Method and device for predicting joint core temperature data of cable joint

Abstract

The invention relates to a joint core temperature data prediction method and device of a cable joint. The method comprises the following steps: acquiring joint structure data of a cable joint and historical surface temperature data of a cable, and constructing a thermal circuit model of the cable joint; based on the historical surface temperature data and the thermal circuit model, simulating to obtain a cable core temperature corresponding to each modal temperature simulation strategy, and based on the historical cable surface temperature corresponding to each modal and the cable core temperature corresponding to each modal, identifying each target position point of the cable and a radial inversion data set of the cable joint; based on the radial inversion data set, training a radial inversion model and an axial inversion model to obtain a cascade inversion model; and on the basis of the current surface temperature data of each target position point, joint core temperature data of the cable joint are predicted through a cascade inversion model. By adopting the method, the calculation accuracy of the joint wire core temperature data of the cable joint can be improved.

Description

Technical Field

[0001] The present application relates to the technical field of power cables, and in particular to a method and device for predicting connector wire core temperature data of a cable connector. Background Art

[0002] The cable core temperature is a key parameter to measure the operating status of the cable. Excessive temperature may lead to cable aging, failure, and even fire. Due to many challenges in the manufacturing and installation of cables, such as complex process flow, material inhomogeneity, and concentrated electrical stress, the intermediate joints are more prone to insulation failure under the combined action of electrical and thermal stress, becoming the most vulnerable part of the entire cable system. In order to ensure the trouble-free operation of the power transmission and distribution network, it is crucial to accurately calculate the temperature of the cable joints.

[0003] At present, the main technical means for simulating the temperature field of the cable body and the intermediate joints include analytical methods and numerical calculation methods. However, the above calculation methods can only predict the approximate cable joint core temperature from the cable surface temperature, and are easily affected by the environment, resulting in poor calculation accuracy of the cable joint core temperature data. 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 connector core temperature data of a cable connector in response to the above technical problems.

[0005] In a first aspect, the present application provides a method for predicting the temperature data of a cable connector core. The method comprises:

[0006] Acquire joint structure data of the cable joint and various historical surface temperature data of the cable, and construct a thermal circuit model of the cable joint based on the joint structure data;

[0007] Based on each of the historical surface temperature data and the thermal circuit model, different modal temperature simulation strategies are used to simulate the wire core temperature corresponding to each of the modal temperature simulation strategies, and based on the historical cable surface temperature corresponding to each of the modes and the wire core temperature corresponding to each of the modes, each target position point of the cable and the radial inversion data set of the cable joint are identified;

[0008] Based on the radial inversion data set, an initial radial inversion model is trained to obtain a radial inversion model, and based on each of the historical surface temperature data and the radial inversion model, predicted core temperature data corresponding to each of the historical surface temperature data is generated;

[0009] Based on each of the historical surface temperature data and the predicted core temperature data corresponding to each of the historical surface temperature data, an initial axial inversion model is trained to obtain an axial inversion model, and the radial inversion model and the axial inversion model are used as a cascade inversion model;

[0010] The current surface temperature data of each of the target position points is obtained, and based on the current surface temperature data of each of the target position points, the joint wire core temperature data of the cable joint is predicted through the cascade inversion model.

[0011] Optionally, constructing a thermal circuit model of the cable joint based on the joint structure data includes:

[0012] Dividing the joint structure data into radial structure data and axial structure data, and dividing the radial structure data into segment structure data of each preset joint segment and joint connection structure data, and constructing radial thermal resistance branches of each preset joint segment based on each segment structure data;

[0013] Based on the connector connection structure data, construct each radial branch between the cable connector and the cable, and divide the axial structure data into cable connector data and cable body data;

[0014] Based on the cable joint data, a cable joint thermal circuit model is constructed, and based on the cable body data, a cable body thermal circuit model is constructed;

[0015] Based on the radial thermal resistance branches and the radial branches, the thermal circuit model of the cable joint is connected with the thermal circuit model of the cable body to obtain the thermal circuit model of the cable joint.

[0016] Optionally, based on each of the historical surface temperature data and the thermal circuit model, simulating and obtaining the wire core temperature corresponding to each of the modal temperature simulation strategies through different modal temperature simulation strategies includes:

[0017] Each historical surface temperature data is split into sub-historical surface temperature data of multiple location points, and simulation parameters of each modal temperature simulation strategy are obtained;

[0018] Adjust the thermal circuit model based on each simulation parameter to obtain a simulated thermal circuit model corresponding to each modal temperature simulation strategy, and input each sub-historical surface temperature data of each historical surface temperature data into each simulated thermal circuit model to obtain sub-core temperature data corresponding to each sub-historical surface temperature data of each historical surface temperature data;

[0019] The sub-core temperature data corresponding to each sub-historical surface temperature data of each historical surface temperature data obtained by simulating each simulated thermal circuit model is used as the core temperature of the modal temperature simulation strategy corresponding to each simulated thermal circuit model.

[0020] Optionally, the identifying each target position point of the cable and the radial inversion data set of the cable joint based on the historical cable surface temperature corresponding to each of the modes and the wire core temperature corresponding to each of the modes includes:

[0021] For each modal temperature simulation strategy, based on the sub-historical surface temperature data of each position point corresponding to the modal temperature simulation strategy and the sub-wire core temperature data corresponding to each position point, the temperature association weight value corresponding to each position point is calculated through the intensity weight algorithm, and the position point corresponding to the temperature association weight value greater than the weight threshold is used as the target position point;

[0022] The sub-historical surface temperature data of each target position point in each of the modal temperature simulation strategies and the sub-core temperature data of each target position point in each of the modal temperature simulation strategies are used as radial inversion data sets of the cable joint.

