E-SIM tag generation method and system

Abstract

This invention discloses an E-SIM tag generation method and system. It extracts key information representing the characteristics of various service packages from massive amounts of data to serve as operator service package tags. These tags significantly reduce the amount of data that needs to be displayed, facilitating users' quick selection of service packages offered by network operators. Furthermore, the operator service package tags, generated through a tag generation model based on network parameters, signal strength, and package parameters, integrate information from multiple perspectives. These tags accurately characterize the features of the network communication service packages offered by operators. Based on these tags, users can select and bind packages, providing accurate information for configuring and binding E-SIM with operator communication service packages, thus improving the accuracy and efficiency of package selection and binding.

Description

Technical Field

[0001] The present invention relates to the field of communication technology, and in particular to an E-SIM tag generation method and system. Background Art

[0002] The Embedded-Subscriber Identity Module (E-SIM) is an electronic SIM card, which is a data file that can be downloaded to a mobile terminal via the Internet. With an E-SIM, various electronic products can connect to the Internet, make and receive calls, send text messages, etc., and the functions are the same as those of an ordinary SIM card. The difference is that a SIM card only serves the communication services of fixed operators, and users cannot choose the communication services of other operators through a SIM card. However, an E-SIM can select the communication services of different operators according to user preferences.

[0003] For different operators, you can configure E-SIM with the operator and bind the operator's communication services, so that you can use the operator's communication service package to communicate through E-SIM. Currently, there are many operators, and the types of communication service packages provided by different operators are also complicated. The efficiency of configuring and binding E-SIM with the operator's communication service package is low, and it is difficult to match the communication service package suitable for the user, that is, the matching accuracy is low. Summary of the invention

[0004] The object of the present invention is to provide an E-SIM tag generation method and system to solve the above problems existing in the prior art.

[0005] An embodiment of the present invention provides a method for generating an E-SIM tag, comprising:

[0006] Obtain the network parameters, signal strength and package parameters of each operator in the service area where the user is located; the operator's network parameters include bearer-level QoS parameters, operating frequency and network standard; the signal strength is detected by the user's terminal device; the operator is in the operator list of the user's terminal device; the package parameters include the package fee and the package data volume; the network package data volume is the amount of data uploaded or downloaded by the package;

[0007] The network parameters, signal strength and package parameters are input into a pre-trained label generation model, and the label generation model outputs an operator service package label; the operator service package label represents the characteristics of the network service package provided by the operator; the dimension of the operator service package label is smaller than the sum of the dimensions of the network parameters, signal strength and package parameters;

[0008] Among them, the label generation model includes a parameter network, a signal network and a package network; the input of the parameter network is the network parameter, the input of the signal network is the signal strength, and the input of the package network is the package parameter.

[0009] Optionally, the method further includes: configuring the operator service package tag into the server so that the user can configure the E-SIM card and the operator service package.

[0010] Optionally, the training method for the label generation model includes:

[0011] Obtaining a training set, the training set including a plurality of training data, the training data including training network parameters, training signal strength and training package parameters;

[0012] The training network parameters, training signal strength and training package parameters are respectively input into the parameter network, signal network and package network, and the features of the training network parameters are extracted through the parameter network and counted as the first feature; the features of the training signal strength are extracted through the signal network and counted as the second feature; the features of the training package parameters are extracted through the package network and counted as the third feature;

[0013] Obtaining a correlation matrix based on the first feature, the second feature, and the third feature; the correlation matrix represents the correlation between the training network parameters, the training signal strength, and the training package parameters;

[0014] The loss function L of the label generation model is obtained based on the association matrix, the first loss function L1, the second loss function L2 of the signal network and the third loss function L3 of the package network;

[0015] If the loss function L of the label generation model converges, it is determined that the label generation model training is completed.

[0016] Optionally, the loss function L of the label generation model is obtained based on the association matrix, the first loss function L1, the second loss function L2 of the signal network, and the third loss function L3 of the package network, which is specifically obtained by the following formula:

[0017] L = a*L1+b*L2+c*L3;

[0018] Among them, a, b, c are weighting factors, and a, b, c are the elements of the main diagonal in the correlation matrix.

