Data generation method, apparatus, and related device

Abstract

The application provides a data generation method and device and related equipment, and relates to the technical field of communication. The method comprises the following steps: receiving a data generation request sent by a second network function; according to the data generation request, sending first information to a third network function and / or sending second information to a fourth network function; wherein the first information is used for triggering the generation of data required by the second network function through a twin network, and the second information is used for searching the data required by the second network function in data stored in the fourth network function. The scheme of the application solves the data problems such as data privacy cannot be sent out and sample imbalance.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of communication, in particular to a data generation method and device and related equipment. BACKGROUND

[0002] With the evolution of 5G and the in-depth study of 6G, network intelligence and endogenous artificial intelligence (AI) will become an important evolution trend of future networks, but the important factor that determines and restricts whether endogenous AI can develop rapidly is data. The training of AI models and the generation of intelligent strategies cannot be supported by precise and multi-dimensional data, especially after large models gradually empower networks, massive network data are needed to build network large models that can accurately infer.

[0003] However, on the one hand, due to data privacy protection, non-standard data of manufacturer equipment is not open, etc., some dimensional data is difficult to obtain; on the other hand, there is little network abnormal data, if more negative sample data is collected, it needs to wait for a long period, which is difficult to meet the needs of AI training, therefore, in order to promote the rapid development of endogenous AI and network large model, data is the core problem that needs to be tackled first, and data generation means is necessary. SUMMARY

[0004] The purpose of the present application is to provide a data generation method, device and related equipment, which solves the problems of data privacy, sample imbalance and other data.

[0005] To achieve the above purpose, an embodiment of the present application provides a data generation method, which is executed by a first network function, comprising:

[0006] receiving a data generation request sent by a second network function;

[0007] According to the data generation request, sending first information to a third network function, and / or sending second information to a fourth network function;

[0008] Among them, the first information is used to trigger the generation of data required by the second network function through a twin network, and the second information is used to search for the data required by the second network function in the data stored in the fourth network function.

[0009] Optionally, the data generation request includes information for indicating at least one of the following:

[0010] Twin range; artificial intelligence (AI) scene description; twin granularity; data volume; feature dimension; data threshold; generation duration.

[0011] Optionally, according to the data generation request, sending first information to a third network function comprises:

[0012] The first information is obtained by analyzing the data generation request;

[0013] The first information is sent to the third network function;

[0014] The first information includes at least one of the following: AI scene description information; and identification and topology of a twin network element.

[0015] Optionally, according to the data generation request, second information is sent to a fourth network function, including:

[0016] The second information is obtained by analyzing the data generation request;

[0017] The second information is sent to the fourth network function;

[0018] The second information includes data generation conditions.

[0019] Optionally, according to the data generation request, first information is sent to a third network function, including:

[0020] In a case where feedback information sent by the fourth network function is received, and the feedback information indicates that the fourth network function does not store data required by the second network function, according to the data generation request, first information is sent to a third network function.

[0021] Optionally, before receiving the data generation request sent by the second network function, the method further includes:

[0022] A registration request is sent to a fifth network function, the registration request including information indicating at least one of the following:

[0023] Identification of the network element; twin range; processing capacity; data generation identification.

[0024] Optionally, after receiving the data generation request sent by the second network function, the method further includes:

[0025] According to the data generation request, third information is sent to the third network function or a sixth network function, the third information including at least one of the following:

[0026] Data volume; feature dimension; data threshold; and generation duration.

[0027] To achieve the above purpose, an embodiment of the present application provides a data generation method, executed by a third network function, including:

[0028] First information sent by a first network function is received;

[0029] generate the twin network according to the first information.

[0030] Optionally, the first information comprises at least one of: AI scene description information; and identification and topology of the twin network element.

[0031] Optionally, generating the twin network according to the first information comprises:

[0032] In a case where the first information comprises AI scene description information, querying whether a matching general template exists based on the AI scene description information, and in a case where the matching general template exists, generating the twin network using the general template; or,

[0033] In a case where the first information comprises identification and topology of the twin network element, and in a case where no matching general template exists or the first information does not comprise AI scene description information, generating the twin network based on the identification and topology of the twin network element.

[0034] Optionally, the method further comprises:

[0035] receiving third information sent by the first network function;

[0036] sending the third information to a sixth network function;

[0037] The third information comprises at least one of:

[0038] data volume; feature dimension; data threshold; and generation duration.

[0039] Optionally, generating the twin network according to the first information comprises:

[0040] In a case where a plurality of first information corresponding to different data generation requests are received, generating a twin network of each of the first information in parallel based on the plurality of first information.

[0041] To achieve the above object, an embodiment of the present application provides a data generation method, executed by a sixth network function, comprising:

[0042] receiving third information sent by a first network function or a third network function;

[0043] configuring a twin network according to the third information to obtain data required by a second network function;

[0044] The third information comprises at least one of:

[0045] data volume; feature dimension; data threshold; and generation duration.

[0046] Optionally, after the configuring the twin network according to the third information to obtain the data required by the second network function, the method further includes:

[0047] sending the data to the second network function.

[0048] Optionally, after the configuring the twin network according to the third information to obtain the data required by the second network function, the method further includes:

[0049] sending the data and the third information to a fourth network function.

[0050] To achieve the above object, an embodiment of the present application provides a data generation method, executed by a fourth network function, including:

[0051] receiving data and third information sent by a fifth network function;

[0052] storing the data and the third information in association;

[0053] wherein the third information includes at least one of the following:

[0054] data volume; feature dimension; data threshold; generation duration.

[0055] Optionally, the storing the data and the third information in association includes:

[0056] storing the received data and the third information in association in a case where there is no data associated with the third information in the currently stored data.

[0057] Optionally, the method further includes:

[0058] deleting the received data in a case where there is data associated with the third information in the currently stored data.

[0059] To achieve the above object, an embodiment of the present application provides a data generation method, executed by a second network function, including:

[0060] sending a data generation request to a first network function, the data generation request being used to request data required by the second network function;

[0061] receiving data sent by a fourth network function or a sixth network function.

[0062] Optionally, the data generation request includes information used to indicate at least one of the following:

[0063] twin range; artificial intelligence AI scene description; twin granularity; data volume; feature dimension; data threshold; generation duration.

[0064] Optionally, before sending the data generation request to the first network function, the method further comprises:

[0065] sending a discovery request to a fifth network function;

[0066] receiving target address information fed back by the fifth network function;

[0067] The discovery request comprises information indicating at least one of:

[0068] twin range; processing capability; data generation identifier.

[0069] To achieve the above object, an embodiment of the present application provides a data generation method, executed by a fifth network function, comprising:

[0070] receiving a registration request sent by a first network function, the registration request comprising information indicating at least one of:

[0071] identifier of the network element; twin range; processing capability; data generation identifier.

[0072] Optionally, the method further comprises:

[0073] receiving a discovery request sent by a second network function;

[0074] determining a matched first network function according to the discovery request;

[0075] sending target address information of the first network function to the second network function;

[0076] The discovery request comprises information indicating at least one of:

[0077] twin range; processing capability; data generation identifier.

[0078] To achieve the above object, an embodiment of the present application provides a data generation apparatus, applied to a first network function, comprising:

[0079] a first receiving module, configured to receive a data generation request sent by a second network function;

[0080] a first sending module, configured to send first information to a third network function and / or send second information to a fourth network function according to the data generation request;

[0081] The first information is used to trigger generation of data required by the second network function through a twin network, and the second information is used to search for the data required by the second network function in data stored by the fourth network function.

[0082] Optionally, the data generation request comprises information for indicating at least one of:

[0083] twin range; artificial intelligence (AI) scenario description; twin granularity; data volume; feature dimension; data threshold; generation duration.

[0084] Optionally, the first sending module is further configured to:

[0085] obtain the first information by analyzing the data generation request;

[0086] send the first information to the third network function;

[0087] wherein the first information comprises at least one of: AI scenario description information; and identification and topology structure of a twin network element.

