Patent Description:
A Network Data Analytics Function (NWDAF) entity is configured as an integrated entity, and includes units such as a data lake, a training platform, an inference platform, and the like. The NWDAF entity interacts with various network elements (NEs), performs data collection, model training and inference operation, and sends a training analysis result to related NEs as demands.

At present, an overall design of the NWDAF entity includes a model training function, so that usage of the model training function is not flexible enough.

Related arts can be found in <CIT>; <CIT>; <CIT>; "<NPL>, and <NPL>).

<CIT> is a document which has been published after the priority date of the present application and the corresponding date of publication of the document is <NUM>-<NUM>-<NUM>. <CIT> discloses an NWDAF (see <FIG>, <FIG> and <FIG>) which subscribes to receive model updates from one or more network entities which are training a model using a machine-learning algorithm.

<CIT> is a document which has been published after the priority date of the present application and the corresponding date of publication of the document is <NUM>-<NUM>-<NUM>. <CIT> discloses an NWDAF (see <FIG>) in which, as a "model consumer", may execute a model request method (see <FIG> & paragraph <NUM>). More particularly, according to the method, the model consumer (NWDAF) sends a model request message carrying any one or more parameters (e.g. model receiver address, model and type identifiers, training data, model screening conditions, data association info and feedback policy) to a model owner. The model owner determines an analytics model according to the model request and feeds back the determined model and/or model identifier to the model consumer (NWDAF).

<CIT> is a document which has been published after the priority date of the present application and the corresponding date of publication of the document is <NUM>-<NUM>-<NUM>. <CIT> discloses an NWDAF and a method for determining device information performed by an interference device (exemplary, the NWDAF), see <FIG>. More particularly, the interference device (e.g. an NWDAF, see paragraph <NUM>) sends a first request to a service discovery entity (e.g. an NRF, see paragraph <NUM>). The first request is for requesting information about one or more training devices and includes an algorithm type or an algorithm identifier of a first model requested by the inference device. The service discovery entity determines the information about the one or more training devices based on the algorithm type of the first model requested by the inference device, where the information about the training device includes capability information and sends such information to the interference device. Then, the inference device determines a first training device from the information about the one or more training devices based on a preset condition.

<NPL>" discloses a procedure for data collection from an AF via a NEF (see e.g. Figure <NUM>. <NUM>-<NUM>). In particular, when NWDAF needs to discover available data from AFs and the appropriated NEF to collect this data, the NWDAF invokes a Nnrf NFDiscovery_Request request service operation using as parameters an NEF NF Type, a list of Event ID(s), and optionally AF identification, application ID. Then, the NRF matches the requested query for available data in AFs with the registered NEF Profiles and sends this information via Nnrf_NFDiscovery_Request_response message back to the NWDAF. After that, The NWDAF subscribes to or cancels subscription to data in AF via NEF by invoking the Nnef_EventExposure_Subscribe or Nnef_EventExposure_Unsubscribe service operation. If the event subscription is authorized by the NEF, the NEF records the association of the event trigger and the NWDAF identity. Based on the request from the NWDAF, the NEF subscribes to or cancels subscription to data in AF by invoking the Naf_EventExposure_Subscribe/ Naf_EventExposure_Unsubscribe service operation. If the NEF subscribes to data in AF, the AF notifies the NEF with the data by invoking Naf_EventExposure_Notify service operation. If the NEF receives the notification from the AF, the NEF notifies the NWDAF with the data by invoking Nnef_EventExposure_Notify service operation.

<NPL>" discloses an NWDAF architecture for 3GPP Release <NUM> in which, in a possible NWDAF functional split, a network function, NF, like a training platform, is separated from the NWDAF (see D5, <FIG>) in contrast to the NWDAF architecture in Release <NUM>, which integrates multiple functions, such as Data Lake, AI Training Function (including Model Training, AI Model Repository, etc.) and AI Inference Function (including Model Deployment, Model Application, Model Management, etc.).