[0019] Optionally, obtaining a correlation matrix based on the first feature, the second feature, and the third feature includes:

[0020] Constructing a package information relationship diagram based on the first feature, the second feature, and the third feature;

[0021] Construct a package information graph based on training network parameters, training signal strength, and training package parameters;

[0022] Extract the features of the package information graph, which is counted as the fourth feature;

[0023] Using the fourth feature as the convolution kernel, perform a convolution operation on the package information relationship graph to obtain a convolution matrix;

[0024] Perform dimensionality reduction on the convolution matrix to obtain the correlation matrix.

[0025] Optionally, a package information relationship diagram is constructed based on the first feature, the second feature, and the third feature, including:

[0026] The first feature, the second feature, and the third feature are set in different rows of the matrix to obtain a package information relationship diagram.

[0027] An embodiment of the present invention further provides an E-SIM tag generation system, the system comprising:

[0028] The acquisition module is used to obtain the network parameters, signal strength and package parameters of each operator in the service area where the user is located; the network parameters of the operator include bearer-level QoS parameters, operating frequency and network standard; the signal strength is detected by the user's terminal device; the operator is in the operator list of the user's terminal device; the package parameters include the package fee and the package carrying data volume; the network package carrying data volume is the amount of data uploaded or downloaded by the package;

[0029] A label generation module, used for inputting network parameters, signal strength and package parameters into a pre-trained label generation model, and the label generation model outputs an operator service package label; the operator service package label represents the characteristics of the network service package provided by the operator; the dimension of the operator service package label is smaller than the sum of the dimensions of the network parameters, signal strength and package parameters;

[0030] Among them, the label generation model includes a parameter network, a signal network and a package network; the input of the parameter network is the network parameter, the input of the signal network is the signal strength, and the input of the package network is the package parameter.

[0031] Optionally, the system further includes:

[0032] The configuration module is used to configure the operator service package label to the server so that the user can configure the E-SIM card and the operator service package.

[0033] Compared with the prior art, the embodiments of the present invention achieve the following beneficial effects:

[0034] The embodiment of the present invention provides an E-SIM label generation method and system, which obtains the network parameters, signal strength and package parameters of each operator in the service area where the user is located; the network parameters of the operator include bearer-level QoS parameters, operating frequency and network standard; the signal strength is obtained by detecting the user's terminal equipment; the operator is in the operator list of the user's terminal equipment; the package parameters include package fees and package bearer data volume; the network package bearer data volume is the data volume provided by the package for upload or download; the network parameters, signal strength and package parameters are input into a pre-trained label generation model, and the label generation model outputs the operator service package label; the operator service package label represents the characteristics of the network service package provided by the operator; the dimension of the operator service package label is less than the sum of the dimensions of the network parameters, signal strength and package parameters; wherein the label generation model includes a parameter network, a signal network and a package network; the input of the parameter network is the network parameter, the input of the signal network is the signal strength, and the input of the package network is the package parameter.

[0035] The operator service package label obtained by adopting the above scheme can accurately characterize the characteristics of the network service package provided by the operator. The dimension of the operator service package label is smaller than the sum of the dimensions of the network parameters, signal strength and package parameters. The information that mainly represents the characteristics of the package can be extracted from the massive package information as the operator service package label. The operator service package label greatly reduces the amount of data that needs to be displayed, which is convenient for users to quickly select the service packages provided by the network operator. At the same time, the operator service package label obtained by the label generation model based on network parameters, signal strength and package parameters, the operator service package label integrates information from multiple angles of network parameters, signal strength and package parameters. The operator service package label can accurately characterize the characteristics of the network communication service package provided by the operator. On this basis, the package selection and binding are performed based on the operator service package label, which provides accurate information for users to configure and bind the E-SIM with the operator's communication service package, and improves the accuracy and efficiency of package selection and binding. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 The present invention provides a flowchart of an E-SIM tag generation method.

[0037] Figure 2 It is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention.