[0088] Optionally, the first sending module is further configured to:

[0089] obtain the second information by analyzing the data generation request;

[0090] send the second information to the fourth network function;

[0091] wherein the second information comprises data generation conditions.

[0092] Optionally, the first sending module is further configured to:

[0093] in a case where feedback information sent by the fourth network function is received and the feedback information indicates that the fourth network function does not store data required by the second network function, send first information to a third network function according to the data generation request.

[0094] Optionally, the apparatus further comprises:

[0095] a registration module configured to send a registration request to a fifth network function, the registration request comprising information for indicating at least one of:

[0096] identification of a network element; twin range; processing capability; data generation identifier.

[0097] Optionally, the apparatus further comprises:

[0098] a third sending module configured to send third information to the third network function or a sixth network function according to the data generation request, the third information comprising at least one of:

[0099] data volume; feature dimension; data threshold; generation duration.

[0100] To achieve the above object, embodiments of the present application provide a data generation apparatus applied to a third network function, comprising:

[0101] a second receiving module configured to receive first information sent by a first network function;

[0102] a first processing module configured to generate a twin network according to the first information.

[0103] Optionally, the first information comprises at least one of the following: AI scene description information; and identification and topology structure of a twin network element.

[0104] Optionally, the first processing module is further configured to:

[0105] in a case where the first information comprises AI scene description information, query whether there is a matched general template based on the AI scene description information, and in a case where there is a matched general template, generate a twin network using the general template; or,

[0106] in a case where the first information comprises identification and topology structure of a twin network element, and there is no matched general template or the first information does not comprise AI scene description information, generate a twin network based on the identification and topology structure of the twin network element.

[0107] Optionally, the apparatus further comprises:

[0108] a seventh receiving module configured to receive third information sent by the first network function;

[0109] a fourth sending module configured to send the third information to a sixth network function;

[0110] wherein the third information comprises at least one of the following:

[0111] data volume; feature dimension; data threshold; and generation duration.

[0112] Optionally, the first processing module is further configured to:

[0113] in a case where a plurality of the first information corresponding to different data generation requests is received, generate a twin network of each of the first information in parallel based on the plurality of the first information.

[0114] To achieve the above object, embodiments of the present application provide a data generation apparatus applied to a sixth network function, comprising:

[0115] a third receiving module configured to receive third information sent by a first network function or a third network function;

[0116] The second processing module is configured to configure a twin network according to the third information, and obtain data required by the second network function.

[0117] The third information includes at least one of the following:

[0118] The data amount, the feature dimension, the data threshold, and the generation duration.

[0119] Optionally, the apparatus further includes:

[0120] The fifth sending module is configured to send the data to the second network function.

[0121] Optionally, the apparatus further includes:

[0122] The sixth sending module is configured to send the data and the third information to a fourth network function.

[0123] To achieve the above object, an embodiment of the present application provides a data generation apparatus applied to a fourth network function, comprising:

[0124] The fourth receiving module is configured to receive data and third information sent by a fifth network function.

[0125] The storage module is configured to store the data and the third information in association.

[0126] The third information includes at least one of the following:

[0127] The data amount, the feature dimension, the data threshold, and the generation duration.

[0128] Optionally, the storage module is further configured to:

[0129] In a case where there is no data associated with the third information in the currently stored data, store the received data and the third information in association.

[0130] Optionally, the apparatus further includes:

[0131] The third processing module is configured to delete the received data in a case where there is data associated with the third information in the currently stored data.

[0132] To achieve the above object, an embodiment of the present application provides a data generation apparatus applied to a second network function, comprising:

[0133] The second sending module is configured to send a data generation request to a first network function, the data generation request being used to request data required by the second network function.

[0134] The fifth receiving module is configured to receive data sent by a fourth network function or a sixth network function.

[0135] Optionally, the data generation request comprises information indicating at least one of:

[0136] twin range; artificial intelligence AI scene description; twin granularity; data volume; feature dimension; data threshold; generation duration.

[0137] Optionally, the apparatus further comprises:

[0138] a discovery module configured to send a discovery request to a fifth network function;

[0139] an eighth receiving module configured to receive target address information fed back by the fifth network function;

[0140] Optionally, the discovery request comprises information indicating at least one of:

[0141] twin range; processing capability; data generation identifier.

[0142] To achieve the above object, embodiments of the present application provide a data generation apparatus applied to a fifth network function, comprising:

[0143] a sixth receiving module configured to receive a registration request sent by a first network function, the registration request comprising information indicating at least one of:

[0144] identifier of the network element; twin range; processing capability; data generation identifier.

[0145] Optionally, the apparatus further comprises:

[0146] a ninth receiving module configured to receive a discovery request sent by a second network function;

[0147] a fourth processing module configured to determine a matched first network function according to the discovery request;

[0148] a sixth sending module configured to send target address information of the first network function to the second network function;

[0149] Optionally, the discovery request comprises information indicating at least one of:

[0150] twin range; processing capability; data generation identifier.

[0151] To achieve the above object, embodiments of the present application provide a network element comprising a first network function for executing the data generation method as described above, a third network function for executing the data generation method as described above, a sixth network function for executing the data generation method as described above, and a fourth network function for executing the data generation method as described above.

[0152] Optionally, a first interface is arranged between the first network function and the third network function, a second interface is arranged between the third network function and the sixth network function, a third interface is arranged between the first network function and the sixth network function, a fourth interface is arranged between the first network function and the fourth network function, and a fifth interface is arranged between the sixth network function and the fourth network function.

[0153] To achieve the above object, the embodiment of the present application provides a data generation system, comprising the network element, the second network function performing the data generation method, and the fifth network function performing the data generation method.

[0154] To achieve the above object, the embodiment of the present application provides a network device, comprising a transceiver, a processor, a memory, and a program or instructions stored in the memory and executable on the processor; wherein the processor implements the data generation method when executing the program or instructions.

[0155] To achieve the above object, the embodiment of the present application provides a readable storage medium, which stores a program or instructions, and the program or instructions are executable on a processor to implement the steps of the data generation method.

[0156] To achieve the above object, the embodiment of the present application provides a computer program product, which comprises computer instructions executable on a processor to implement the steps of the data generation method.

[0157] The beneficial effects of the above technical solutions of the present application are as follows:

[0158] The method of the embodiment of the present application receives a data generation request of a second network function, and after understanding the data demand of the second network function, sends first information to a third network function according to the data generation request to trigger the twin network to generate the required data, and / or sends second information to a fourth network function to search for the required data in the data stored in the fourth network function, thereby solving the data problems of data privacy not being able to be sent out and sample imbalance. BRIEF DESCRIPTION OF DRAWINGS

[0159] Figure 1 One of the flowcharts of the data generation method of the embodiment of the present application;

[0160] Figure 2 The structural schematic diagram of the network digital twin network element of the embodiment of the present application;

[0161] Figure 3 The starting and orchestration flowchart of the third network function in the embodiment of the present application;

[0162] Figure 4 Application diagram of the method of the embodiment of the present application;

[0163] Figure 5 Flowchart No. 2 of the data generation method of the embodiment of the present application;

[0164] Figure 6 Flowchart No. 3 of the data generation method of the embodiment of the present application;

[0165] Figure 7 Flowchart No. 4 of the data generation method of the embodiment of the present application;

[0166] Figure 8 Flowchart No. 5 of the data generation method of the embodiment of the present application;

[0167] Figure 9 Flowchart No. 6 of the data generation method of the embodiment of the present application;

[0168] Figure 10 Module structure diagram of the Figure 1

[0169] Module structure diagram of the Figure 11 Figure 5 Module structure diagram of the

[0170] Figure 12 Figure 6 Module structure diagram of the

[0171] Figure 13 Module structure diagram of the Figure 7

[0172] Module structure diagram of the Figure 14 Figure 8 Module structure diagram of the

[0173] Figure 15 Figure 9 Module structure diagram of the

[0174] Figure 16 Structure diagram of the network device of the embodiment of the present application. DETAILED DESCRIPTION

[0175] To make the technical problems, technical solutions and advantages to be solved by the present application more clear, the following will be described in detail with reference to the drawings and specific embodiments.