[0023] Optionally, the training of an initial radial inversion model based on the radial inversion data set to obtain a radial inversion model includes:

[0024] Based on the radial inversion data set, an initial radial inversion model is trained to obtain a candidate radial inversion model, and based on the candidate radial inversion model, predicted sub-core temperature data corresponding to each sub-surface temperature data of each target position point is generated;

[0025] Based on the predicted sub-core temperature data corresponding to each sub-surface temperature data of each target position point, the predicted effect value of the candidate radial inversion model is calculated by a model effect prediction algorithm, and when the predicted effect value does not meet the prediction effect condition, return to execute the step of training the initial radial inversion model based on the radial inversion data set to obtain the candidate radial inversion model until the predicted effect value meets the prediction effect condition;

[0026] The candidate radial inversion model obtained in the last iteration is used as the radial inversion model.

[0027] Optionally, the predicting of the connector core temperature data of the cable connector by the cascade inversion model based on the current surface temperature data of each target position point includes:

[0028] The current surface temperature data of each target position point is input into the radial inversion model to predict the predicted wire core temperature data of each target position point, and the predicted wire core temperature data of each target position point is input into the axial inversion model to obtain the joint wire core temperature data of the cable joint.

[0029] In a second aspect, the present application also provides a device for predicting the temperature data of a cable connector core.

[0030] The device comprises:

[0031] An acquisition module, used to acquire the joint structure data of the cable joint and each historical surface temperature data of the cable, and to construct a thermal circuit model of the cable joint based on the joint structure data;

[0032] A simulation module, for simulating the core temperature corresponding to each of the modal temperature simulation strategies through different modal temperature simulation strategies based on each of the historical surface temperature data and the thermal circuit model, and identifying each target position point of the cable and a radial inversion data set of the cable joint based on the historical cable surface temperature corresponding to each of the modes and the core temperature corresponding to each of the modes;

[0033] A generating module, used for training an initial radial inversion model based on the radial inversion data set to obtain a radial inversion model, and generating predicted core temperature data corresponding to each of the historical surface temperature data based on each of the historical surface temperature data and the radial inversion model;

[0034] A training module, for training an initial axial inversion model based on each of the historical surface temperature data and the predicted core temperature data corresponding to each of the historical surface temperature data, to obtain an axial inversion model, and using the radial inversion model and the axial inversion model as a cascade inversion model;

[0035] The prediction module is used to obtain the current surface temperature data of each of the target position points, and based on the current surface temperature data of each of the target position points, predict the connector wire core temperature data of the cable connector through the cascade inversion model.

[0036] Optionally, the acquisition module is specifically used to:

[0037] Dividing the joint structure data into radial structure data and axial structure data, and dividing the radial structure data into segment structure data of each preset joint segment and joint connection structure data, and constructing radial thermal resistance branches of each preset joint segment based on each segment structure data;

[0038] Based on the connector connection structure data, construct each radial branch between the cable connector and the cable, and divide the axial structure data into cable connector data and cable body data;

[0039] Based on the cable joint data, a cable joint thermal circuit model is constructed, and based on the cable body data, a cable body thermal circuit model is constructed;

[0040] Based on the radial thermal resistance branches and the radial branches, the thermal circuit model of the cable joint is connected with the thermal circuit model of the cable body to obtain the thermal circuit model of the cable joint.

[0041] Optionally, the simulation module is specifically used for:

[0042] Each historical surface temperature data is split into sub-historical surface temperature data of multiple location points, and simulation parameters of each modal temperature simulation strategy are obtained;

[0043] Adjust the thermal circuit model based on each simulation parameter to obtain a simulated thermal circuit model corresponding to each modal temperature simulation strategy, and input each sub-historical surface temperature data of each historical surface temperature data into each simulated thermal circuit model to obtain sub-core temperature data corresponding to each sub-historical surface temperature data of each historical surface temperature data;

[0044] The sub-core temperature data corresponding to each sub-historical surface temperature data of each historical surface temperature data obtained by simulating each simulated thermal circuit model is used as the core temperature of the modal temperature simulation strategy corresponding to each simulated thermal circuit model.

[0045] Optionally, the simulation module is specifically used for:

[0046] For each modal temperature simulation strategy, based on the sub-historical surface temperature data of each position point corresponding to the modal temperature simulation strategy and the sub-wire core temperature data corresponding to each position point, the temperature association weight value corresponding to each position point is calculated through the intensity weight algorithm, and the position point corresponding to the temperature association weight value greater than the weight threshold is used as the target position point;

[0047] The sub-historical surface temperature data of each target position point in each of the modal temperature simulation strategies and the sub-core temperature data of each target position point in each of the modal temperature simulation strategies are used as radial inversion data sets of the cable joint.

[0048] Optionally, the generating module is specifically used for:

[0049] Based on the radial inversion data set, an initial radial inversion model is trained to obtain a candidate radial inversion model, and based on the candidate radial inversion model, predicted sub-core temperature data corresponding to each sub-surface temperature data of each target position point is generated;

[0050] Based on the predicted sub-core temperature data corresponding to each sub-surface temperature data of each target position point, the predicted effect value of the candidate radial inversion model is calculated by a model effect prediction algorithm, and when the predicted effect value does not meet the prediction effect condition, return to execute the step of training the initial radial inversion model based on the radial inversion data set to obtain the candidate radial inversion model until the predicted effect value meets the prediction effect condition;

[0051] The candidate radial inversion model obtained in the last iteration is used as the radial inversion model.

[0052] Optionally, the prediction module is specifically used to:

[0053] The current surface temperature data of each target position point is input into the radial inversion model to predict the predicted wire core temperature data of each target position point, and the predicted wire core temperature data of each target position point is input into the axial inversion model to obtain the joint wire core temperature data of the cable joint.

