the initial sample extrusion pressure information to obtain a new extrusion pressure prediction model.

[0137] Step S612, replace the extrusion pressure prediction model with the new extrusion pressure prediction model, and return to execute the step of generating the initial sample extrusion pressure information of the submarine cable extruder based on the target parameter combination information, the test sample set information, and the extrusion pressure prediction model, until the extrusion pressure evaluation value is lower than the preset evaluation value threshold, and then use the new extrusion pressure prediction model obtained by the last iteration as the target extrusion pressure prediction model.

[0138] Step S613, identifying the target parameter type corresponding to each current parameter data.

[0139] Step S614, in the target extrusion pressure prediction model, identify the input data location information corresponding to each target parameter type, and input the current parameter data of each target parameter type into the input data location information corresponding to each target parameter type in the target extrusion pressure prediction model to predict the target extrusion pressure information of the submarine cable extruder.

[0140] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0141] Based on the same inventive concept, the embodiment of the present application also provides a prediction device for submarine cable extrusion pressure for implementing the above-mentioned prediction method for submarine cable extrusion pressure. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above-mentioned method, so the specific limitations in the embodiments of one or more submarine cable extrusion pressure prediction devices provided below can refer to the limitations of the submarine cable extrusion pressure prediction method above, and will not be repeated here.

[0142] In an exemplary embodiment, Figure 7 As shown, a prediction device for submarine cable extrusion pressure is provided, comprising: a collection module 710, a screening module 720, an adjustment module 730 and a prediction module 740, wherein:

[0143] The acquisition module 710 is used to acquire historical extruder parameter information and test sample set information of the submarine cable extruder, and identify the correlation information between the submarine cable extruder and the insulation melting pressure based on the historical extruder parameter information;

[0144] A screening module 720 is used to adjust the initial parameter optimization model based on the correlation information to obtain the parameter optimization model, and screen the target parameter combination information of the submarine cable extruder through the parameter optimization algorithm and the parameter optimization model based on the correlation information;

[0145] An adjustment module 730 is used to generate initial sample extrusion pressure information of the submarine cable extruder based on the target parameter combination information, the test sample set information, and the extrusion pressure prediction model, and adjust the extrusion pressure prediction model based on the initial sample extrusion pressure information through an extrusion pressure evaluation strategy to obtain a target extrusion pressure prediction model;

[0146] The prediction module 740 is used to obtain each current parameter data of the target parameter combination information, and based on each current parameter data, predict the target extrusion pressure information of the submarine cable extruder through the target extrusion pressure prediction model.

[0147] Optionally, the acquisition module 710 is specifically used for:

[0148] Splitting the historical extruder parameter information into historical parameter sets of each parameter type and each historical insulation melting pressure data;

[0149] For each parameter type, based on the historical parameter set of the parameter type and the historical insulation melting pressure data, the correlation coefficient between the parameter type and the insulation melting pressure is calculated through the insulation melting pressure correlation algorithm, and the target parameter type greater than the correlation coefficient threshold is screened as the correlation information between the submarine cable extruder and the insulation melting pressure.

[0150] Optionally, the screening module 720 is specifically used for:

[0151] Obtain the initial parameter optimization model;

[0152] Based on the historical parameter sets of each target parameter type and each historical insulation melting pressure data, the initial parameter optimization model is trained to obtain a parameter optimization model.

[0153] Optionally, the screening module 720 is specifically used for:

[0154] Based on the historical parameter set of each target parameter type and the parameter optimization model, in each target parameter type, each initial target parameter information of each target parameter type is screened;

[0155] Through the parameter optimization algorithm, the target parameter information of each target parameter type is screened in each initial target parameter information of each target parameter type, and the target parameter information of all target parameter types is used as the target parameter combination information of the submarine cable extruder.

[0156] Optionally, the adjustment module 730 is specifically configured to:

[0157] Based on the initial sample extrusion pressure information, identifying the predicted value of each sample point, and based on the test sample set information, identifying the true value of each sample point;

[0158] Based on the predicted value of each sample point and the true value of each sample point, the evaluation index value of each evaluation index type is calculated by each evaluation algorithm, and the evaluation index value of each evaluation index type is used as the extrusion pressure evaluation value of the initial sample extrusion pressure information;

[0159] When the extrusion pressure evaluation value is lower than a preset evaluation value threshold, adjusting the extrusion pressure prediction model based on the extrusion pressure evaluation value of the initial sample extrusion pressure information to obtain a new extrusion pressure prediction model;

[0160] The new extrusion pressure prediction model replaces the extrusion pressure prediction model, and the step of generating the initial sample extrusion pressure information of the submarine cable extruder based on the target parameter combination information, the test sample set information, and the extrusion pressure prediction model is returned to execution, until the extrusion pressure evaluation value is lower than the preset evaluation value threshold, and the new extrusion pressure prediction model obtained in the last iteration is used as the target extrusion pressure prediction model.

[0161] Optionally, the prediction module 740 is specifically used to:

[0162] Identify the target parameter type corresponding to each current parameter data;

[0163] In the target extrusion pressure prediction model, the input data location information corresponding to each target parameter type is identified, and the current parameter data of each target parameter type is input into the input data location information corresponding to each target parameter type in the target extrusion pressure prediction model to predict the target extrusion pressure information of the submarine cable extruder.

[0164] Each module in the above-mentioned prediction device for submarine cable extrusion pressure can be implemented in whole or in part by software, hardware and a combination thereof. Each of the above-mentioned modules can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute the operations corresponding to each of the above modules.

[0165] In an exemplary embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as shown in FIG. Figure 8As shown. The computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit and an input device. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface, the display unit and the input device are connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and the external device. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be realized through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, a method for predicting the extrusion pressure of a submarine cable is realized. The display unit of the computer device is used to form a visually visible picture, which can be a display screen, a projection device or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad set on the computer device shell, or an external keyboard, touchpad or mouse.