[0176] ​​​​It should be understood that every technical feature mentioned in the specification refers to a specific feature of the embodiments, which is included in at least one embodiment of the present application. Therefore, "in one embodiment" or "in an embodiment" appearing throughout the specification does not necessarily refer to the same embodiment. In addition, these specific features, structures or characteristics can be combined in one or more embodiments in any suitable manner.

[0177] In various embodiments of the present application, it should be understood that the size of the serial number of the following processes does not mean the order of execution, and the execution order of the processes should be determined by its function and inherent logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0178] In addition, the terms "system" and "network" are often used interchangeably herein.

[0179] In the embodiments provided in the present application, it should be understood that "B corresponding to A" means that B is associated with A, and B can be determined according to A. However, it should also be understood that the determination of B according to A does not mean that B is determined only according to A, but B can also be determined according to A and / or other information.

[0180] For the convenience of understanding, some contents related to the embodiments of the present application are described as follows:

[0181] At present, the network data analysis function (NWDAF) proposed for network intelligence is limited in terms of network intelligence due to data privacy and other restrictions.

[0182] The digital twin network obtains a digital model of the entire network in the virtual space by fine modeling of each network entity and function, which can be virtually mapped with the physical network, and can reproduce all or part of the functions of the physical network in a certain proportion. Therefore, through digital twinning, network data can be simulated and generated under different network conditions for AI model training, thereby solving data problems such as data privacy cannot be exported and sample imbalance.

[0183] As shown in FIG. 1, a data generation method according to an embodiment of the present application is executed by a first network function, and includes the following steps. Figure 1 Step 11, receiving a data generation request sent by a second network function;

[0184] Step 12, according to the data generation request, sending first information to a third network function, and / or sending second information to a fourth network function;

[0185]

[0186] ​The first information is used to trigger generation of the data required by the second network function by the twin network, and the second information is used to search for the data required by the second network function in the data stored by the fourth network function.

[0187] That is, the first network function generates a data generation request of the second network function, and after learning the data requirement of the second network function, sends the first information to the third network function according to the data generation request to trigger the twin network to generate the required data, and / or sends the second information to the fourth network function to search for the required data in the data stored by the fourth network function, thereby solving the data problems such as data privacy cannot be exported and sample imbalance.

[0188] Optionally, the second network function can be an NWDAF, and the NWDAF subsequently obtains the data and uses the data for AI model training, thereby solving the problem of poor generalization of the AI model caused by data loss.

[0189] Of course, the second network function is not limited to the NWDAF, but can also be other network functions that require data, which are not listed one by one here.

[0190] Optionally, in the embodiment, the first network function can be a network function of a network digital twin (NDT) network element, and the first network function can be referred to as a data instruction parsing function (DIPF). The NDT network element further includes a third network function, a fourth network function, and a sixth network function. The third network function can be referred to as a data generation orchestration function (DGOF), the fourth network function can be referred to as a data generation function (DGF), and the sixth network function can be referred to as a data storage function (DSF).

[0191] It should be noted that in the embodiment, the NDT network element mainly constructs a twin body of a physical network through a digital twin technology, and according to different modeling accuracies, the functions and performances of the physical network can be reproduced to different degrees, thereby being able to simulate and generate network data. Specifically, different types of data are generated under different threshold conditions to meet the multi-dimensional requirements of AI model training. The structure of the NDT network element is as follows: Figure 2As shown, a first interface (Ig1 interface) is arranged between the first network function (DIPF) and the third network function (DGOF), a second interface (Gg interface) is arranged between the third network function (DGOF) and the sixth network function (DGF), a third interface (Ig2 interface) is arranged between the first network function (DIPF) and the sixth network function (DGF), a fourth interface (Is interface) is arranged between the first network function (DIPF) and the fourth network function (DSF), and a fifth interface (Gs interface) is arranged between the sixth network function (DGF) and the fourth network function (DSF).

[0192] Optionally, the data generation request includes information for indicating at least one of:

[0193] Twin range; artificial intelligence AI scene description; twin granularity; data volume; feature dimension; data threshold; generation duration.

[0194] The twin range (Twin Domain) indicates a network domain of the twin, and can include one or more of a terminal, an access network, and a core network.

[0195] The AI scene description (AI Scene description) is mainly used for scene description, and the network element node and topology information required by the twin can be obtained through the transmission message, so that the twin environment is accurately constructed, and real data closer to the physical network is generated.

[0196] The twin granularity (Twin granularity) indicates the granularity of the twin possessed by the NDT, and can be fine to what extent. The parameter can be used to judge the granularity of the data simulated and generated by the NDT, so as to reduce the waste of network and computing resources.

[0197] The data volume (Data volume) indicates the required data quantity of the second network function.

[0198] The feature dimension (Feature dimension) indicates the feature dimension of the data generation, and the specific feature dimension required is clear, so that the NDT can be more clear about the features to be generated, thereby reducing unnecessary data generation, improving data generation efficiency, and reducing storage space waste.

[0199] The threshold setting (Threshold setting) indicates the threshold condition setting of the data generation. The parameter can be used to flexibly define the conditions of the required data, so as to solve the problems of poor model generalization caused by difficult acquisition of negative sample data and sample imbalance.

[0200] The generation duration indicates a duration of data generation.

[0201] As an implementation manner, the data generation request comprises request information, which can be:

[0202]

[0203] Optionally, according to the data generation request, the first information is sent to a third network function, comprising:

[0204] The first information is obtained by analyzing the data generation request;

[0205] The first information is sent to the third network function;

[0206] The first information comprises at least one of the following: AI scene description information, and identification and topology structure of a twin network element.

[0207] That is, the first network function analyzes the data generation request to obtain AI scene description information and / or identification and topology structure of a twin network element (Twin entity ID(s)&topology) after receiving the data generation request.

[0208] In an implementation manner, the DIPF can retrieve a network domain from a Twin Domain, and retrieve and characterize network elements and topological relationships involved in an AI scene according to an AIScenedescription to obtain Twin entity ID(s)&topology.

[0209] Of course, the first information can also comprise one or more of twin granularity, data volume, feature dimension, data threshold, and generation duration.

[0210] After receiving the first information, the third network function can generate a twin network according to the first information, so that the sixth network function further configures the twin network to simulate generation of data required by the second network function.

[0211] Optionally, the third network function can set a general template based on one or more AI scenes in order to quickly respond to data request services. For example, one AI scene corresponds to one general template, and the general template has set a twin process. Therefore, when the first information comprises AI scene description information, it is determined whether there is a matching general template based on the AI scene description information, and the twin network is generated using the general template when there is a matching general template.

[0212] The general template can be understood as a prefabricated orchestration template. The third network function can retrieve a general template matching the received AI scene description information from a prefabricated template library, start a twin process of the matching general template, and generate a twin network.

[0213] Optionally, in a case where the first information includes an identifier and a topology of a twin network element, and there is no matching general template or the first information does not include AI scene description information, the third network function generates a twin network based on the identifier and the topology of the twin network element.

[0214] That is, the third network function can directly start custom orchestration based on the Twin entity ID(s) & topology to generate a twin network. Of course, the third network function can first retrieve whether there is a general template matching the received AI scene description information in the prefabricated template library based on the AI scene description information, and if not, start custom orchestration based on the Twin entity ID(s) & topology to generate a twin network.

[0215] Optionally, in a case where the third network function receives a plurality of first information corresponding to different data generation requests, the third network function generates a twin network of each of the first information in parallel based on the plurality of first information.