[0038] Markings in the figure: 500 - bus; 501 - receiver; 502 - processor; 503 - transmitter; 504 - memory; 505 - bus interface. DETAILED DESCRIPTION

[0039] The present invention will be described in detail below in conjunction with the accompanying drawings.

[0040] Example

[0041] The embodiment of the present invention provides a method for generating an E-SIM tag, such as Figure 1 As shown, the E-SIM tag generation method includes the following steps:

[0042] S101: Obtain network parameters, signal strength and package parameters of each operator in the service area where the user is located.

[0043] Optionally, the network parameters of the operator include bearer-level QoS parameters, operating frequency, and network standard. Bearer-level QoS parameters: Each EPS bearer (GBR and Non-GBR bearer) is associated with the following bearer-level QoS parameters: QoS Classification Identifier (QCI): used to specify the packet forwarding method for controlling the bearer level defined in the access node, such as scheduling weight, access threshold, queue management threshold, link layer protocol configuration, etc. The QCI parameters can be pre-configured by the operator to effectively reduce the amount of data when QoS parameters are transmitted and facilitate the interconnection and interoperability of products from different manufacturers. Allocation and Retention Priority (ARP): used to determine whether a request for bearer establishment or bearer modification can be accepted, and can also be used to determine whether a request for bearer establishment or bearer modification is rejected due to limited system resources. In addition, the eNodeB can also use ARP parameters to determine which bearer can be released when the system is resource-constrained. Operating frequency and network standard: The operating frequency and network standard of operators on 2G, 3G, 4G, and 5G networks are different. For example, operator A and operator B share frequencies to increase 5G coverage, while operator C, as a new player in the mobile communications field, owns the 700MHz and 4.9GHz frequency bands. The characteristic of 5G network is that there is only one standard in the world, which brings convenience to telecom operators and baseband chip developers and simplifies the process of networking and developing 5G chips. Supported 5G and LTE network bands: Different models of mobile devices support a variety of 5G and LTE frequency bands, and the specific support of these frequency bands varies from country to country. These network parameters are crucial to ensure that operators can provide stable and efficient communication services, and also affect the compatibility of user devices and network coverage. Signal strength is detected by the user's terminal device. For example, it is detected by the signal detection module of the mobile phone. The operator is in the operator list of the user's terminal device. Package parameters include package fees and package data volume. The network package data volume is the amount of data uploaded or downloaded by the package.

[0044] S102: Input network parameters, signal strength and package parameters into a pre-trained label generation model, and the label generation model outputs the operator service package label.

[0045] In an embodiment of the present invention, the operator service package label characterizes the characteristics of the network service package provided by the operator. The dimension of the operator service package label is smaller than the sum of the dimensions of the network parameters, signal strength and package parameters. In this way, information mainly representing the characteristics of the package can be extracted from the massive package information as the operator service package label. The operator service package label greatly reduces the amount of data that needs to be displayed, which is convenient for users to quickly select the service packages provided by the network operator. At the same time, the operator service package label obtained by the label generation model based on the network parameters, signal strength and package parameters, the operator service package label integrates information from multiple angles of network parameters, signal strength and package parameters. The operator service package label can accurately characterize the characteristics of the network communication service package provided by the operator. On this basis, the package selection and binding are performed based on the operator service package label, which provides accurate information for users to configure and bind the E-SIM with the operator's communication service package, thereby improving the accuracy and efficiency of package selection and binding.

[0046] As an optional implementation, the operator service package label has a corresponding relationship with the configuration parameters of the E-SIM card and the operator service package. In order to facilitate the subsequent user to configure and bind the E-SIM and the operator's communication service package, the E-SIM label generation method also includes: configuring the operator service package label to the server for the user to configure the E-SIM card and the operator service package. That is, the user selects the displayed operator service package label, and the system background sends the configuration parameters corresponding to the operator service package label to the user according to the corresponding relationship between the operator service package label and the configuration parameters. After obtaining the configuration parameters, the user can configure and bind the E-SIM card and the operator service package.