[0054] 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.

[0055] 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.

[0056] 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.

[0057] The method and device for predicting the core temperature data of the cable joint of the above-mentioned cable joint obtains the joint structure data of the cable joint and the historical surface temperature data of the cable, and constructs a thermal circuit model of the cable joint based on the joint structure data; based on the historical surface temperature data and the thermal circuit model, simulates the core temperature corresponding to each modal temperature simulation strategy through different modal temperature simulation strategies, and identifies each target position point of the cable and the radial inversion data set of the cable joint based on the historical cable surface temperature corresponding to each mode and the core temperature corresponding to each mode; based on the radial inversion data set, trains an initial radial inversion Model, obtain radial inversion model, and based on each of the historical surface temperature data and the radial inversion model, generate predicted core temperature data corresponding to each of the historical surface temperature data; based on each of the historical surface temperature data and the predicted core temperature data corresponding to each of the historical surface temperature data, train the initial axial inversion model to obtain the axial inversion model, and use the radial inversion model and the axial inversion model as the cascade inversion model; obtain the current surface temperature data of each of the target position points, and based on the current surface temperature data of each of the target position points, predict the core temperature data of the cable joint through the cascade inversion model. This scheme generates a training data set containing surface temperature data and core temperature data by constructing a thermal circuit model of the cable joint, and then uses the neighborhood component analysis algorithm to select the optimal measurement point on the cable surface and determine the number and position of the feature points. Afterwards, this scheme establishes a cascade inversion model composed of a radial inversion model and an axial inversion model based on the extreme learning machine algorithm, which can accurately invert the core temperature of the intermediate joint from the cable surface temperature. Therefore, when predicting the core temperature of the joint, a comprehensive prediction is made from the two perspectives of axial heat transfer characteristics and radial heat transfer characteristics, avoiding the limitations of the traditional cable joint core temperature prediction based on surface temperature. Model training is performed from simulation data of multiple modes, so that this scheme greatly reduces the impact of the environment on the prediction process, thereby comprehensively improving the calculation accuracy of the core temperature data of the cable joint. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 It is a schematic flow chart of a method for predicting connector wire core temperature data of a cable connector in one embodiment;

[0059] Figure 2 A schematic diagram of a model circuit structure of a thermal circuit model of a cable connector in one embodiment;

[0060] Figure 3 A schematic diagram of the structure of a cascade inversion model in one embodiment;

[0061] Figure 4A schematic diagram of a process flow of an example of predicting connector wire core temperature data of a cable connector in one embodiment;

[0062] Figure 5 It is a structural block diagram of a device for predicting the temperature data of a cable connector core in one embodiment;

[0063] Figure 6 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0064] 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.

[0065] The method for predicting the core temperature data of the cable joint provided in the embodiment of the present application can be applied to the prediction application environment of the core temperature of the cable joint. The method can be applied to a terminal, a server, or a system including a terminal and a server, and is implemented through the interaction between the terminal and the server. Among them, the terminal can be, but is not limited to, various personal computers, laptops, smart phones, tablet computers, etc. Among them, the terminal generates a training data set containing surface temperature data and core temperature data by constructing a thermal circuit model of the cable joint, and then uses a neighborhood component analysis algorithm to select the optimal measuring point on the cable surface to determine the number and position of the characteristic points. Afterwards, this scheme establishes a cascade inversion model consisting of a radial inversion model and an axial inversion model based on the extreme learning machine algorithm, which can accurately invert the core temperature of the intermediate joint from the cable surface temperature. Therefore, when predicting the core temperature of the joint, a comprehensive prediction is made from the two perspectives of axial heat transfer characteristics and radial heat transfer characteristics, avoiding the limitations of the traditional cable joint core temperature prediction based on surface temperature. Model training is performed from simulation data of multiple modes, so that this scheme greatly reduces the impact of the environment on the prediction process, thereby comprehensively improving the calculation accuracy of the core temperature data of the cable joint.

[0066] In one embodiment, Figure 1 As shown, a method for predicting the temperature data of a cable connector core is provided, and the method is applied to a terminal as an example for explanation, and includes the following steps:

[0067] Step S101, obtaining joint structure data of a cable joint and various historical surface temperature data of the cable, and constructing a thermal circuit model of the cable joint based on the joint structure data.

[0068] In this embodiment, the terminal responds to the user's information upload operation and obtains the joint structure data of the cable joint part, wherein the cable joint is a cable intermediate joint, and the cable intermediate joint includes two parts, the cable joint and the cable body, and the joint structure data also includes the cable joint data and the cable body data. The joint structure data is divided into radial and axial directions, including radial structure data and axial structure data, wherein the radial structure data is the structure data of the radial thermal resistance structure of the joint structure, and the structure data of the radial branch structure connecting the cable joint and the cable body. The axial structure data includes the structure data of the cable joint connected to the cable body through the axial thermal resistance of the conductor, the copper shell and part of the aluminum sheath, and the material structure data of the axial heat transfer in the good conductor material, etc. Then, the terminal obtains the surface temperature data of the cable obtained by each historical acquisition, and obtains each historical surface temperature data of the cable. Then, the terminal constructs a thermal circuit model of the cable joint based on the joint structure data. Among them, the thermal model is a three-dimensional steady-state thermal circuit model of the single-core cable intermediate joint including the thermal circuit model of the cable intermediate joint and the three-dimensional thermal circuit model of the cable body. The specific model construction process will be described in detail later.

[0069] Step S102, based on each historical surface temperature data and the thermal circuit model, different modal temperature simulation strategies are used to simulate the wire core temperature corresponding to each modal temperature simulation strategy, and based on the historical cable surface temperature corresponding to each mode and the wire core temperature corresponding to each mode, each target position point of the cable and the radial inversion data set of the cable joint are identified.