[0216] That is, the second network function can send a plurality of data generation requests at the same time, and after being parsed by the first network function, the third network function can process the plurality of data generation requests in parallel.

[0217] Thus, from the above, in this embodiment, the third network function sets a prefabricated orchestration template + flexible custom orchestration combined twin network orchestration method suitable for multi-process service, for parallel data generation service of multiple instructions (data generation requests). As shown in Figure 3 When the first information is received, if the AI scene description information of the first information matches the scene in the general template, the twin network is built based on the prefabricated general template, and the twin body parameters are instantiated. When the received instruction does not match the general template and there is a personalized customization requirement, the orchestration process is started according to the instruction to generate a twin network.

[0218] Optionally, in this embodiment, the first information is sent to the third network function according to the data generation request, including:

[0219] In a case where the feedback information sent by the fourth network function is received, and the feedback information indicates that the fourth network function does not store the data required by the second network function, the first information is sent to the third network function according to the data generation request.

[0220] That is, the first network function sends the second information to the fourth network function first to query the data required by the second network function, so that the first information is sent to the third network function in the case that the fourth network function does not store the data required by the second network function.

[0221] Optionally, in this embodiment, the second information is sent to the fourth network function according to the data generation request, including:

[0222] The second information is obtained by analyzing the data generation request;

[0223] The second information is sent to the fourth network function;

[0224] The second information includes data generation conditions.

[0225] That is, the first network function analyzes the data generation request after receiving the data generation request to obtain the data generation conditions, so that the fourth network function uses the data generation conditions to retrieve the data required by the second network function in the stored data.

[0226] Optionally, the data generation conditions include but are not limited to at least one of the following: data volume, feature dimension, data threshold, and generation duration. At this time, the content of the data generation conditions is the corresponding information of the data required by the second network function.

[0227] Optionally, in this embodiment, before receiving the data generation request sent by the second network function, it further includes:

[0228] A registration request is sent to the fifth network function, and the registration request includes information indicating at least one of the following:

[0229] The identifier of the network element, the twin range, the processing capability, and the data generation identifier.

[0230] In this way, the NDT network element can be registered with the fifth network function through the first network function, so that the fifth network function matches the applicable NDT network element for the second network function after receiving the discovery request of the second network function.

[0231] The registration request includes registration information (Registration information), indicating at least one of the following: identity of the network element (NDT IP), twin range (Twin Domain), processing capacity, data generation identifier (Data generation identifier). Wherein Twin Domain represents the scale of the twin of NDT, which covers which network elements and network domains. The processing capacity represents the ability of multi-thread processing of NDT, which can be used to determine how many parallel processes (i.e. how many twin networks are generated in parallel) can be run simultaneously. Data generation identifier represents the identification of data generation type.

[0232] The fifth network function matches the target NDT network element based on the registration information of the registered NDT network element after receiving the discovery request of the second network function, and feeds back the target address information, such as the IP of the target NDT network element, to the fifth network function. In this way, the second network function can send a data generation request to the first network function of the target NDT network element through the IP.

[0233] The discovery request includes information indicating at least one of the following: twin range; processing capacity; data generation identifier. Here, the meanings of twin range, processing capacity, and data generation identifier are consistent with those in the registration information.

[0234] Optionally, in this embodiment, after receiving the data generation request sent by the second network function, it further includes:

[0235] According to the data generation request, the third information is sent to the third network function or the sixth network function, and the third information includes at least one of the following:

[0236] Data volume; feature dimension; data threshold; generation duration.

[0237] That is, the first network function parses the data generation request and can directly send the third information to the sixth network function, or forwards the third information to the sixth network function via the third network function. After receiving the third information, the sixth network function configures the twin network based on the third information to obtain the data required by the second network function.

[0238] Specifically, the sixth network function translates the third information into network configuration and configures it in the twin network. For example, if network congestion data caused by insufficient network bandwidth needs to be generated, Threshold setting is required, and the required data volume, feature dimension, and occurrence duration are configured, and then the whole twin process is started for data generation.

[0239] Optionally, after the sixth network function obtains the data required by the second network function, the sixth network function can send the data to the second network function. Of course, the sixth network function can also send the data and the third information to the fourth network function, and store the data and the third information jointly, so as to avoid repeated generation of data when a request is generated for the same data, and reduce resource waste. Moreover, if there is matching data, the generation process is not needed, and the data is directly sent to the requester; if not, the data generation process is started, so that the process is simplified. At this time, the third information can be referred to as generation conditions.

[0240] Optionally, after the fourth network function receives the data and the third information sent by the sixth network function, the fourth network function can first determine whether there is data associated with the third information in the currently stored data, and if not, the associated storage is performed; if not, the received data is deleted, so as to reduce the space occupation.

[0241] The associated storage is to combine the third information and the data into one, that is, {generation conditions and corresponding data}. The storage format can be JSON, XML, etc.

[0242] Next, the application of the method of the embodiments of the present application will be described in combination with Figure 4 The second network device is taken as an example of NWDAF, and the fifth network device is taken as an example of network repository function (NRF).

[0243] Step 1: The NDT network element registers with the NRF through the DIPF, and the registration request at least includes {NDT IP, Data generation identifier, Twin Domain, processing capacity…}.

[0244] Step 2: The NWDAF sends a discovery request (NDT_Discovery_Request) to the NRF, which can include {Data generation identifier, Twin Domain, processing capacity}.

[0245] Step 3: The NRF sends a discovery response (NDT_Response) to the NWDAF. The NRF selects a corresponding NDT according to the registration request and the discovery request, and returns the IP to the NWDAF;

[0246] Step 4: The NWDAF sends a data generation request to the DIPF of the NDT according to the IP, including but not limited to

[0247]

[0248] Step5: DIPF completes data generation request analysis and obtains the first information, the second information, and the third information.

[0249] Step6: DIPF sends the second information to the DSF, and first searches whether there is data that can be met in the stored data in the DSF based on the second information according to {generation conditions}. If yes, the generation is not needed, and the data is directly sent to the requester. If no, the data generation process is started, and the DIPF sends the first information to the DGOF.

[0250] Step7: If the output in step6 is “None”, the DIPF starts and orchestrates: searches in the prefabricated template library, judges whether there is a matching general template, if yes, starts the matching template twin process to generate the twin network; if no, starts the custom orchestration process to generate the twin network based on the Twin entity ID(s) & topology. If there are multiple data generation requests at the same time, multiple twin service processes are orchestrated and started.

[0251] Step8: Based on step7, the DGF configures the twin network based on the third information to simulate the generated data. The DGF sends the generated data to the NWDAF. The DGF also sends the task completion notification to the DGOF, and the DGOF ends the data generation service process of multiple processes and releases the related network and computing resources.

[0252] Step9: The DGF sends the third information and the generated data ({Generation conditions, Corresponding data}) to the DSF, and the DSF judges whether it has been stored through the generation condition. If yes, the received data is deleted. If no, the {generation conditions and corresponding data} are stored.

[0253] In addition, in this embodiment, in the NDT network element:

[0254] The first interface is the interface between the DIPF and the DGOF, and is mainly used for transmitting the analyzed first information;

[0255] The second interface is the interface between DGOF and DGF, mainly used to transmit conditional messages for data generation, i.e., the third information. Its specific format is as follows: {Data volume: M, Feature dimension: N, Threshold setting: =} <X%,Generation duration:T1-T2…};

[0256] The third interface: This is the interface between DIPF and DGF, mainly used to transmit conditional messages for data generation, i.e., third information. The specific format is as follows: {Data volume: M, Feature dimension: N, Threshold setting: = <X%,Generation duration:T1-T2…};

[0257] The fourth interface is the interface between DIPF and DSF. It is mainly used to transmit the data generation condition message parsed by DIPF, i.e., the second information, which is used to retrieve and determine whether there is corresponding data stored in DSF.