[0166] Those skilled in the art will understand that Figure 8 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0167] In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps of a method for predicting submarine cable extrusion pressure when executing the computer program.

[0168] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of the method for predicting the extrusion pressure of a submarine cable are implemented.

[0169] In one embodiment, a computer program product is provided, comprising a computer program, which implements the steps of a method for predicting submarine cable extrusion pressure when executed by a processor.

[0170] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0171] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0172] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0173] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A method for predicting the extrusion pressure of a submarine cable, characterized in that: The method comprises: Collecting historical extruder parameter information and test sample set information of a submarine cable extruder, and identifying correlation information between the submarine cable extruder and insulation melting pressure based on the historical extruder parameter information; Based on the correlation information, an initial parameter optimization model is adjusted to obtain a parameter optimization model, and based on the correlation information, target parameter combination information of the submarine cable extruder is screened through a parameter optimization algorithm and the parameter optimization model; Based on the target parameter combination information, the test sample set information, and the extrusion pressure prediction model, initial sample extrusion pressure information of the submarine cable extruder is generated, and based on the initial sample extrusion pressure information, the extrusion pressure prediction model is adjusted through an extrusion pressure evaluation strategy to obtain a target extrusion pressure prediction model; The current parameter data of the target parameter combination information are obtained, and based on the current parameter data, the target extrusion pressure information of the submarine cable extruder is predicted by the target extrusion pressure prediction model.

2. The method according to claim 1, characterized in that The identifying, based on the historical extruder parameter information, the correlation information between the submarine cable extruder and the insulation melting pressure comprises: Splitting the historical extruder parameter information into historical parameter sets of each parameter type and each historical insulation melting pressure data; For each parameter type, based on the historical parameter set of the parameter type and the historical insulation melting pressure data, the correlation coefficient between the parameter type and the insulation melting pressure is calculated through the insulation melting pressure correlation algorithm, and the target parameter type greater than the correlation coefficient threshold is screened as the correlation information between the submarine cable extruder and the insulation melting pressure.

3. The method according to claim 2, characterized in that The step of adjusting the initial parameter optimization model based on the correlation information to obtain the parameter optimization model includes: Obtain the initial parameter optimization model; Based on the historical parameter sets of each target parameter type and each historical insulation melting pressure data, the initial parameter optimization model is trained to obtain a parameter optimization model.

4. The method according to claim 2, characterized in that: The method of screening target parameter combination information of the submarine cable extruder based on the correlation information through a parameter optimization algorithm and a parameter optimization model includes: Based on the historical parameter set of each target parameter type and the parameter optimization model, in each target parameter type, each initial target parameter information of each target parameter type is screened; Through the parameter optimization algorithm, the target parameter information of each target parameter type is screened in each initial target parameter information of each target parameter type, and the target parameter information of all target parameter types is used as the target parameter combination information of the submarine cable extruder.

5. The method according to claim 1, characterized in that The method of adjusting the extrusion pressure prediction model based on the initial sample extrusion pressure information and obtaining a target extrusion pressure prediction model through an extrusion pressure evaluation strategy includes: Based on the initial sample extrusion pressure information, identifying the predicted value of each sample point, and based on the test sample set information, identifying the true value of each sample point; Based on the predicted value of each sample point and the true value of each sample point, the evaluation index value of each evaluation index type is calculated by each evaluation algorithm, and the evaluation index value of each evaluation index type is used as the extrusion pressure evaluation value of the initial sample extrusion pressure information; When the extrusion pressure evaluation value is lower than a preset evaluation value threshold, adjusting the extrusion pressure prediction model based on the extrusion pressure evaluation value of the initial sample extrusion pressure information to obtain a new extrusion pressure prediction model; The new extrusion pressure prediction model replaces the extrusion pressure prediction model, and the step of generating the initial sample extrusion pressure information of the submarine cable extruder based on the target parameter combination information, the test sample set information, and the extrusion pressure prediction model is returned to execution, until the extrusion pressure evaluation value is lower than the preset evaluation value threshold, and the new extrusion pressure prediction model obtained in the last iteration is used as the target extrusion pressure prediction model.

6. The method according to claim 5, characterized in that The method of predicting the target extrusion pressure information of the submarine cable extruder based on each of the current parameter data by using the target extrusion pressure prediction model includes: Identify the target parameter type corresponding to each current parameter data; In the target extrusion pressure prediction model, the input data location information corresponding to each target parameter type is identified, and the current parameter data of each target parameter type is input into the input data location information corresponding to each target parameter type in the target extrusion pressure prediction model to predict the target extrusion pressure information of the submarine cable extruder.

7. A device for predicting the extrusion pressure of a submarine cable, characterized in that: The device comprises: A collection module, used to collect historical extruder parameter information and test sample set information of a submarine cable extruder, and identify correlation information between the submarine cable extruder and insulation melting pressure based on the historical extruder parameter information; A screening module, used to adjust the initial parameter optimization model based on the correlation information to obtain the parameter optimization model, and based on the correlation information, through the parameter optimization algorithm and the parameter optimization model, screen the target parameter combination information of the submarine cable extruder; An adjustment module, configured to generate initial sample extrusion pressure information of the submarine cable extruder based on the target parameter combination information, the test sample set information, and an extrusion pressure prediction model, and to adjust the extrusion pressure prediction model based on the initial sample extrusion pressure information through an extrusion pressure evaluation strategy to obtain a target extrusion pressure prediction model; The prediction module is used to obtain each current parameter data of the target parameter combination information, and based on each current parameter data, predict the target extrusion pressure information of the submarine cable extruder through the target extrusion pressure prediction model.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

Images