Embodiments of the disclosure provide a network data analysis method, and a functional entity and an electronic device, to solve the problem of usage of the model training function of the NWDAF entity being not flexible enough.

According to the embodiments of the disclosure, the NWDAF entity requests the first object to generate the model, the first object is a training platform, a training module, a training functional entity, or a training service module, and the NWDAF entity receives the model sent by the first object. Since the first object and the NWDAF are two separate entities, the first object having a model training function may be used by multiple NWDAFs, improving flexibility.

In the following, the invention is best understood in view of <FIG> and <FIG>. The remaining embodiments, aspects and examples disclosed below are included for illustrative purposes and for facilitating the understanding of the invention.

In order to explain technical solutions of the embodiments of the disclosure more clearly, the drawings to be used in the descriptions of the embodiments of the disclosure will be simply introduced below. It is apparent that the drawings described below are merely some embodiments of the disclosure. Other drawings may also be obtained by those of ordinary skill in the art according to these drawings without paying any creative work.

Embodiments of the invention are those described below with reference to <FIG>, <FIG>, <FIG>. Remaining embodiments, aspects and examples disclosed below are included for illustrative purposes and for facilitating the understanding of the invention.

The technical solutions of the embodiments of the disclosure will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the disclosure. It is apparent that the described embodiments are some but not all of the embodiments of the disclosure. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the disclosure without any creative work fall within the protection scope of the disclosure.

In a first version of the fifth-generation (<NUM>) standard, an NWDAF entity based on machine learning is used by the third Generation Partnership Project (3GPP) as a basis for a network slice selection function and a policy control function. In other words, execution of the network slice selection function and the policy control function depends on the NWDAF entity. The NWDAF entity may perform "collecting" operation and data processing operation on many types of data, such as monitoring traffic loads of all <NUM> network slices in real time, collecting and analyzing usage behavior of a user on a <NUM> mobile terminal, collecting and analyzing operation performance of a <NUM> application, or the like, and then may make real-time analysis and decisions on them by using a machine learning technology.

Specific embodiments of the disclosure may be applied to the above-described <NUM> mobile communication system, however, it should be understood that the embodiments of the disclosure may also be applied to other networks having NWDAF or similar functional entities.

Specific embodiments of the disclosure are further described in detail below.

Referring to <FIG>, it is a flowchart of a network data analysis method applied to an NWDAF entity according to an embodiment of the disclosure. As shown in <FIG>, the network data analysis method includes the following operations.

In operation <NUM>, a first object is requested to generate a model, the first object is a training platform, a training module, a training functional entity, or a training service module.

The model may be a statistical model or a prediction model.

In operation <NUM>, the model sent by the first object is received.

In the embodiment, the model is generated by the first object and sent to the NWDAF entity. The first object and the NWDAF are two separate entities, therefore, the first object having a model training function exists separately as a physical entity or a logical entity, so that each first object may be used by multiple NWDAFs, improving flexibility.

In the embodiment, the NWDAF entity requests the first object to generate the model, the first object is a training platform, a training module, a training functional entity, or a training service module, and the NWDAF entity receives the model sent by the first object. Since the first object and the NWDAF are two separate entities, the first object having a model training function may be used by multiple NWDAFs, improving flexibility.

In an embodiment of the disclosure, the requesting the first object to generate the model may specifically include the following operations.

A first request message for requesting the first object to generate the model is sent to the first object; the model is carried by a first response message.

Alternatively, a training service provided by a third object to generate the model is invoked; the third object is a training module, a training functional entity, or a training service module.

In the embodiment, there are two ways to request the first object to generate the model. The first way is to request the first object to generate the model by sending the first request message to the first object. The second way is to request the first object to generate the model by invoking the training service provided by the third object to generate the model.

The training module logically belongs to the NWDAF entity, but may be disposed in the NWDAF entity in terms of physical deployment, or may be deployed in other entities or NEs. All of the training functional entity, the training service module and the training module are software logic modules, and are different explanations of a software implementation.

In an embodiment of the disclosure, the first request message may include at least one of an algorithm identifier parameter, an algorithm performance requirement parameter, or a data address parameter.