[0258] The fifth interface is the interface between DSF and DGF, mainly used to transmit data generation conditions and corresponding generation data. The specific format is as follows: {Generation Condition: Corresponding Data}.

[0259] In addition, in this embodiment, the interface (Nndt interface) between the NDT network element and the second network function is used to transmit the aforementioned data generation request, and the specific format is as follows:

[0260]

[0261] In summary, the method of this application embodiment, through digital twins, can simulate and generate network data under different network conditions for AI model training, thereby solving data problems such as data privacy and imbalanced samples.

[0262] like Figure 5 As shown, a data generation method according to an embodiment of this application, executed by a third network function, includes:

[0263] Step 51: Receive the first information sent by the first network function;

[0264] Step 52: Generate a twin network based on the first information.

[0265] In this way, the third network function, according to steps 51 and 52, can generate a twin network based on the first information sent by the first network function, so that the required data can be obtained through the twin network in the future, thus solving data problems such as data privacy and imbalanced samples.

[0266] Optionally, the first information includes at least one of the following: AI scene description information; the identifier and topology of the twin network element.

[0267] Optionally, generating a twin network based on the first information includes:

[0268] If the first information includes AI scene description information, a query is performed based on the AI ​​scene description information to see if a matching general template exists. If a matching general template exists, a Siamese network is generated using the general template; or,

[0269] If the first information includes the identifier and topology of the twin network element, and there is no matching general template, or if the first information does not include AI scene description information, a twin network is generated based on the identifier and topology of the twin network element.

[0270] Optionally, it also includes:

[0271] Receive the third information sent by the first network function;

[0272] Send the third information to the sixth network function;

[0273] The third information includes at least one of the following:

[0274] Data volume; feature dimensions; data threshold; generation duration.

[0275] Optionally, generating a twin network based on the first information includes:

[0276] When multiple pieces of the first information corresponding to different data generation requests are received, a twin network for each piece of the first information is generated in parallel based on the multiple pieces of the first information.

[0277] It should be noted that this method is implemented in conjunction with the data generation method of the above embodiments. The implementation of the above method embodiments is applicable to this method and can achieve the same technical effect.

[0278] like Figure 6 As shown, an embodiment of this application provides a data generation method, executed by a sixth network function, including:

[0279] Step 61: Receive third information sent by the first network function or the third network function;

[0280] Step 62: Configure the twin network according to the third information to obtain the data required for the second network function; wherein the third information includes at least one of the following:

[0281] Data volume; feature dimensions; data threshold; generation duration.

[0282] In this way, after receiving the third information, the sixth network function uses the third information to configure the twin network information, thereby obtaining the required data and solving data problems such as data privacy and imbalanced samples.

[0283] Among them, the twin network is generated by the third network function based on the first information sent by the first network function.

[0284] Optionally, after configuring the twin network based on the third information to obtain the data required for the second network function, the method further includes:

[0285] The data is sent to the second network function.

[0286] Optionally, after configuring the twin network based on the third information to obtain the data required for the second network function, the method further includes:

[0287] The data and the third information are sent to the fourth network function.

[0288] It should be noted that this method is implemented in conjunction with the data generation method of the above embodiments. The implementation of the above method embodiments is applicable to this method and can achieve the same technical effect.

[0289] like Figure 7 As shown, an embodiment of this application provides a data generation method, executed by a fourth network function, including:

[0290] Step 71: Receive data and third information sent by the fifth network function;

[0291] Step 72: Associate and store the data and the third information.

[0292] The third information includes at least one of the following:

[0293] Data volume; feature dimensions; data threshold; generation duration.

[0294] Thus, after the fourth network function associates and stores the data received from the fifth network function with the third information according to steps 71 and 72, the first network function can easily query the required data based on the storage.

[0295] Optionally, the data and the third information are stored together, including:

[0296] If no data associated with the third information exists in the current stored data, the received data and the third information will be associated and stored together.

[0297] Optionally, it also includes:

[0298] If data associated with the third information exists in the current stored data, the received data will be deleted.

[0299] It should be noted that this method is implemented in conjunction with the data generation method of the above embodiments. The implementation of the above method embodiments is applicable to this method and can achieve the same technical effect.

[0300] like Figure 8 As shown, an embodiment of this application provides a data generation method, executed by a second network function, including:

[0301] Step 81: Send a data generation request to the first network function, the data generation request being used to request the data required by the second network function;

[0302] Step 82: Receive data sent by the fourth or sixth network function.

[0303] That is, according to steps 81 and 82, the second network function will request the required data from the first network function through a data generation request, thereby receiving the required data sent by the fourth or sixth network function.

[0304] The first network function will send first information to the third network function and / or send second information to the fourth network function after receiving a data generation request from the second network function. The first information is used to trigger the generation of data required by the second network function through the twin network, and the second information is used to retrieve the data required by the second network function from the data stored in the fourth network function.

[0305] Optionally, the data generation request includes information indicating at least one of the following:

[0306] Twin scope; AI scenario description; twin granularity; data volume; feature dimension; data threshold; generation duration.

[0307] Optionally, before sending the data generation request to the first network function, the method further includes:

[0308] Send a discovery request to the fifth network function;

[0309] Receive the target address information fed back by the fifth network function;

[0310] The discovery request includes information indicating at least one of the following:

[0311] Twin scope; processing capacity; data generation identifier.

[0312] It should be noted that this method is implemented in conjunction with the data generation method of the above embodiments. The implementation of the above method embodiments is applicable to this method and can achieve the same technical effect.

[0313] like Figure 9 As shown, an embodiment of this application provides a data generation method, executed by a fifth network function, including:

[0314] Step 91: Receive a registration request sent by the first network function, the registration request including information indicating at least one of the following:

[0315] The identifier of the network element; the scope of the twin; the processing capacity; and the data generation identifier.

[0316] That is, the fifth network function will receive the registration request sent by the first network function, and the first network function will register so that it can be matched with the appropriate first network function in the future.

[0317] Optionally, it also includes:

[0318] Receive discovery requests sent by the second network function;

[0319] Based on the discovery request, determine the first matching network function;

[0320] Send the target address information of the first network function to the second network function;

[0321] The discovery request includes information indicating at least one of the following:

[0322] Twin scope; processing capacity; data generation identifier.

[0323] It should be noted that this method is implemented in conjunction with the data generation method of the above embodiments. The implementation of the above method embodiments is applicable to this method and can achieve the same technical effect.

[0324] like Figure 10 As shown, an embodiment of this application provides a data generation apparatus applied to a first network function, including:

[0325] The first receiving module 1010 is used to receive a data generation request sent by the second network function;

[0326] The first sending module 1020 is used to generate a request based on the data, send first information to the third network function, and / or send second information to the fourth network function;

[0327] The first information is used to trigger the generation of the data required for the second network function through the twin network, and the second information is used to retrieve the data required for the second network function from the data stored in the fourth network function.

[0328] The device receives a data generation request from a second network function. After understanding the data requirements of the second network function, it sends a first message to a third network function to trigger the twin network to generate the required data; and / or sends a second message to a fourth network function to retrieve the required data from the data stored in the fourth network function. This solves data problems such as data privacy and imbalanced samples.

[0329] Optionally, the data generation request includes information indicating at least one of the following:

[0330] Twin scope; AI scenario description; twin granularity; data volume; feature dimension; data threshold; generation duration.

[0331] Optionally, the first sending module is further configured to:

[0332] The first information is obtained by parsing the data to generate the request;

[0333] Send the first information to the third network function;

[0334] The first information includes at least one of the following: AI scene description information; the identifier and topology of the twin network element.

[0335] Optionally, the first sending module is further configured to:

[0336] The second information is obtained by parsing the data generation request;

[0337] The second information is sent to the fourth network function;

[0338] The second information includes data generation conditions.