[0047] In the embodiment of the present invention, the user can obtain the operator service package label remotely, and configure the E-SIM card and the operator service package based on the correspondence between the operator service package label and the configuration parameters.

[0048] As another optional implementation, the operator service package label includes information characterizing the operator's E-SIM configuration parameters, network stability index, package fees, and package data volume.

[0049] In an embodiment of the present invention, the label generation model includes a parameter network, a signal network and a package network; the input of the parameter network is the network parameter, the input of the signal network is the signal strength, and the input of the package network is the package parameter.

[0050] The following describes the label generation model training method provided by the embodiment of the present invention from the perspective of the label generation model training method.

[0051] The training methods for the label generation model include:

[0052] A training set is obtained, where the training set includes multiple training data, and the training data includes training network parameters, training signal strength, and training package parameters.

[0053] The training network parameters, training signal strength and training package parameters are respectively input into the parameter network, the signal network and the package network, and the characteristics of the training network parameters are extracted through the parameter network, which are counted as the first characteristics. The characteristics of the training signal strength are extracted through the signal network, which are counted as the second characteristics. The characteristics of the training package parameters are extracted through the package network, which are counted as the third characteristics. In an embodiment of the present invention, the parameter network is a convolutional neural network (CNN), the signal network is a deep belief network (DBNs), and the package network is a recurrent neural network (RNN).

[0054] The association matrix is ​​obtained based on the first feature, the second feature, and the third feature. The association matrix represents the association relationship between the training network parameters, the training signal strength, and the training package parameters. Specifically, the association matrix is ​​obtained based on the first feature, the second feature, and the third feature, including:

[0055] A package information relationship graph is constructed based on the first feature, the second feature, and the third feature. That is, the first feature, the second feature, and the third feature are set in different rows of the matrix to obtain a package information relationship graph. The first row, the second row, and the third row of the package information relationship graph contain the first feature, the second feature, and the third feature, respectively. If the data dimensions of the first feature, the second feature, and the third feature are not aligned, the package information relationship graph is expanded so that the package information relationship graph includes all the first features, the second features, and the third features.

[0056] A package information graph is constructed based on training network parameters, training signal strength, and training package parameters. That is, the training network parameters, training signal strength, and training package parameters are set in different rows of the matrix to obtain a package information graph. The first row, the second row, and the third row in the package information graph contain training network parameters, training signal strength, and training package parameters, respectively. If the data is not aligned, the size of the package information graph is expanded so that the package information graph includes the training network parameters, training signal strength, and training package parameters.

[0057] The feature of the package information graph is extracted and counted as the fourth feature. In the embodiment of the present invention, the feature of the package information graph is extracted by CNN.

[0058] Using the fourth feature as the convolution kernel, the package information relationship graph is convolved to obtain the convolution matrix.

[0059] The convolution matrix is ​​subjected to a dimensionality reduction operation to obtain an association matrix. The dimensionality reduction operation is implemented by a convolution operation. Specifically, the convolution matrix can be subjected to a 3*3 unit matrix to obtain an association matrix.

[0060] The loss function L of the label generation model is obtained based on the association matrix, the first loss function L1, the second loss function L2 of the signal network and the third loss function L3 of the package network, which is specifically obtained by the following formula:

[0061] L=a*L1+b*L2+c*L3,

[0062] Among them, a, b, c are weighting factors, and a, b, c are the elements of the main diagonal in the correlation matrix.

[0063] If the loss function L of the label generation model converges, it is determined that the label generation model training is completed.

[0064] By adopting the above scheme, the mutual influencing relationship between network parameters, signal strength and package parameters can be excavated. The operator service package label generated on this basis can contain the characteristic information of network parameters, signal strength and package parameters. At the same time, the information of network parameters, signal strength and package parameters is reflected in the operator service package label with lower dimension and less data volume, which is convenient for users to select packages and configure and bind E-SIM cards with operator service packages.