Specifically, the algorithm identifier parameter may indicate which algorithm is requested for usage in training, the algorithm performance requirement parameter may indicate performance requirement of the algorithm requested for usage, and the data address parameter may indicate where data is collected for training.

In an embodiment of the disclosure, the first response message may include at least one of an identifier, an input parameter, an output parameter, or other model parameters of the model.

In an embodiment of the disclosure, the method may further include the following operations before requesting the first object to generate the model.

A second request message is sent to an NRF entity.

A second response message, which is returned by the NRF entity, is received; and the second response message is used to indicate available second objects, each of the second objects is a training platform, a training module, a training functional entity, or a training service module; the first object is selected from the second objects.

Specifically, in a current architecture, all of services, training platforms, or the like are uniformly registered with the NRF. Before the first object is determined, that is, before requesting the first object to generate the model, the NWDAF entity may ask the NRF entity to inform it objects currently available, to avoid blind selection of the first object. That is, the NWDAF entity sends the second request message to the NRF entity, to request the NRF entity to return the available second objects.

Furthermore, the NWDAF entity may provide its own requirements, for example, the NWDAF carries its own requirements in the second request message. The parameters is used by the NRF entity to determine the second objects. The second objects determined this way will more conform to the requirements of the NWDAF entity. The NRF entity may determine the second objects according to the parameters carried in the second request message, and send the second response message indicating the available second objects to the NWDAF entity.

The NWDAF entity receives the second response message returned by the NRF entity, and selects the first object from the second objects according to the indication of the second response message. The second response message carries parameters of the second objects, the parameters are used by the NWDAF entity to select the first object from the second objects.

There may be multiple second objects returned by the NRF entity, and since the second response message carries parameters of the second objects, it facilitates the NWDAF entity to select an appropriate first object from the multiple second objects, for example, to select an object with a high training accuracy or an object with a fast training speed.

In an embodiment of the disclosure, the method may further include the following operations after receiving the model sent by the first object.

A model registration message including identifier information of the model and address information of the NWDAF entity is sent to the NRF entity.

Specifically, the NWDAF entity sends the model registration message to the NRF entity, to complete registration of the NWDAF entity with the NRF entity, so that a Network Function (NF) entity may invoke and obtain the model later.

Furthermore, when the NWDAF entity requesting the first object to generate the model is made based on the request of the NF entity, the NWDAF entity sends the model to the NF after receiving the model sent by the first object.

Referring to <FIG>, it is a flowchart of a network data analysis method applied to an NRF entity according to an embodiment of the disclosure. As shown in <FIG>, the network data analysis method includes the following operations.

In operation <NUM>, a second request message sent by an NWDAF entity is received.

In operation <NUM>, a second response message indicating available second objects is sent to the NWDAF entity; each of the second objects is a training platform, a training module, a training functional entity, or a training service module.

Specifically, before requesting the first object to generate a model, the NWDAF entity may ask the NRF entity to inform it objects currently available, to avoid blind selection of the first object. That is, the NWDAF entity sends the second request message to the NRF entity, to request the NRF entity to return the available second objects.

After receiving the second request message sent by the NWDAF entity, the NRF entity sends the second response message indicating the available second objects to the NWDAF entity, so that the NWDAF entity may select the first object from the second objects after obtaining the second objects, and request the first object to generate the model.

In the embodiment, the second request message sent by the NWDAF entity is received; the second response message indicating the available second objects is sent to the NWDAF entity; each of the second objects is a training platform, a training module, a training functional entity, or a training service module. Therefore, after obtaining the second objects, the NWDAF entity may select the first object from the second objects and request the first object to generate the model. Since the first object and the NWDAF are two separate entities, the first object having a model training function may be used by multiple NWDAFs, improving flexibility.

In an embodiment of the disclosure, the method may further include the following operations before receiving the second request message sent by the NWDAF entity.

A registration request message sent by a to-be-registered object is received; the second objects are selected from objects registered in the NRF entity.