[0339] Optionally, the first sending module is further configured to:

[0340] Upon receiving feedback information from the fourth network function, and the feedback information indicating that the fourth network function has not stored the data required by the second network function, the first information is sent to the third network function according to the data generation request.

[0341] Optionally, the device further includes:

[0342] The registration module is used to send a registration request to the fifth network function, the registration request including information indicating at least one of the following:

[0343] The identifier of the network element; the scope of the twin; the processing capacity; and the data generation identifier.

[0344] Optionally, the device further includes:

[0345] The third sending module is configured to generate a request based on the data and send third information to the third network function or the sixth network function, wherein the third information includes at least one of the following:

[0346] Data volume; feature dimensions; data threshold; generation duration.

[0347] It should be noted that the device uses the data generation method executed by the first network function described above. The implementation of the above method embodiment is applicable to this device and can achieve the same technical effect.

[0348] like Figure 11 As shown, an embodiment of this application provides a data generation apparatus applied to a third network function, including:

[0349] The second receiving module 1110 is used to receive the first information sent by the first network function;

[0350] The first processing module 1120 is used to generate a twin network based on the first information.

[0351] The device can generate a twin network based on the first information sent by the first network function, so that the required data can be obtained through the twin network in the future, thus solving data problems such as data privacy and imbalanced samples.

[0352] Optionally, the first information includes at least one of the following: AI scene description information; the identifier and topology of the twin network element.

[0353] Optionally, the first processing module is further configured to:

[0354] If the first information includes AI scene description information, a query is performed based on the AI ​​scene description information to see if a matching general template exists. If a matching general template exists, a Siamese network is generated using the general template; or,

[0355] If the first information includes the identifier and topology of the twin network element, and there is no matching general template, or if the first information does not include AI scene description information, a twin network is generated based on the identifier and topology of the twin network element.

[0356] Optionally, the device further includes:

[0357] The seventh receiving module is used to receive the third information sent by the first network function;

[0358] The fourth sending module is used to send the third information to the sixth network function;

[0359] The third information includes at least one of the following:

[0360] Data volume; feature dimensions; data threshold; generation duration.

[0361] Optionally, the first processing module is further configured to:

[0362] When multiple pieces of the first information corresponding to different data generation requests are received, a twin network for each piece of the first information is generated in parallel based on the multiple pieces of the first information.

[0363] It should be noted that the device uses the data generation method executed by the third network function described above. The implementation of the above method embodiment is applicable to this device and can achieve the same technical effect.

[0364] like Figure 12 As shown, an embodiment of this application provides a data generation apparatus applied to a sixth network function, including:

[0365] The third receiving module 1210 is used to receive third information sent by the first network function or the third network function;

[0366] The second processing module 1220 is used to configure the twin network according to the third information and obtain the data required for the second network function.

[0367] The third information includes at least one of the following:

[0368] Data volume; feature dimensions; data threshold; generation duration.

[0369] After receiving the third information, the device uses the third information to configure the twin network information, thereby obtaining the required data and solving data problems such as data privacy and imbalanced samples.

[0370] Optionally, the device further includes:

[0371] The fifth sending module is used to send the data to the second network function.

[0372] Optionally, the device further includes:

[0373] The sixth sending module is used to send the data and the third information to the fourth network function.

[0374] It should be noted that the device uses the data generation method executed by the sixth network function described above. The implementation of the above method embodiment is applicable to this device and can achieve the same technical effect.

[0375] like Figure 13 As shown, an embodiment of this application provides a data generation apparatus applied to a fourth network function, including:

[0376] The fourth receiving module 1310 is used to receive data and third information sent by the fifth network function;

[0377] Storage module 1320 is used to associate and store the data and the third information;

[0378] The third information includes at least one of the following:

[0379] Data volume; feature dimensions; data threshold; generation duration.

[0380] After the device associates and stores the data received from the fifth network function with the third information, it can facilitate the first network function to query the required data based on the stored data.

[0381] Optionally, the storage module is further configured to:

[0382] If no data associated with the third information exists in the current stored data, the received data and the third information will be associated and stored together.

[0383] Optionally, the device further includes:

[0384] The third processing module is used to delete the received data if data associated with the third information exists in the current stored data.

[0385] It should be noted that the device uses the data generation method executed by the fourth network function described above. The implementation of the above method embodiment is applicable to this device and can achieve the same technical effect.

[0386] like Figure 14 As shown, an embodiment of this application provides a data generation apparatus applied to a second network function, including:

[0387] The second sending module 1410 is used to send a data generation request to the first network function, the data generation request being used to request data required by the second network function;

[0388] The fifth receiving module 1420 is used to receive data sent by the fourth network function or the sixth network function.

[0389] The device requests the required data from the first network function through a data generation request, thereby receiving the required data sent by the fourth or sixth network function.

[0390] Optionally, the data generation request includes information indicating at least one of the following:

[0391] Twin scope; AI scenario description; twin granularity; data volume; feature dimension; data threshold; generation duration.

[0392] Optionally, the device further includes:

[0393] The discovery module is used to send discovery requests to the fifth network function.

[0394] The eighth receiving module is used to receive the target address information fed back by the fifth network function;

[0395] The discovery request includes information indicating at least one of the following:

[0396] Twin scope; processing capacity; data generation identifier.

[0397] It should be noted that the device uses the data generation method executed by the second network function described above. The implementation of the above method embodiment is applicable to this device and can achieve the same technical effect.

[0398] like Figure 15 As shown, an embodiment of this application provides a data generation apparatus applied to a fifth network function, including:

[0399] The sixth receiving module 1510 is configured to receive a registration request sent by the first network function, the registration request including information indicating at least one of the following:

[0400] The identifier of the network element; the scope of the twin; the processing capacity; and the data generation identifier.

[0401] The device receives a registration request from the first network function, which then registers itself so that it can be matched with suitable first network functions in the future.

[0402] Optionally, the device further includes:

[0403] The ninth receiving module is used to receive discovery requests sent by the second network function;

[0404] The fourth processing module is used to determine the first matching network function based on the discovery request;

[0405] The sixth sending module is used to send the target address information of the first network function to the second network function;

[0406] The discovery request includes information indicating at least one of the following:

[0407] Twin scope; processing capacity; data generation identifier.

[0408] It should be noted that the device uses the data generation method executed by the fifth network function described above. The implementation of the above method embodiment is applicable to this device and can achieve the same technical effect.

[0409] like Figure 2 As shown, an embodiment of this application provides a network element, including a first network function that performs the data generation method described above, a third network function that performs the data generation method described above, a sixth network function that performs the data generation method described above, and a fourth network function that performs the data generation method described above.

[0410] Optionally, a first interface is provided between the first network function and the third network function, a second interface is provided between the third network function and the sixth network function, a third interface is provided between the first network function and the sixth network function, a fourth interface is provided between the first network function and the fourth network function, and a fifth interface is provided between the sixth network function and the fourth network function.

[0411] Embodiments of this application provide a data generation system, including: a network element as described above, a second network function that executes the data generation method as described above, and a fifth network function that executes the data generation method as described above.

[0412] like Figure 16 As shown, an embodiment of this application provides a network device, including a transceiver 1610, a processor 1600, a memory 1620, and a program or instructions stored in the memory 1620 and executable on the processor 1600; when the processor 1600 executes the program or instructions, it implements the above-described data generation method.

[0413] The transceiver 1610 is used to receive and send data under the control of the processor 1600.

[0414] Among them, Figure 16In this context, the bus architecture can include any number of interconnected buses and bridges, specifically linking various circuits together, represented by one or more processors (processor 1600) and memory (memory 1620). The bus architecture can also link together various other circuits such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and therefore will not be described further herein. The bus interface provides an interface. The transceiver 1610 can be multiple elements, including transmitters and receivers, providing a unit for communicating with various other devices over a transmission medium. The processor 1600 is responsible for managing the bus architecture and general processing, and the memory 1620 can store data used by the processor 1600 during operation.