[0065] Based on the above-mentioned E-SIM tag generation method, an embodiment of the present invention provides an E-SIM tag generation system, which is used to execute the above-mentioned E-SIM tag generation method. The E-SIM tag generation system includes:

[0066] The acquisition module is used to obtain the network parameters, signal strength and package parameters of each operator in the service area where the user is located; the operator's network parameters include bearer-level QoS parameters, operating frequency and network standard; the signal strength is detected by the user's terminal equipment; the operator is in the operator list of the user's terminal equipment; the package parameters include package fees and package data volume; the network package data volume is the amount of data uploaded or downloaded by the package.

[0067] The label generation module is used to input network parameters, signal strength and package parameters into a pre-trained label generation model, and the label generation model outputs the operator service package label; the operator service package label represents the characteristics of the network service package provided by the operator; the dimension of the operator service package label is smaller than the sum of the dimensions of the network parameters, signal strength and package parameters; wherein the label generation model includes a parameter network, a signal network and a package network; the input of the parameter network is the network parameter, the input of the signal network is the signal strength, and the input of the package network is the package parameter.

[0068] Optionally, the E-SIM tag generation system further includes: a configuration module, used to configure the operator service package tag into the server, so that the user can configure the E-SIM card and the operator service package.

[0069] The specific implementation of the functions of each module of the above system is as described in the above E-SIM tag generation method, which will not be repeated here.

[0070] The embodiment of the present invention also provides an electronic device for integrating the above-mentioned E-SIM tag generation system, such as Figure 2 As shown, the electronic device includes a memory 504, a processor 502, and a computer program stored in the memory 504 and executable on the processor 502. When the processor 502 executes the program, the steps of any of the methods for generating an E-SIM tag described above are implemented.

[0071] Among them, Figure 2 In the embodiment of the present invention, a bus architecture (represented by bus 500) is shown, which may include any number of interconnected buses and bridges, and bus 500 links various circuits including one or more processors represented by processor 502 and memory represented by memory 504. Bus 500 may also link various other circuits such as peripherals, voltage regulators, and power management circuits, which are well known in the art and are therefore not further described herein. Bus interface 505 provides an interface between bus 500 and receiver 501 and transmitter 503. Receiver 501 and transmitter 503 may be the same element, namely a transceiver, providing a unit for communicating with various other devices over a transmission medium. Processor 502 is responsible for managing bus 500 and general processing, while memory 504 may be used to store data used by processor 502 when performing operations.

[0072] An embodiment of the present invention further provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the steps of any one of the above-mentioned methods for generating an E-SIM tag are implemented.

[0073] The algorithm and display provided herein are not inherently related to any particular computer, virtual system or other device. Various general purpose systems can also be used together with the teachings based on this. According to the above description, it is obvious that the structure required for constructing such systems. In addition, the present invention is not directed to any specific programming language either. It should be understood that various programming languages ​​can be utilized to realize the content of the present invention described herein, and the description of the above specific languages ​​is for disclosing the best mode of the present invention.

[0074] In the description provided herein, a large number of specific details are described. However, it is understood that embodiments of the present invention can be practiced without these specific details. In some instances, well-known methods, structures and techniques are not shown in detail so as not to obscure the understanding of this description.

[0075] Similarly, it should be understood that in order to streamline the present disclosure and aid in understanding one or more of the various inventive aspects, in the above description of exemplary embodiments of the present invention, various features of the present invention are sometimes grouped together into a single embodiment, figure, or description thereof. However, this disclosed method should not be interpreted as reflecting the intention that the claimed invention requires more features than those explicitly recited in each claim. More specifically, as reflected in the claims below, inventive aspects lie in less than all the features of the individual embodiments disclosed above. Therefore, the claims that follow the specific embodiment are hereby expressly incorporated into the specific embodiment, with each claim itself serving as a separate embodiment of the present invention.

[0076] Those skilled in the art will appreciate that the modules in the devices in the embodiments may be adaptively changed and arranged in one or more devices different from the embodiments. The modules or units or components in the embodiments may be combined into one module or unit or component, and in addition they may be divided into a plurality of submodules or subunits or subcomponents. Except that at least some of such features and / or processes or units are mutually exclusive, all features disclosed in this specification (including the accompanying claims, abstracts and drawings) and all processes or units of any method or device disclosed in this manner may be combined in any combination. Unless otherwise expressly stated, each feature disclosed in this specification (including the accompanying claims, abstracts and drawings) may be replaced by an alternative feature providing the same, equivalent or similar purpose.