In the embodiment, all of services, training platforms, or the like may be uniformly registered with the NRF entity. The NRF entity receives the registration request message sent by the to-be-registered object, and after receiving the second request message sent by the NWDAF entity, the NRF entity returns the second response message indicating the available second objects to the NWDAF entity. The second objects are selected from objects registered in the NRF.

Furthermore, the registration request message may include at least one of an address parameter, a location parameter, a load parameter, an algorithm capability parameter, or a supported algorithm type parameter of the to-be-registered object.

Furthermore, the second request message may carry parameters used by the NRF to determine the second objects.

Specifically, the NWDAF entity may provide its own requirements, for example, the NWDAF carries its own requirements in the second request message. The parameters are used by the NRF entity to determine the second objects. The second objects determined in this way will more conform to the requirements of the NWDAF entity. The NRF entity may determine the second objects according to the parameters carried in the second request message, and send the second response message indicating the available second objects to the NWDAF entity.

Furthermore, the second response message may carry parameters of the second objects for the NWDAF to select a first object from the second objects.

Specifically, there may be multiple second objects returned by the NRF entity, and since the second response message carries parameters of the second objects, it facilitates the NWDAF entity to select an appropriate first object from the multiple second objects, for example, to select an object with a high training accuracy or an object with a fast training speed.

In an embodiment of the disclosure, the method may further include the following operations after sending the second response message to the NWDAF entity.

A model registration message sent by the NWDAF entity and including identifier information of the model and address information of the NWDAF entity is received.

Specifically, the NWDAF entity sends the model registration message to the NRF entity, to complete registration of the NWDAF entity with the NRF entity, so that an NF entity may invoke and obtain the model later.

Referring to <FIG>, it is a flowchart of a network data analysis method, applied to a training object including a training platform, a training module, a training functional entity, or a training service module, according to an embodiment of the disclosure. As shown in <FIG>, the network data analysis method includes the following operations.

In operation <NUM>: a request sent by an NWDAF entity is received, to generate a model.

The training object may be a first object or a third object. The model may be a statistical model or a prediction model.

In operation <NUM>, the model is sent to the NWDAF entity.

The training object sends the generated model to the NWDAF entity.

In the embodiment, the training object receives the request sent by the NWDAF entity to generate the model, and sends the model to the NWDAF entity. Since the training object has a model training function, and the training object and the NWDAF are two separate entities, the training object having the model training function may be used by multiple NWDAFs, improving flexibility.

In an embodiment of the disclosure, the receiving the request sent by the NWDAF entity and generating the model may specifically include the following operations.

Data training is performed after receiving a first request message sent by the NWDAF entity, to generate the model.

Alternatively, a request of invoking data training service sent by the NWDAF entity is received, to provide a training service for generating the model.

Specifically, the training object may provide two ways to generate the model according to the request sent by the NWDAF entity.

The first way is to perform data training after receiving the first request message sent by the NWDAF entity, to generate the model; and the second way is to receive the request of invoking the data training service sent by the NWDAF entity, to provide the training service for generating the model, so that the NWDAF entity invokes the training service for generating the model. In the embodiment, the training module logically belongs to the NWDAF entity, but may be disposed in the NWDAF entity in terms of physical deployment, or may be deployed in other entities or NEs. All of the training functional entity, the training service module and the training module are software logic modules, and are different explanations of a software implementation.

In an embodiment of the disclosure, before receiving the request sent by the NWDAF entity and generating the model, the method may further include the following operations.

A registration request message is sent to an NRF entity, to request registration with the NRF entity.

In the embodiment, the training object may register it with the NRF entity. The NRF entity receives the registration request message sent by the training object to request registration with the NRF entity. Therefore, after receiving the second request message sent by the NWDAF entity, the NRF entity returns a second response message indicating available second objects to the NWDAF entity. The second objects are selected from objects registered in the NRF.

In an embodiment of the disclosure, after receiving the request sent by the NWDAF entity and generating the model, the method may further include the following operations.