[0415] Embodiments of this application provide a readable storage medium having a program or instructions stored thereon, which, when executed by a processor, implement the steps in the data generation method described above.

[0416] Embodiments of this application provide a computer program product, including computer instructions, which, when executed by a processor, implement the above-described... Figure 1 , Figure 5 , Figure 6 , Figure 7 , Figure 8 or Figure 9 The various processes of the method embodiments shown can achieve the same technical effect, and will not be described again here to avoid repetition.

[0417] It should be further noted that the terminals described in this specification include, but are not limited to, smartphones, tablets, etc., and many of the functional components described are referred to as modules in order to emphasize the independence of their implementation.

[0418] In this embodiment, the module can be implemented in software so that it can be executed by various types of processors. For example, an identified executable code module may include one or more physical or logical blocks of computer instructions, which may be constructed as objects, procedures, or functions. Nevertheless, the executable code of the identified module does not need to be physically located together, but may include different instructions stored in different bits, which, when logically combined, constitute the module and achieve the module's intended purpose.

[0419] In practice, an executable code module can be a single instruction or many instructions, and can even be distributed across multiple different code segments, different programs, and across multiple memory devices. Similarly, operational data can be identified within the module and can be implemented in any suitable form and organized within any suitable type of data structure. This operational data can be collected as a single dataset or distributed across different locations (including different storage devices), and can exist, at least in part, solely as electronic signals within the system or network.

[0420] When a module can be implemented using software, considering the current level of hardware technology, modules that can be implemented in software can be implemented using hardware circuits by those skilled in the art to achieve the corresponding functions, without considering cost. These hardware circuits include conventional very-large-scale integrated circuits (VLSI) or gate arrays, as well as existing semiconductors such as logic chips and transistors, or other discrete components. Modules can also be implemented using programmable hardware devices, such as field-programmable gate arrays, programmable array logic, and programmable logic devices.

[0421] The exemplary embodiments described above are with reference to the accompanying drawings. Many different forms and embodiments are feasible without departing from the spirit and teachings of this application. Therefore, this application should not be construed as limiting the exemplary embodiments set forth herein. Rather, these exemplary embodiments are provided to make this application complete and convey the scope of this application to those skilled in the art. In these drawings, component dimensions and relative dimensions may be exaggerated for clarity. The terminology used herein is for the purpose of describing particular exemplary embodiments only and is not intended to be limiting. As used herein, unless clearly indicated otherwise, the singular forms “a,” “an,” and “the” are intended to include all such forms. It will be further understood that the terms “comprising” and / or “including”, when used in this specification, indicate the presence of the stated features, integers, steps, operations, components, and / or elements, but do not exclude the presence or addition of one or more other features, integers, steps, operations, components, and / or groups thereof. Unless otherwise indicated, when stated, a range of values ​​includes the upper and lower limits of the range and any subranges in between.

[0422] The above description is the preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principles described in this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A data generation method, characterized in that, Performed by the first network function, including: Receive data generation requests sent by the second network function; Based on the data generation request, send first information to the third network function, and / or send second information to the fourth network function; Wherein, the first information is used to trigger the generation of data required for the second network function through the twin network, the second information is used to retrieve the data required for the second network function from the data stored in the fourth network function; the data generation request is used to request the data required for the second network function. The method further includes: Based on the data generation request, third information is sent to the third network function or the sixth network function, the third information including at least one of the following: data volume; feature dimension; data threshold; generation duration.

2. The method according to claim 1, characterized in that, The data generation request includes information indicating at least one of the following: Twin scope; AI scenario description; twin granularity; data volume; feature dimension; data threshold; generation duration.

3. The method according to claim 1 or 2, characterized in that, Based on the data generation request, send first information to the third network function, including: The first information is obtained by parsing the data to generate the request; Send the first information to the third network function; The first information includes at least one of the following: AI scene description information; the identifier and topology of the twin network element.

4. The method according to claim 1 or 2, characterized in that, Based on the data generation request, send second information to the fourth network function, including: The second information is obtained by parsing the data generation request; The second information is sent to the fourth network function; The second information includes data generation conditions.

5. The method according to claim 1, characterized in that, Based on the data generation request, send first information to the third network function, including: Upon receiving feedback information from the fourth network function, and the feedback information indicating that the fourth network function has not stored the data required by the second network function, the first information is sent to the third network function according to the data generation request.

6. The method according to claim 1, characterized in that, Before receiving the data generation request sent by the second network function, it also includes: Send a registration request to the fifth network function, the registration request including information indicating at least one of the following: The identifier of the network element; the scope of the twin; the processing capacity; and the data generation identifier.

7. A data generation method, characterized in that, Performed by a third network function, including: The first network function receives the first message sent based on the data generated request. Generate a twin network based on the first information; The data generation request is sent by the second network function, and the data generation request is used to request the data required by the second network function; the first information is used to trigger the generation of the data required by the second network function through the twin network; The method further includes: Receive the third information sent by the first network function; Send the third information to the sixth network function; The third information includes at least one of the following: data volume; feature dimension; data threshold; generation duration.

8. The method according to claim 7, characterized in that, The first information includes at least one of the following: AI scene description information; the identifier and topology of the twin network element.

9. The method according to claim 8, characterized in that, Generate a twin network based on the first information, including: If the first information includes AI scene description information, a query is performed based on the AI ​​scene description information to see if a matching general template exists. If a matching general template exists, a Siamese network is generated using the general template; or, If the first information includes the identifier and topology of the twin network element, and there is no matching general template, or if the first information does not include AI scene description information, a twin network is generated based on the identifier and topology of the twin network element.

10. The method according to claim 7, characterized in that, Generate a twin network based on the first information, including: When multiple pieces of the first information corresponding to different data generation requests are received, a twin network for each piece of the first information is generated in parallel based on the multiple pieces of the first information.

11. A data generation method, characterized in that, Performed by the sixth network function, including: Receive third information sent by the first network function or the third network function; Configure the twin network based on the third information to obtain the data required for the second network function; The third information includes at least one of the following: Data volume; feature dimensions; data threshold; generation duration; The third information sent by the first network function is sent by the first network function to the sixth network function after receiving the data generation request sent by the second network function; The third information sent by the third network function is the third information sent by the first network function based on the data sent by the second network function, which is received by the third network function. The data generation request is used to request the data required by the second network function. The first network function sends first information to the third network function and / or sends second information to the fourth network function according to the data generation request. The first information is used to trigger the generation of the data required by the second network function through the twin network, and the second information is used to retrieve the data required by the second network function from the data stored in the fourth network function.

12. The method according to claim 11, characterized in that, After configuring the twin network based on the third information and obtaining the data required for the second network function, the process further includes: The data is sent to the second network function.

13. The method according to claim 11, characterized in that, After configuring the twin network based on the third information and obtaining the data required for the second network function, the process further includes: The data and the third information are sent to the fourth network function.

14. A data generation method, characterized in that, Performed by the fourth network function, including: Receive data and third-party information sent by the sixth network function; The data and the third information are associated and stored; The third information includes at least one of the following: Data volume; feature dimensions; data threshold; generation duration; The data and the third information are sent by the sixth network function after configuring the twin network based on the third information sent by the first network function or the third network function and obtaining the data required by the second network function; The third information sent by the first network function is sent by the first network function to the sixth network function after receiving the data generation request sent by the second network function; The third information sent by the third network function is the third information sent by the first network function based on the data sent by the second network function, which is received by the third network function. The data generation request is used to request the data required by the second network function. The first network function sends first information to the third network function and / or sends second information to the fourth network function according to the data generation request. The first information is used to trigger the generation of the data required by the second network function through the twin network, and the second information is used to retrieve the data required by the second network function from the data stored in the fourth network function.