[0070] In this embodiment, the terminal simulates the wire core temperature corresponding to each modal temperature simulation strategy through different modal temperature simulation strategies based on each historical surface temperature data and the thermal circuit model, and identifies each target position point of the cable and the radial inversion data set of the cable joint based on the historical cable surface temperature corresponding to each mode and the wire core temperature corresponding to each mode. Among them, each modal temperature simulation strategy is a simulation strategy corresponding to simulation conditions such as different load currents, ambient temperatures and convection heat transfer coefficients. Among them, each target position point is a position point where the temperature correlation strength between the surface temperature data and the wire core temperature data is relatively large. Radial inversion data and cable surface temperature data including each target position point, and wire core temperature data of each target position point. The specific identification process will be described in detail later.

[0071] Step S103, based on the radial inversion data set, an initial radial inversion model is trained to obtain a radial inversion model, and based on each historical surface temperature data and the radial inversion model, predicted core temperature data corresponding to each historical surface temperature data is generated.

[0072] In this embodiment, the terminal trains an initial radial inversion model based on the radial inversion data set to obtain a radial inversion model, and generates predicted wire core temperature data corresponding to each historical surface temperature data based on each historical surface temperature data and the radial inversion model. The data set for training the initial radial inversion model includes a radial inversion data set with the cable surface temperature data of each target position point as input data and the wire core temperature data of each target position point as output data.

[0073] Step S104, based on each historical surface temperature data and the predicted core temperature data corresponding to each historical surface temperature data, an initial axial inversion model is trained to obtain an axial inversion model, and the radial inversion model and the axial inversion model are used as a cascade inversion model.

[0074] In this embodiment, the terminal trains an initial axial inversion model based on each historical surface temperature data and the predicted wire core temperature data corresponding to each historical surface temperature data to obtain an axial inversion model, and uses the radial inversion model and the axial inversion model as a cascade inversion model. The training process of the axial inversion model is the same as the training process of the radial inversion model, and the data set of the axial inversion model includes an axial inversion data set that uses the predicted wire core temperature data of each target position point output by the radial inversion model as input data and uses the wire core temperature of the joint at the position to be predicted as output data.

[0075] Step S105, obtaining the current surface temperature data of each target position point, and based on the current surface temperature data of each target position point, predicting the joint wire core temperature data of the cable joint through a cascade inversion model.

[0076] In this embodiment, the terminal obtains the current surface temperature data of each target position point, and based on the current surface temperature data of each target position point, predicts the connector core temperature data of the cable connector through the cascade inversion model. The specific identification process will be described in detail later.

[0077] Based on the above scheme, by constructing a thermal circuit model of the cable joint, a training data set containing surface temperature data and core temperature data is generated. Then, the neighborhood component analysis algorithm is used to select the optimal measurement points on the cable surface and determine the number and position of the characteristic points. After that, this scheme establishes a cascade inversion model consisting of a radial inversion model and an axial inversion model based on the extreme learning machine algorithm, which can accurately invert the core temperature of the intermediate joint from the cable surface temperature. Therefore, when predicting the core temperature of the joint, a comprehensive prediction is made from the two perspectives of axial heat transfer characteristics and radial heat transfer characteristics, avoiding the limitations of the traditional cable joint core temperature prediction based on surface temperature. The model is trained from simulation data of multiple modes, so that this scheme greatly reduces the impact of the environment on the prediction process, thereby comprehensively improving the calculation accuracy of the core temperature data of the cable joint.

[0078] Optionally, constructing a thermal circuit model of a cable joint based on the joint structure data includes: dividing the joint structure data into radial structure data and axial structure data, and dividing the radial structure data into segment structure data of each preset joint segment and joint connection structure data, and based on each segment structure data, respectively constructing a radial thermal resistance branch of each preset joint segment; constructing each radial branch between the cable joint and the cable based on the joint connection structure data, and dividing the axial structure data into cable joint data and cable body data; constructing a cable joint thermal circuit model based on the cable joint data, and constructing a cable body thermal circuit model based on the cable body data, and connecting the cable joint thermal circuit model with the cable body thermal circuit model based on each radial thermal resistance branch and each radial branch to obtain a thermal circuit model of the cable joint.

[0079] In this embodiment, the terminal divides the joint structure data into radial structure data and axial structure data, and divides the radial structure data into segment structure data of each preset joint segment and joint connection structure data, and constructs the radial thermal resistance branch of each preset joint segment based on each segment structure data. Then, the terminal constructs each radial branch between the cable joint and the cable based on the joint connection structure data, and divides the axial structure data into cable joint data and cable body data. After that, the terminal constructs a cable joint thermal circuit model based on the cable joint data, and constructs a cable body thermal circuit model based on the cable body data. Finally, based on each radial thermal resistance branch and each radial branch, the terminal connects the cable joint thermal circuit model with the cable body thermal circuit model to obtain the thermal circuit model of the cable joint. Figure 2 The figure shows the thermal circuit model of the cable joint. The thermal circuit model is used to simulate the current transmission process between the various joint structural parts of the cable joint and the various connection structural parts of the cable body. In this thermal model, all materials, conductors, connection components, and shielding structures involved are characterized by resistance models.

[0080] Based on the above scheme, by constructing a three-dimensional steady-state thermal circuit model of the detailed structure of the cable intermediate joint, the accuracy and practicality of the simulated cable core temperature data can be improved.