At least one of an identifier, an input parameter, an output parameter, or other model parameters of the model is sent to the NWDAF entity.

Referring to <FIG>, it is a schematic diagram of information transmission among multiple functional entities according to an embodiment of the disclosure. <FIG> includes four functional entities which are an NRF entity, an NWDAF entity, an AI training platform <NUM> (which may be understood as a training object <NUM>), and an AI training platform <NUM> (which may be understood as a training object <NUM>) respectively.

In operation 1a, the training platform <NUM> registers with the NRF entity, to inform the NRF entity of address, location, load, algorithm capability, or the like of the training platform <NUM>, and the NRF entity returns a response message (Nnrf_NFManagement_NFRegister_request/Rsp) to the training platform <NUM>. That is, the training platform <NUM> registers, with the NRF, its own capability including information such as address, location, load, algorithm capability, algorithm type, or the like.

In operation 1b, the training platform <NUM> registers with the NRF entity, to inform the NRF entity of address, location, load, algorithm capability, or the like of the training platform <NUM>, and the NRF entity returns a response message (Nnrf_NFManagement_NFRegister_request/Rsp) to the training platform <NUM>. That is, the training platform <NUM> registers, with the NRF, its own capability including information such as address, location, load, algorithm capability, algorithm type, or the like.

In operation <NUM>, the NWDAF entity triggers data analysis. The NWDAF (Inference Platform or Inference Function) triggers data analysis for a particular Analytics. There are two cases as follows.

In operation <NUM>, the NWDAF entity sends a request message (which may be understood as a first request message) including algorithm type, address of the NWDAF, or the like to the NRF entity, that is, the NWDAF applies, from the NRF, service of a training platform carrying information such as algorithm capability, algorithm type, address of the NWDAF, or the like.

In operation <NUM>, the NRF entity returns a response message (Nnrf_NFDiscovery_Request response) including one or more information of the training platform, including address of the training platform, supported algorithm identifier, algorithm performance, algorithm speed, or the like.

In operation <NUM>, the NWDAF selects a training platform according to the information returned by the NRF. In <FIG>, the NWDAF entity selects the training platform <NUM> according to the output of the NRF entity.

In operation <NUM>, the NWDAF entity sends, to the training platform <NUM>, a model request carrying algorithm identifier, algorithm performance requirement, and data address (optional).

In operation <NUM>, the training platform performs data collection and model training.

In operation <NUM>, the training platform responds to the NWDAF entity with a model response carrying an identifier, an input (Event ID list), an output, or other model parameters of the model.

In operation <NUM>, the NWDAF entity completes deployment of the model.

In operation <NUM>, with respect to case (a) of the operation <NUM>, the NWDAF returns a result of Analytics Data to the requested NF.

With respect to case (b) of the operation <NUM>, the NWDAF registers with the NRF, carries Analytic ID and address of the NWDAF, so that an NF may invoke and obtain Analytics Data later.

In <FIG>, the NWDAF entity sends, to the NRF entity, a registration message (Nnrf_NFManagement_NFRegister_Request) including Analytics ID and address of the NWDAF, and the NRF entity returns a response message to the NWDAF entity.

In the disclosure, the training platform side (Training Platform) may include:.

The NWDAF side (Inference Platform or Inference Function) may include:.

According to the network data analysis method of the disclosure, the data training platform and the NWDAF NE may be separated. The NWDAF registers the capability through the NRF and finds the training platform. In actual deployment, the training platform may be a third-party Artificial Intelligence (AI) platform or a self-developed AI platform.

Referring to <FIG>, it is a schematic structural diagram of a functional entity according to an embodiment of the disclosure. As shown in <FIG>, a first functional entity <NUM> includes a request module <NUM> and a first receiving module <NUM>.

The request module <NUM> is configured to request a first object to generate a model, the first object is a training platform, a training module, a training functional entity, or a training service module.

The first receiving module <NUM> is configured to receive the model sent by the first object.

Furthermore, the request module <NUM> may be configured to:.

Furthermore, the first functional entity <NUM> may further include a first sending module and a second receiving module.