[0077] In addition, those skilled in the art will appreciate that, although some embodiments herein include certain features included in other embodiments but not other features, the combination of features of different embodiments is meant to be within the scope of the present invention and form different embodiments. For example, in the claims below, any one of the claimed embodiments may be used in any combination.

[0078] The various component embodiments of the present invention may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. It should be understood by those skilled in the art that a microprocessor or digital signal processor (DSP) may be used in practice to implement some or all of the functions of some or all of the components in the apparatus according to an embodiment of the present invention. The present invention may also be implemented as a device or device program (e.g., a computer program and a computer program product) for executing part or all of the methods described herein. Such a program implementing the present invention may be stored on a computer-readable medium, or may have the form of one or more signals. Such a signal may be downloaded from an Internet website, or provided on a carrier signal, or provided in any other form.

Claims

1. A method for generating an E-SIM tag, characterized in that: include: Obtain the network parameters, signal strength and package parameters of each operator in the user's service area; the operator's network parameters include bearer-level QoS parameters, operating frequency and network standard; The signal strength is detected by the user's terminal device; the operator is in the operator list of the user's terminal device; the package parameters include the package fee and the package data volume; the network package data volume is the amount of data uploaded or downloaded provided by the package; Input network parameters, signal strength and package parameters into a pre-trained label generation model, and the label generation model outputs the operator service package label; The operator service package label represents the characteristics of the network service package provided by the operator; the dimension of the operator service package label is smaller than the sum of the dimensions of the network parameter, signal strength and package parameter; The label generation model includes a parameter network, a signal network and a package network; the input of the parameter network is the network parameter, the input of the signal network is the signal strength, and the input of the package network is the package parameter; The training methods for the label generation model include: Obtaining a training set, the training set including a plurality of training data, the training data including training network parameters, training signal strength and training package parameters; The training network parameters, training signal strength and training package parameters are respectively input into the parameter network, signal network and package network, and the features of the training network parameters are extracted through the parameter network and counted as the first feature; the features of the training signal strength are extracted through the signal network and counted as the second feature; the features of the training package parameters are extracted through the package network and counted as the third feature; Obtaining a correlation matrix based on the first feature, the second feature, and the third feature; the correlation matrix represents the correlation between the training network parameters, the training signal strength, and the training package parameters; The loss function L of the label generation model is obtained based on the association matrix, the first loss function L1 of the parameter network, the second loss function L2 of the signal network and the third loss function L3 of the package network; If the loss function L of the label generation model converges, it is determined that the label generation model training is completed; The loss function L of the label generation model is obtained based on the association matrix, the first loss function L1, the second loss function L2 of the signal network and the third loss function L3 of the package network, which is specifically obtained by the following formula: L = a*L1+b*L2+c*L3; Among them, a, b, c are weighting factors, and a, b, c are the elements of the main diagonal in the correlation matrix; Obtaining a correlation matrix based on the first feature, the second feature, and the third feature includes: Constructing a package information relationship diagram based on the first feature, the second feature, and the third feature; Constructing a package information graph based on the training network parameters, the training signal strength and the training package parameters, specifically: setting the training network parameters, the training signal strength and the training package parameters in different rows of the matrix to obtain a package information graph; the first row, the second row and the third row of the package information graph contain the training network parameters, the training signal strength and the training package parameters respectively; if the data is not aligned, expanding the size of the package information graph so that the package information graph includes the training network parameters, the training signal strength and the training package parameters; Extract the features of the package information graph, which is counted as the fourth feature; Using the fourth feature as the convolution kernel, perform a convolution operation on the package information relationship graph to obtain a convolution matrix; Perform dimensionality reduction on the convolution matrix to obtain the correlation matrix; A package information relationship diagram is constructed based on the first feature, the second feature, and the third feature, including: The first feature, the second feature, and the third feature are set in different rows of the matrix to obtain a package information relationship graph; the first row, the second row, and the third row of the package information relationship graph contain the first feature, the second feature, and the third feature respectively; if the data dimensions of the first feature, the second feature, and the third feature are not aligned, the package information relationship graph is expanded so that the package information relationship graph includes all the first features, the second features, and the third features.