[0081] Optionally, based on each historical surface temperature data and the thermal circuit model, different modal temperature simulation strategies are used to simulate the wire core temperature corresponding to each modal temperature simulation strategy, including: splitting each historical surface temperature data into sub-historical surface temperature data of multiple position points, and obtaining the simulation parameters of each modal temperature simulation strategy; adjusting the thermal circuit model based on each simulation parameter to obtain the simulated thermal circuit model corresponding to each modal temperature simulation strategy, and inputting each sub-historical surface temperature data of each historical surface temperature data into each simulated thermal circuit model to obtain the sub-wire core temperature data corresponding to each sub-historical surface temperature data of each historical surface temperature data; using the sub-wire core temperature data corresponding to each sub-historical surface temperature data of each historical surface temperature data simulated by each simulated thermal circuit model as the wire core temperature of the modal temperature simulation strategy corresponding to each simulated thermal circuit model.

[0082] In this embodiment, the terminal splits each historical surface temperature data into sub-historical surface temperature data of multiple locations, and obtains simulation parameters of each modal temperature simulation strategy. The simulation parameters include current parameters corresponding to the load current, ambient temperature parameters corresponding to the ambient temperature, and heat transfer parameters corresponding to the convection heat transfer coefficient. The terminal obtains the simulated thermal circuit model corresponding to each modal temperature simulation strategy by adding each parameter to the model environmental parameters of the thermal circuit model.

[0083] The terminal inputs each sub-historical surface temperature data of each historical surface temperature data into each simulated heat circuit model, and obtains the sub-core temperature data corresponding to each sub-historical surface temperature data of each historical surface temperature data. Finally, the terminal uses the sub-core temperature data corresponding to each sub-historical surface temperature data of each historical surface temperature data obtained by simulating each simulated heat circuit model as the core temperature of the modal temperature simulation strategy corresponding to each simulated heat circuit model.

[0084] Based on the above scheme, multiple position points are preset to simulate the sub-core temperature data corresponding to each sub-historical surface temperature data, which improves the comprehensiveness and accuracy of the simulation of the core temperature. In addition, by constructing simulation thermal circuit models corresponding to different modal temperature simulation strategies and then simulating them separately, the influence of environmental factors on the core temperature simulation is eliminated, and the accuracy of the simulated core temperature is improved.

[0085] Optionally, based on the historical cable surface temperature corresponding to each mode and the wire core temperature corresponding to each mode, each target position point of the cable and the radial inversion data set of the cable joint are identified, including: for each modal temperature simulation strategy, based on the sub-historical surface temperature data of each position point corresponding to the modal temperature simulation strategy and the sub-wire core temperature data corresponding to each position point, the temperature association weight value corresponding to each position point is calculated through an intensity weight algorithm, and the position point corresponding to the temperature association weight value greater than the weight threshold is used as the target position point; the sub-historical surface temperature data of each target position point in each modal temperature simulation strategy and the sub-wire core temperature data of each target position point in each modal temperature simulation strategy are used as the radial inversion data set of the cable joint.

[0086] In this embodiment, for each modal temperature simulation strategy, the terminal calculates the temperature-related weight value corresponding to each position point based on the sub-historical surface temperature data of each position point corresponding to the modal temperature simulation strategy and the sub-core temperature data corresponding to each position point through an intensity weight algorithm, and takes the position point corresponding to the temperature-related weight value greater than the weight threshold as the target position point. The intensity weight algorithm is a neighborhood component analysis (NCA) algorithm. The weight threshold is preset in the terminal.

[0087] The terminal uses the sub-historical surface temperature data of each target position point in each modal temperature simulation strategy and the sub-core temperature data of each target position point in each modal temperature simulation strategy as the radial inversion data set of the cable joint.

[0088] Based on the above scheme, the optimal measuring points on the cable surface are selected through comprehensive analysis of the simulated core temperature data for training subsequent radial and axial inversion models, which improves the accuracy and effect of training.

[0089] Optionally, based on the radial inversion data set, an initial radial inversion model is trained to obtain a radial inversion model, including: based on the radial inversion data set, the initial radial inversion model is trained to obtain a candidate radial inversion model, and based on the candidate radial inversion model, predicted sub-core temperature data corresponding to each sub-surface temperature data of each target position point is generated; based on the predicted sub-core temperature data corresponding to each sub-surface temperature data of each target position point, a predicted effect value of the candidate radial inversion model is calculated through a model effect prediction algorithm, and when the predicted effect value does not meet the prediction effect condition, return to execute the step of training the initial radial inversion model based on the radial inversion data set to obtain the candidate radial inversion model, until the predicted effect value meets the prediction effect condition; and the candidate radial inversion model obtained in the last iteration is used as the radial inversion model.

[0090] In this embodiment, the terminal trains an initial radial inversion model based on the radial inversion data set to obtain a candidate radial inversion model, and generates predicted sub-core temperature data corresponding to each sub-surface temperature data of each target position point based on the candidate radial inversion model. The initial radial inversion model is a node hotspot temperature cascade radial inversion model established based on the Extreme Learning Machine (ELM) algorithm.

[0091] Specifically, the training process of the model is as follows: for the training data set X, the input of each sample is x(i) and the output is y(i). The input weight w and bias b are randomly generated, and the hidden layer output Y is calculated as:

[0092] Y=G(Xw+b)

[0093] Furthermore, the terminal uses pseudo-inverse solution to obtain the calculation formula of the output weight β:

[0094] β=pinv(Y)

[0095] Among them, β is the output weight and pinv(Y) is the pseudo-inverse of Y.

[0096] Furthermore, the terminal outputs the core temperature by solving the following equation:

[0097] f(x)=G(Xw+b)

[0098] Then, the terminal calculates the prediction effect value of the candidate radial inversion model through the model effect prediction algorithm based on the predicted sub-core temperature data corresponding to each sub-surface temperature data of each target position point.