The first sending module is configured to send a second request message to an NRF entity.

The second receiving module is configured to receive a second response message which is returned by the NRF entity; the second response message is used to indicate available second objects, each of the second objects is a training platform, a training module, a training functional entity, or a training service module; the first object is selected from the second objects.

Furthermore, the second request message may carry parameters used by the NRF entity to determine the second objects.

Furthermore, the second response message may carry parameters of the second objects, the parameters are used by the NWDAF entity to select the first object from the second objects.

Furthermore, the first request message may include at least one of an algorithm identifier parameter, an algorithm performance requirement parameter, or a data address parameter.

Furthermore, the first response message may include at least one of an identifier, an input parameter, an output parameter, or other model parameters of the model.

Furthermore, the first functional entity <NUM> may further include a second sending module.

The second sending module is configured to send, to the NRF entity, a model registration message including identifier information of the model and address information of the NWDAF entity.

The first functional entity <NUM> may implement each process of the method embodiment shown in <FIG> implemented by the NWDAF entity, which is not elaborated here, so as to avoid repetition.

According to the embodiment of the disclosure, the first functional entity <NUM> requests the first object to generate the model, the first object is a training platform, a training module, a training functional entity, or a training service module, and the first functional entity <NUM> receives the model sent by the first object. Since the first object and the NWDAF are two separate entities, the first object having a model training function may be used by multiple NWDAFs, improving flexibility.

Referring to <FIG>, it is a schematic structural diagram of a functional entity according to an embodiment of the disclosure. As shown in <FIG>, a second functional entity <NUM> includes a first receiving module <NUM> and a sending module <NUM>.

The first receiving module <NUM> is configured to receive a second request message sent by an NWDAF entity.

The sending module <NUM> is configured to send, to the NWDAF entity, a second response message indicating available second objects; each of the second objects is a training platform, a training module, a training functional entity, or a training service module.

Furthermore, the second functional entity <NUM> may further include a first receiving module.

The first receiving module is configured to receive a registration request message sent by a to-be-registered object; the second objects are selected from objects registered in the NRF entity.

Furthermore, the second functional entity <NUM> may further include a second receiving module.

The second receiving module is configured to receive a model registration message sent by the NWDAF entity and including identifier information of the model and address information of the NWDAF entity.

The second functional entity <NUM> may implement each process of the method embodiment shown in <FIG> implemented by the NRF entity, which is not elaborated here, so as to avoid repetition.

According to the embodiment of the disclosure, the second functional entity <NUM> receives the second request message sent by the NWDAF entity; and sends, to the NWDAF entity, the second response message indicating available second objects; each of the second objects is a training platform, a training module, a training functional entity, or a training service module. Therefore, after obtaining the second objects, the NWDAF entity may select the first object from the second objects and request the first object to generate the model. Since the first object and the NWDAF are two separate entities, the first object having a model training function may be used by multiple NWDAFs, improving flexibility.

Referring to <FIG>, it is a schematic structural diagram of a functional entity according to an embodiment of the disclosure. As shown in <FIG>, a third functional entity <NUM> is a training object including a training platform, a training module, a training functional entity, or a training service module. The third functional entity <NUM> includes a receiving module <NUM> and a first sending module <NUM>.

The receiving module <NUM> is configured to receive a request sent by an NWDAF entity, to generate a model.

The first sending module <NUM> is configured to send the model to the NWDAF entity.

Furthermore, the receiving module <NUM> may be configured to:.

Furthermore, the third functional entity <NUM> may further include a second sending module.

The second sending module is configured to send a registration request message to an NRF entity, to request registration with the NRF entity.

Furthermore, the third functional entity <NUM> may further include a third sending module.

The third sending module is configured to send, to the NWDAF entity, at least one of an identifier, an input parameter, an output parameter, or other model parameters of the model.

The third functional entity <NUM> may implement each process of the method embodiment shown in <FIG> implemented by the training object, which is not elaborated here, so as to avoid repetition.