15. The method according to claim 14, characterized in that, The data and the third information are associated and stored, including: If no data associated with the third information exists in the current stored data, the received data and the third information will be associated and stored together.

16. The method according to claim 14, characterized in that, Also includes: If data associated with the third information exists in the current stored data, the received data will be deleted.

17. A data generation method, characterized in that, Performed by the second network function, including: Send a data generation request to a first network function, so that the first network function sends first information to a third network function and / or sends second information to a fourth network function according to the data generation request; Receive data sent by the fourth or sixth network function; The data generation request is used to request the data required by the second network function; the first information is used to trigger the generation of the data required by the second network function through the twin network; and the second information is used to retrieve the data required by the second network function from the data stored in the fourth network function.

18. The method according to claim 17, characterized in that, The data generation request includes information indicating at least one of the following: Twin scope; AI scenario description; twin granularity; data volume; feature dimension; data threshold; generation duration.

19. The method according to claim 17, characterized in that, Before sending the data generation request to the first network function, it also includes: Send a discovery request to the fifth network function; Receive the target address information fed back by the fifth network function; The discovery request includes information indicating at least one of the following: Twin scope; processing capacity; data generation identifier.

20. A data generation method, characterized in that, Performed by the fifth network function, including: Receive a registration request sent by a first network function, the registration request including information indicating at least one of the following: The identifier of the network element; the scope of the twin; the processing capacity; the data generation identifier; The first network function receives a data generation request sent by the second network function, and according to the data generation request, sends first information to the third network function and / or sends second information to the fourth network function; the first information is used to trigger the generation of data required by the second network function through the twin network, and the second information is used to retrieve the data required by the second network function from the data stored in the fourth network function; the data generation request is used to request the data required by the second network function.

21. The method according to claim 20, characterized in that, Also includes: Receive discovery requests sent by the second network function; Based on the discovery request, determine the first matching network function; Send the target address information of the first network function to the second network function; The discovery request includes information indicating at least one of the following: Twin scope; processing capacity; data generation identifier.

22. A data generation apparatus, characterized in that, Applied to the first network function, including: The first receiving module is used to receive data generation requests sent by the second network function; The first sending module is used to generate a request based on the data, send first information to the third network function, and / or send second information to the fourth network function; Wherein, the first information is used to trigger the generation of data required for the second network function through the twin network, the second information is used to retrieve the data required for the second network function from the data stored in the fourth network function; the data generation request is used to request the data required for the second network function. The device further includes: The third sending module is used to send third information to the third network function or the sixth network function according to the data generation request. The third information includes at least one of the following: data volume; feature dimension; data threshold; generation duration.

23. A data generation apparatus, characterized in that, Applications to third-party network functions include: The second receiving module is used to receive the first information sent by the first network function based on the data generation request; The first processing module is used to generate a twin network based on the first information; The data generation request is sent by the second network function, and the data generation request is used to request the data required by the second network function; the first information is used to trigger the generation of the data required by the second network function through the twin network; The device further includes: The seventh receiving module is used to receive the third information sent by the first network function; The fourth sending module is used to send the third information to the sixth network function; The third information includes at least one of the following: data volume; feature dimension; data threshold; generation duration.

24. A data generation apparatus, characterized in that, Applied to the sixth network function, including: The third receiving module is used to receive third information sent by the first network function or the third network function; The second processing module is used to configure the twin network according to the third information and obtain the data required for the second network function. The third information includes at least one of the following: Data volume; feature dimensions; data threshold; generation duration; The third information sent by the first network function is sent by the first network function to the sixth network function after receiving the data generation request sent by the second network function; The third information sent by the third network function is the third information sent by the first network function based on the data sent by the second network function, which is received by the third network function. The data generation request is used to request the data required by the second network function. The first network function sends first information to the third network function and / or sends second information to the fourth network function according to the data generation request. The first information is used to trigger the generation of the data required by the second network function through the twin network, and the second information is used to retrieve the data required by the second network function from the data stored in the fourth network function.

25. A data generation apparatus, characterized in that, Applied to the fourth network function, including: The fourth receiving module is used to receive data and third information sent by the sixth network function; A storage module is used to associate and store the data and the third information. The third information includes at least one of the following: Data volume; feature dimensions; data threshold; generation duration; The data and the third information are sent by the sixth network function after configuring the twin network based on the third information sent by the first network function or the third network function and obtaining the data required by the second network function; The third information sent by the first network function is sent by the first network function to the sixth network function after receiving the data generation request sent by the second network function; The third information sent by the third network function is the third information sent by the first network function based on the data sent by the second network function, which is received by the third network function. The data generation request is used to request the data required by the second network function. The first network function sends first information to the third network function and / or sends second information to the fourth network function according to the data generation request. The first information is used to trigger the generation of the data required by the second network function through the twin network, and the second information is used to retrieve the data required by the second network function from the data stored in the fourth network function.

26. A data generation apparatus, characterized in that, Applied to second network functions, including: The second sending module is used to send a data generation request to the first network function, so that the first network function sends first information to the third network function and / or sends second information to the fourth network function according to the data generation request. The fifth receiving module is used to receive data sent by the fourth or sixth network function; The data generation request is used to request the data required by the second network function; the first information is used to trigger the generation of the data required by the second network function through the twin network; and the second information is used to retrieve the data required by the second network function from the data stored in the fourth network function.

27. A data generation apparatus, characterized in that, Applied to the fifth network function, including: The sixth receiving module is configured to receive a registration request sent by the first network function, the registration request including information indicating at least one of the following: The identifier of the network element; the scope of the twin; the processing capacity; the data generation identifier; The first network function receives a data generation request sent by the second network function, and according to the data generation request, sends first information to the third network function and / or sends second information to the fourth network function; the first information is used to trigger the generation of data required by the second network function through the twin network, and the second information is used to retrieve the data required by the second network function from the data stored in the fourth network function; the data generation request is used to request the data required by the second network function.

28. A network element, characterized in that, It includes performing a first network function of the data generation method as described in any one of claims 1-6, a third network function of the data generation method as described in any one of claims 7-10, a sixth network function of the data generation method as described in any one of claims 11-13, and a fourth network function of the data generation method as described in any one of claims 14-16.

29. The network element according to claim 28, characterized in that, A first interface is provided between the first network function and the third network function, a second interface is provided between the third network function and the sixth network function, a third interface is provided between the first network function and the sixth network function, a fourth interface is provided between the first network function and the fourth network function, and a fifth interface is provided between the sixth network function and the fourth network function.

30. A data generation system, characterized in that, include: The network element as described in claim 28 or 29 performs a second network function of the data generation method as described in any one of claims 17-19, and performs a fifth network function of the data generation method as described in claim 20 or 21.

31. A network device, comprising: A transceiver, a processor, a memory, and a program or instructions stored in the memory and executable on the processor; characterized in that, when the processor executes the program or instructions, it implements the data generation method as described in any one of claims 1-6, or the data generation method as described in any one of claims 7-10, or the data generation method as described in any one of claims 11-13, or the data generation method as described in any one of claims 14-16, or the data generation method as described in any one of claims 17-19, or the data generation method as described in claim 20 or 21.

32. A readable storage medium having a program or instructions stored thereon, characterized in that, When the program or instructions are executed by the processor, they implement the data generation method as described in any one of claims 1-6, or the data generation method as described in any one of claims 7-10, or the data generation method as described in any one of claims 11-13, or the data generation method as described in any one of claims 14-16, or the data generation method as described in any one of claims 17-19, or the steps in the data generation method as described in claim 20 or 21.

33. A computer program product, characterized in that, The method includes computer instructions that, when executed by a processor, implement the data generation method as described in any one of claims 1-6, or the data generation method as described in any one of claims 7-10, or the data generation method as described in any one of claims 11-13, or the data generation method as described in any one of claims 14-16, or the data generation method as described in any one of claims 17-19, or the steps of the data generation method as described in claim 20 or 21.

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