[0099] The process of calculating the prediction effect value is that the terminal calculates the goodness of fit of the radial temperature inversion model and the sensitivity coefficient of the radial temperature inversion model based on the predicted sub-core temperature data corresponding to each sub-surface temperature data of each target position point, such as the goodness of fit R 2 And the sensitivity coefficient Se, calculated as follows:

[0100] R 2 = regression sum of squares / total sum of squares

[0101]

[0102] Among them, a i is the fitting coefficient corresponding to the temperature of the i-th conductor.

[0103] Afterwards, when the predicted effect value does not meet the predicted effect condition, the terminal returns to execute the step of training the initial radial inversion model based on the radial inversion data set to obtain the candidate radial inversion model until the predicted effect value meets the predicted effect condition. The predicted effect condition is R2 >0.95 and SE<5.

[0104] The terminal uses the candidate radial inversion model obtained in the last iteration as the radial inversion model.

[0105] Based on the above scheme, the radial inversion model is trained through the radial inversion data sets of the target location points, and the model performance is optimized by the goodness of fit and sensitivity coefficient, so that the radial inversion model is obtained by iterative training, which improves the training accuracy of the radial inversion model.

[0106] Optionally, based on the current surface temperature data of each target position point, the joint wire core temperature data of the cable joint is predicted through a cascade inversion model, including: inputting the current surface temperature data of each target position point into a radial inversion model to predict the predicted wire core temperature data of each target position point, and inputting the predicted wire core temperature data of each target position point into an axial inversion model to obtain the joint wire core temperature data of the cable joint.

[0107] In this embodiment, the terminal inputs the current surface temperature data of each target position point into the radial inversion model to predict the predicted wire core temperature data of each target position point, and inputs the predicted wire core temperature data of each target position point into the axial inversion model to obtain the joint wire core temperature data of the cable joint.

[0108] Based on the above scheme, this scheme constructs a cascade inversion model. When predicting the joint core temperature data of the cable joint through the surface temperature data, the joint core temperature data of the cable joint is obtained after comprehensively considering the radial temperature characteristics and lateral temperature characteristics of the cable joint, thereby improving the accuracy of the joint core temperature data of the cable joint.

[0109] The present application also provides an example of predicting the temperature data of the cable connector core. Figure 4 As shown, the specific processing process includes the following steps:

[0110] Step S401, obtaining the joint structure data of the cable joint and the historical surface temperature data of the cable.

[0111] Step S402, divides the joint structure data into radial structure data and axial structure data, and divides the radial structure data into segment structure data of each preset joint segment and joint connection structure data, and based on each segment structure data, constructs the radial thermal resistance branch of each preset joint segment respectively.

[0112] Step S403, based on the connector connection structure data, construct each radial branch between the cable connector and the cable, and divide the axial structure data into cable connector data and cable body data.

[0113] Step S404: constructing a cable joint thermal circuit model based on the cable joint data, and constructing a cable body thermal circuit model based on the cable body data.

[0114] Step S405: Based on each radial thermal resistance branch and each radial branch, the thermal circuit model of the cable joint is connected with the thermal circuit model of the cable body to obtain the thermal circuit model of the cable joint.

[0115] Step S406: split each historical surface temperature data into sub-historical surface temperature data of multiple location points, and obtain simulation parameters of each modal temperature simulation strategy.

[0116] Step S407, adjust the thermal circuit model based on each simulation parameter to obtain the simulated thermal circuit model corresponding to each modal temperature simulation strategy, and input each sub-historical surface temperature data of each historical surface temperature data into each simulated thermal circuit model to obtain the sub-wire core temperature data corresponding to each sub-historical surface temperature data of each historical surface temperature data.

[0117] Step S408 , using the sub-wire core temperature data corresponding to each sub-historical surface temperature data of each historical surface temperature data obtained by simulating each simulated thermal circuit model as the wire core temperature of the modal temperature simulation strategy corresponding to each simulated thermal circuit model.

[0118] Step S409, for each modal temperature simulation strategy, based on the sub-historical surface temperature data of each position point corresponding to the modal temperature simulation strategy and the sub-wire core temperature data corresponding to each position point, the temperature-related weight value corresponding to each position point is calculated through the strength weight algorithm, and the position point corresponding to the temperature-related weight value greater than the weight threshold is used as the target position point.

[0119] Step S410, using the sub-historical surface temperature data of each target position point in each modal temperature simulation strategy and the sub-core temperature data of each target position point in each modal temperature simulation strategy as the radial inversion data set of the cable joint.

[0120] Step S411, based on the radial inversion data set, an initial radial inversion model is trained to obtain a radial inversion model, and based on each historical surface temperature data and the radial inversion model, predicted core temperature data corresponding to each historical surface temperature data is generated.

[0121] Step S412, obtaining current surface temperature data of each target position point.

[0122] Step S413, input the current surface temperature data of each target position point into the radial inversion model to predict the predicted wire core temperature data of each target position point, and input the predicted wire core temperature data of each target position point into the axial inversion model to obtain the joint wire core temperature data of the cable joint.

[0123] 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.

[0124] Based on the same inventive concept, the embodiment of the present application also provides a device for predicting the temperature data of the core of a cable joint for realizing the method for predicting the temperature data of the core of a cable joint involved in the above-mentioned method. 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 the device for predicting the temperature data of the core of a cable joint provided below can refer to the limitations of the method for predicting the temperature data of the core of a cable joint in the above text, and will not be repeated here.