According to the embodiment, the training object <NUM> receives the request sent by the NWDAF entity, to generate the model, and sends the model to the NWDAF entity. Since the training object has a model training function, and the training object and the NWDAF are two separate entities, the training object having a model training function may be used by multiple NWDAFs, improving flexibility.

Referring to <FIG>, an embodiment of the disclosure further provides a functional entity including a bus <NUM>, a transceiver <NUM>, an antenna <NUM>, a bus interface <NUM>, a processor <NUM>, and a memory <NUM>.

In an embodiment of the disclosure, when the functional entity is an NWDAF entity, the functional entity includes a processor and a transceiver.

The transceiver is configured to request a first object to generate a model and receive the model sent by the first object, the first object is a training platform, a training module, a training functional entity, or a training service module.

Furthermore, the transceiver may be further configured to:.

Furthermore, the transceiver may be further configured to:
send, to the NRF entity, a model registration message including identifier information of the model and address information of the NWDAF entity.

In the embodiment, the functional entity may further include a computer program stored on the memory <NUM> and executable on the processor <NUM>. The computer program may implement the following operations when executed by the processor <NUM>.

A first object is requested to generate a model, the first object is a training platform, a training module, a training functional entity, or a training service module.

The model sent by the first object is received.

Furthermore, the requesting the first object to generate the model may specifically include the following operations.

Furthermore, the computer program may further implement the following operations when executed by the processor <NUM>.

A second response message, which is returned by the NRF entity, is received; the second response message is used to indicate available second objects, each of the second objects is a training platform, a training module, a training functional entity, or a training service module; the first object is selected from the second objects.

The functional entity may implement each process of the method embodiment shown in <FIG> implemented by the NWDAF entity, which is not elaborated here, so as to avoid repetition.

According to the embodiment, the functional entity requests the first object to generate the model, the first object is a training platform, a training module, a training functional entity, or a training service module, and the functional entity receives the model sent by the first object. Since the first object and the NWDAF are two separate entities, the first object having a model training function may be used by multiple NWDAFs, improving flexibility.

In an embodiment of the disclosure, when the functional entity is an NRF entity, the functional entity includes a processor and a transceiver.

The transceiver is configured to receive a second request message sent by an NWDAF entity; and send, to the NWDAF entity, a second response message indicating available second objects; each of the second objects is a training platform, a training module, a training functional entity, or a training service module.

Furthermore, the transceiver may be further configured to:
receive a registration request message sent by a to-be-registered object; the second objects are selected from objects registered in the NRF entity.

receive a model registration message sent by the NWDAF entity and including identifier information of the model and address information of the NWDAF entity.

A second request message sent by an NWDAF entity is received.

A second response message indicating available second objects is sent to the NWDAF entity; each of the second objects is a training platform, a training module, a training functional entity, or a training service module.

The functional entity may implement each process of the method embodiment shown in <FIG> implemented by the NRF entity, which is not elaborated here, so as to avoid repetition.

According to the embodiment, the functional entity receives the second request message sent by the NWDAF entity; and sends, to the NWDAF entity, the second response message indicating available second objects; each of the second objects is a training platform, a training module, a training functional entity, or a training service module. Therefore, after obtaining the second objects, the NWDAF entity may select the first object from the second objects and request the first object to generate the model. Since the first object and the NWDAF are two separate entities, the first object having a model training function may be used by multiple NWDAFs, improving flexibility.

In an embodiment of the disclosure, the functional entity is a training object, and when the training object includes a training platform, a training module, a training functional entity, or a training service module, the functional entity includes a processor and a transceiver.

The transceiver is configured to receive a request sent by an NWDAF entity, generate a model, and send the model to the NWDAF entity.

Furthermore, the transceiver may be further configured to:
send a registration request message to an NRF entity, to request registration with the NRF entity.

Furthermore, the transceiver may be further configured to:
send, to the NWDAF entity, at least one of an identifier, an input parameter, an output parameter, or other model parameters of the model.

A request sent by an NWDAF entity is received, to generate a model.