2. The E-SIM tag generation method according to claim 1, characterized in that: The method further comprises: The operator service package label is configured in the server so that the user can configure the E-SIM card and the operator service package.

3. An E-SIM tag generation system, characterized in that: The system comprises: The acquisition module is used to obtain the network parameters, signal strength and package parameters of each operator in the service area where the user is located; the network parameters of the operator include bearer-level QoS parameters, operating frequency and network standard; the signal strength is detected by the user's terminal device; the operator is in the operator list of the user's terminal device; the package parameters include the package fee and the package carrying data volume; the network package carrying data volume is the amount of data uploaded or downloaded by the package; A label generation module, used for inputting network parameters, signal strength and package parameters into a pre-trained label generation model, and the label generation model outputs an operator service package label; the operator service package label represents the characteristics of the network service package provided by the operator; the dimension of the operator service package label is smaller than the sum of the dimensions of the network parameters, signal strength and package parameters; The label generation model includes a parameter network, a signal network and a package network; the input of the parameter network is the network parameter, the input of the signal network is the signal strength, and the input of the package network is the package parameter; The training methods for the label generation model include: Obtaining a training set, the training set including a plurality of training data, the training data including training network parameters, training signal strength and training package parameters; The training network parameters, training signal strength and training package parameters are respectively input into the parameter network, signal network and package network, and the features of the training network parameters are extracted through the parameter network and counted as the first feature; the features of the training signal strength are extracted through the signal network and counted as the second feature; the features of the training package parameters are extracted through the package network and counted as the third feature; Obtaining a correlation matrix based on the first feature, the second feature, and the third feature; the correlation matrix represents the correlation between the training network parameters, the training signal strength, and the training package parameters; The loss function L of the label generation model is obtained based on the association matrix, the first loss function L1 of the parameter network, the second loss function L2 of the signal network and the third loss function L3 of the package network; If the loss function L of the label generation model converges, it is determined that the label generation model training is completed; The loss function L of the label generation model is obtained based on the association matrix, the first loss function L1, the second loss function L2 of the signal network and the third loss function L3 of the package network, which is specifically obtained by the following formula: L = a*L1+b*L2+c*L3; Among them, a, b, c are weighting factors, and a, b, c are the elements of the main diagonal in the correlation matrix; Obtaining a correlation matrix based on the first feature, the second feature, and the third feature includes: Constructing a package information relationship diagram based on the first feature, the second feature, and the third feature; Constructing a package information graph based on the training network parameters, the training signal strength and the training package parameters, specifically: setting the training network parameters, the training signal strength and the training package parameters in different rows of the matrix to obtain a package information graph; the first row, the second row and the third row of the package information graph contain the training network parameters, the training signal strength and the training package parameters respectively; if the data is not aligned, expanding the size of the package information graph so that the package information graph includes the training network parameters, the training signal strength and the training package parameters; Extract the features of the package information graph, which is counted as the fourth feature; Using the fourth feature as the convolution kernel, perform a convolution operation on the package information relationship graph to obtain a convolution matrix; Perform dimensionality reduction on the convolution matrix to obtain the correlation matrix; A package information relationship diagram is constructed based on the first feature, the second feature, and the third feature, including: The first feature, the second feature, and the third feature are set in different rows of the matrix to obtain a package information relationship graph; the first row, the second row, and the third row of the package information relationship graph contain the first feature, the second feature, and the third feature respectively; if the data dimensions of the first feature, the second feature, and the third feature are not aligned, the package information relationship graph is expanded so that the package information relationship graph includes all the first features, the second features, and the third features.

4. The E-SIM label generation system according to claim 3, characterized in that: The system further comprises: The configuration module is used to configure the operator service package label to the server so that the user can configure the E-SIM card and the operator service package.

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