[0125] In one embodiment, Figure 5 As shown, a device for predicting the temperature data of a cable joint core is provided, comprising: an acquisition module 510, a simulation module 520, a generation module 530, a training module 540 and a prediction module 550, wherein:

[0126] An acquisition module 510 is used to acquire the joint structure data of the cable joint and the historical surface temperature data of the cable, and to construct a thermal circuit model of the cable joint based on the joint structure data;

[0127] A simulation module 520 is used to simulate the core temperature corresponding to each of the modal temperature simulation strategies based on the historical surface temperature data and the thermal circuit model through different modal temperature simulation strategies, and identify each target position point of the cable and the radial inversion data set of the cable joint based on the historical cable surface temperature corresponding to each of the modes and the core temperature corresponding to each of the modes;

[0128] A generating module 530 is used to train an initial radial inversion model based on the radial inversion data set to obtain a radial inversion model, and to generate predicted core temperature data corresponding to each of the historical surface temperature data based on each of the historical surface temperature data and the radial inversion model;

[0129] A training module 540 is used to train an initial axial inversion model based on each of the historical surface temperature data and the predicted core temperature data corresponding to each of the historical surface temperature data to obtain an axial inversion model, and use the radial inversion model and the axial inversion model as a cascade inversion model;

[0130] The prediction module 550 is used to obtain the current surface temperature data of each of the target position points, and based on the current surface temperature data of each of the target position points, predict the joint core temperature data of the cable joint through the cascade inversion model.

[0131] Optionally, the acquisition module 510 is specifically configured to:

[0132] Dividing the joint structure data into radial structure data and axial structure data, and dividing the radial structure data into segment structure data of each preset joint segment and joint connection structure data, and constructing radial thermal resistance branches of each preset joint segment based on each segment structure data;

[0133] Based on the connector connection structure data, construct each radial branch between the cable connector and the cable, and divide the axial structure data into cable connector data and cable body data;

[0134] Based on the cable joint data, a cable joint thermal circuit model is constructed, and based on the cable body data, a cable body thermal circuit model is constructed;

[0135] Based on the radial thermal resistance branches and the radial branches, the thermal circuit model of the cable joint is connected with the thermal circuit model of the cable body to obtain the thermal circuit model of the cable joint.

[0136] Optionally, the simulation module 520 is specifically used for:

[0137] Each historical surface temperature data is split into sub-historical surface temperature data of multiple location points, and simulation parameters of each modal temperature simulation strategy are obtained;

[0138] Adjust the thermal circuit model based on each simulation parameter to obtain a simulated thermal circuit model corresponding to each modal temperature simulation strategy, and input each sub-historical surface temperature data of each historical surface temperature data into each simulated thermal circuit model to obtain sub-core temperature data corresponding to each sub-historical surface temperature data of each historical surface temperature data;

[0139] The sub-core temperature data corresponding to each sub-historical surface temperature data of each historical surface temperature data obtained by simulating each simulated thermal circuit model is used as the core temperature of the modal temperature simulation strategy corresponding to each simulated thermal circuit model.

[0140] Optionally, the simulation module 520 is specifically used for:

[0141] For each modal temperature simulation strategy, based on the sub-historical surface temperature data of each position point corresponding to the modal temperature simulation strategy and the sub-wire core temperature data corresponding to each position point, the temperature association weight value corresponding to each position point is calculated through the intensity weight algorithm, and the position point corresponding to the temperature association weight value greater than the weight threshold is used as the target position point;

[0142] The sub-historical surface temperature data of each target position point in each of the modal temperature simulation strategies and the sub-core temperature data of each target position point in each of the modal temperature simulation strategies are used as radial inversion data sets of the cable joint.

[0143] Optionally, the generating module 530 is specifically configured to:

[0144] Based on the radial inversion data set, an initial radial inversion model is trained to obtain a candidate radial inversion model, and based on the candidate radial inversion model, predicted sub-core temperature data corresponding to each sub-surface temperature data of each target position point is generated;

[0145] Based on the predicted sub-core temperature data corresponding to each sub-surface temperature data of each target position point, the predicted effect value of the candidate radial inversion model is calculated by a model effect prediction algorithm, and when the predicted effect value does not meet the prediction effect condition, return to execute the step of training the initial radial inversion model based on the radial inversion data set to obtain the candidate radial inversion model until the predicted effect value meets the prediction effect condition;

[0146] The candidate radial inversion model obtained in the last iteration is used as the radial inversion model.

[0147] Optionally, the prediction module 550 is specifically used for:

[0148] The current surface temperature data of each target position point is input into the radial inversion model to predict the predicted wire core temperature data of each target position point, and the predicted wire core temperature data of each target position point is input into the axial inversion model to obtain the joint wire core temperature data of the cable joint.

[0149] Each module in the above-mentioned cable joint core temperature data prediction device 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.

[0150] In one embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as follows: Figure 6 As shown. The computer device includes a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. Among them, 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 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 achieved 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 temperature data of the connector core of a cable connector is implemented. The display screen of the computer device 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 covered on the display screen, or a key, trackball or touchpad set on the computer device shell, or an external keyboard, touchpad or mouse, etc.

[0151] Those skilled in the art will understand that Figure 6 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.

[0152] In one embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the steps of any one of the methods in the first aspect are implemented.

[0153] 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 any one of the methods in the first aspect are implemented.

[0154] In one embodiment, a computer program product is provided, comprising a computer program, which, when executed by a processor, implements the steps of any one of the methods in the first aspect.

[0155] 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.

[0156] 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 the present 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.

[0157] 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.