Furthermore, the computer program may further implement the following operations when executed by the processor <NUM>. Data training is performed after receiving a first request message sent by the NWDAF entity, to generate the model.

Furthermore, the computer program may further implement the following operations when executed by the processor <NUM>. A registration request message is sent to an NRF entity, to request registration with the NRF entity.

Furthermore, the computer program may further implement the following operations when executed by the processor <NUM>. At least one of an identifier, an input parameter, an output parameter, or other model parameters of the model is sent to the NWDAF entity.

The functional entity may implement each process of the method embodiment shown in <FIG> implemented by the training object, which is not elaborated here, so as to avoid repetition.

According to the embodiment of the disclosure, the functional entity receives the request sent by the NWDAF entity, to generate the model, and sends the model to the NWDAF entity. Since the training object has a model training function, and the training object and the NWDAF are two separate entities, the training object having a model training function may be used by multiple NWDAFs, improving flexibility.

In <FIG>, a bus architecture (represented by the bus <NUM>), the bus <NUM> may include any number of interconnected buses and bridges, the bus <NUM> connects various circuits including one or more processors represented by processor <NUM> and memories represented by the memory <NUM> together. The bus <NUM> may also connect a variety of other circuits, such as a peripheral device, a voltage regulator, a power management circuit, or the like together, which is well known in the art and thus is not elaborated here. The bus interface <NUM> provides an interface between the bus <NUM> and the transceiver <NUM>. The transceiver <NUM> may be an element or multiple elements, such as multiple receivers and transmitters, to provide a unit for communicating with various other devices on a transmission medium. The data processed by the processor <NUM> is transmitted over a wireless medium via an antenna <NUM>, which further receives the data and transmits the data to the processor <NUM>.

The processor <NUM> is responsible for managing the bus <NUM> and general processing, and may also provide various functions including timing, peripheral interfaces, voltage regulation, power management, and other control functions. The memory <NUM> may store data used by the processor <NUM> upon performing operations.

In an embodiment, the processor <NUM> may be a Center Processing Unit (CPU), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA), or a Complex Programmable Logic Device (CPLD).

In an embodiment, an embodiment of the disclosure further provides an electronic device, including a processor <NUM>, a memory <NUM>, and a computer program stored on the memory <NUM> and executable on the processor <NUM>. The computer program may implement each process of any one of the network data analysis method embodiments shown in <FIG> when executed by the processor <NUM>, and may achieve the same technical effect, which is not elaborated here, so as to avoid repetition.

An embodiment of the disclosure further provides a computer-readable storage medium, having stored thereon a computer program. The computer program may implement each process of any one of the network data analysis method embodiments shown in <FIG> when executed by the processor, and may achieve the same technical effect, which is not elaborated here, so as to avoid repetition.

For example, the computer-readable storage medium is a Read Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.

It should be noted that in the disclosure, terms "include", "comprise" or any other variant thereof are intended to cover non-exclusive inclusion, such that a process, method, article or device including a series of elements includes not only those elements but also other elements not listed explicitly, or includes elements inherent to such process, method, article or device. Without further limitation, an element defined by a sentence "include a. " does not exclude that additional identical elements also exist in the process, method, article or device including the element.

Claim 1:
A network data analysis method, performed by a network data analytics function, NWDAF, entity, comprising:
requesting (<NUM>) a first object to generate a model; and
receiving (<NUM>) model information of the model sent by the first object;
wherein before requesting the first object to generate the model, the method further comprises:
sending a second request message to a network repository function, NRF, entity; and
receiving a second response message which is returned by the NRF entity, the second response message being used to indicate available second objects; each of the second objects being a training platform, a training module, a training functional entity, or a training service module; the first object being selected from the second objects;
wherein the requesting the first object to generate the model comprises:
invoking a training service provided by the first object to generate the model; the first object being a training module, a training functional entity, or a training service module;
wherein after receiving (<NUM>) the model information of the model sent by the first object, the method further comprises:
sending, to the NRF entity, a model registration message, the model registration message comprising identifier information of the model and address information of the NWDAF entity.