[0158] 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 temperature data of a cable connector core, characterized in that: The method comprises: Acquire joint structure data of the cable joint and various historical surface temperature data of the cable, and construct a thermal circuit model of the cable joint based on the joint structure data; Based on each of the historical surface temperature data and the thermal circuit model, different modal temperature simulation strategies are used to simulate the wire core temperature corresponding to each of the modal temperature simulation strategies, and based on the historical cable surface temperature corresponding to each of the modes and the wire core temperature corresponding to each of the modes, each target position point of the cable and the radial inversion data set of the cable joint are identified; Based on the radial inversion data set, an initial radial inversion model is trained to obtain a radial inversion model, and based on each of the historical surface temperature data and the radial inversion model, predicted core temperature data corresponding to each of the historical surface temperature data is generated; Based on each of the historical surface temperature data and the predicted core temperature data corresponding to each of the historical surface temperature data, an initial axial inversion model is trained to obtain an axial inversion model, and the radial inversion model and the axial inversion model are used as a cascade inversion model; The current surface temperature data of each of the target position points is obtained, and based on the current surface temperature data of each of the target position points, the joint wire core temperature data of the cable joint is predicted through the cascade inversion model.

2. The method according to claim 1, characterized in that The step of constructing a thermal circuit model of the cable joint based on the joint structure data includes: Dividing the joint structure data into radial structure data and axial structure data, and dividing the radial structure data into segment structure data of each preset joint segment and joint connection structure data, and constructing radial thermal resistance branches of each preset joint segment based on each segment structure data; Based on the connector connection structure data, construct each radial branch between the cable connector and the cable, and divide the axial structure data into cable connector data and cable body data; Based on the cable joint data, a cable joint thermal circuit model is constructed, and based on the cable body data, a cable body thermal circuit model is constructed; Based on the radial thermal resistance branches and the radial branches, the thermal circuit model of the cable joint is connected with the thermal circuit model of the cable body to obtain the thermal circuit model of the cable joint.

3. The method according to claim 1, characterized in that: The method of simulating the wire core temperature corresponding to each of the modal temperature simulation strategies based on each of the historical surface temperature data and the thermal circuit model through different modal temperature simulation strategies includes: Each historical surface temperature data is split into sub-historical surface temperature data of multiple location points, and simulation parameters of each modal temperature simulation strategy are obtained; Adjust the thermal circuit model based on each simulation parameter to obtain a simulated thermal circuit model corresponding to each modal temperature simulation strategy, and input each sub-historical surface temperature data of each historical surface temperature data into each simulated thermal circuit model to obtain sub-core temperature data corresponding to each sub-historical surface temperature data of each historical surface temperature data; The sub-core temperature data corresponding to each sub-historical surface temperature data of each historical surface temperature data obtained by simulating each simulated thermal circuit model is used as the core temperature of the modal temperature simulation strategy corresponding to each simulated thermal circuit model.

4. The method according to claim 3, characterized in that The method of identifying each target position point of the cable and the radial inversion data set of the cable joint based on the historical cable surface temperature corresponding to each mode and the wire core temperature corresponding to each mode includes: For each modal temperature simulation strategy, based on the sub-historical surface temperature data of each position point corresponding to the modal temperature simulation strategy and the sub-wire core temperature data corresponding to each position point, the temperature association weight value corresponding to each position point is calculated through the intensity weight algorithm, and the position point corresponding to the temperature association weight value greater than the weight threshold is used as the target position point; The sub-historical surface temperature data of each target position point in each of the modal temperature simulation strategies and the sub-core temperature data of each target position point in each of the modal temperature simulation strategies are used as radial inversion data sets of the cable joint.

5. The method according to claim 3, characterized in that: The step of training an initial radial inversion model based on the radial inversion data set to obtain a radial inversion model comprises: Based on the radial inversion data set, an initial radial inversion model is trained to obtain a candidate radial inversion model, and based on the candidate radial inversion model, predicted sub-core temperature data corresponding to each sub-surface temperature data of each target position point is generated; Based on the predicted sub-core temperature data corresponding to each sub-surface temperature data of each target position point, the predicted effect value of the candidate radial inversion model is calculated by a model effect prediction algorithm, and when the predicted effect value does not meet the prediction effect condition, return to execute the step of training the initial radial inversion model based on the radial inversion data set to obtain the candidate radial inversion model until the predicted effect value meets the prediction effect condition; The candidate radial inversion model obtained in the last iteration is used as the radial inversion model.

6. The method according to claim 1, characterized in that The method of predicting the joint core temperature data of the cable joint based on the current surface temperature data of each target position point through the cascade inversion model includes: The current surface temperature data of each target position point is input into the radial inversion model to predict the predicted wire core temperature data of each target position point, and the predicted wire core temperature data of each target position point is input into the axial inversion model to obtain the joint wire core temperature data of the cable joint.

7. A device for predicting the temperature data of a cable joint core, characterized in that: The device comprises: An acquisition module, used to acquire the joint structure data of the cable joint and each historical surface temperature data of the cable, and to construct a thermal circuit model of the cable joint based on the joint structure data; A simulation module, for simulating the core temperature corresponding to each of the modal temperature simulation strategies through different modal temperature simulation strategies based on each of the historical surface temperature data and the thermal circuit model, and identifying each target position point of the cable and a radial inversion data set of the cable joint based on the historical cable surface temperature corresponding to each of the modes and the core temperature corresponding to each of the modes; A generating module, used for training an initial radial inversion model based on the radial inversion data set to obtain a radial inversion model, and generating predicted core temperature data corresponding to each of the historical surface temperature data based on each of the historical surface temperature data and the radial inversion model; A training module, for training an initial axial inversion model based on each of the historical surface temperature data and the predicted core temperature data corresponding to each of the historical surface temperature data, to obtain an axial inversion model, and using the radial inversion model and the axial inversion model as a cascade inversion model; The prediction module is used to obtain the current surface temperature data of each of the target position points, and based on the current surface temperature data of each of the target position points, predict the connector wire core temperature data of the cable connector through the cascade inversion 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.

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