CN116227943A

CN116227943A - Client autonomous analysis method, device and medium

Info

Publication number: CN116227943A
Application number: CN202111429668.4A
Authority: CN
Inventors: 梅承力; 李鹏宇; 聂衡; 夏旭
Original assignee: China Telecom Corp Ltd
Current assignee: China Telecom Corp Ltd
Priority date: 2021-11-29
Filing date: 2021-11-29
Publication date: 2023-06-06
Also published as: WO2023093320A1

Abstract

The present disclosure relates to a client autonomic analysis method, apparatus, and medium. A method for autonomous analysis of business data by a customer, comprising: receiving, by the first analysis function, a data analysis request of the client, the request including an autonomous analysis service field, an address of a second analysis function to be used, and a client ID; identifying, by the first analysis function, a value of an autonomous analysis service field in the request; in the case where the value representing request is a client autonomous analysis request, confirming by the first analysis function whether the client represented by the client ID is a contracted client, and in the case where it is a contracted client, acquiring by the first analysis function contract information of the client; querying a service network element corresponding to the subscription information by a first analysis function; collecting, by a first analysis function, operator data associated with the customer from the network element and transmitting the collected operator data to a second analysis function according to the address; the analysis results are received by the first analysis function from the second analysis function and decisions are made based on the analysis results.

Description

Client autonomous analysis method, device and medium

Technical Field

The present disclosure relates generally to client autonomic analysis methods, apparatus, and media.

Background

The 5G network can provide various services such as slicing, and the NWDAF network element architecture is introduced by the standard, so that intelligent analysis such as slicing SLA (Service Level Agreement ) guarantee, user track, fault analysis and the like is supported, and intelligent analysis is provided for users.

It is currently known that some important clients such as enterprises, governments, etc. do need NWDAF analysis functions, but clearly indicate that the service data is not willing to be analyzed by the operators, who wish to develop model training and intelligent analysis themselves, and that the operators work well.

The present disclosure is directed to solving one or more of the problems of the prior art.

Disclosure of Invention

The following presents a simplified summary of the disclosure in order to provide a basic understanding of some aspects of the disclosure. However, it should be understood that this summary is not an exhaustive overview of the disclosure. It is not intended to identify key or critical elements of the disclosure or to delineate the scope of the disclosure. Its purpose is to present some concepts related to the disclosure in a simplified form as a prelude to the more detailed description that is presented later.

According to one aspect of the present disclosure, there is provided a client-independent analysis method of service data, including: receiving, by the first analysis function, a data analysis request of the client through the network function or the application function, the data analysis request including an autonomous analysis service field, an address of the second analysis function to be used, and a client ID; identifying, by the first analysis function, a value of an autonomous analysis service field in the data analysis request; in the case where the value indicates that the data analysis request is a client autonomous analysis request, confirming, by a first analysis function, whether the client indicated by the client ID is a contracted client, and in the case where the client is a contracted client, acquiring, by the first analysis function, contract information of the client; querying a network element corresponding to the subscription information and providing service for the client by a first analysis function; collecting, by a first analysis function, operator data relating to the customer from the network element and transmitting the collected operator data to a second analysis function in dependence of the address; the data analysis results are received by the first analysis function from the second analysis function and decisions are made based on the data analysis results.

According to another aspect of the present disclosure, a customer-autonomous analysis device for providing business data includes: a memory having instructions stored thereon; and a processor configured to execute instructions stored on the memory to perform the method as described above.

According to yet another aspect of the present disclosure, there is provided a computer-readable storage medium comprising computer-executable instructions which, when executed by one or more processors, cause the one or more processors to perform a method according to the above aspects of the present disclosure.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the disclosure and together with the description, serve to explain the principles of the disclosure.

The disclosure may be more clearly understood from the following detailed description taken in conjunction with the accompanying drawings in which:

fig. 1 is a schematic diagram showing a method of requesting data analysis by a general public user in the related art.

Fig. 2 shows a schematic diagram of analysis functional decoupling according to one embodiment of the present disclosure.

Fig. 3 illustrates a flow diagram of a method for customer-autonomous analysis of business data according to one particular embodiment of the present disclosure.

Fig. 4 illustrates an example of an autonomic analysis service field, according to one embodiment of the present disclosure.

Fig. 5 shows a schematic diagram of a process in which a customer signs up with an operator and registers for provisioning services according to an embodiment of the present disclosure.

Fig. 6 illustrates an example of a usage pattern of customer subscription information according to one embodiment of the present disclosure.

Fig. 7 illustrates an example of a usage pattern of customer subscription information according to another embodiment of the present disclosure.

Fig. 8 is a schematic diagram illustrating an example of a first analysis function and a second analysis function according to one embodiment of the present disclosure.

FIG. 9 illustrates an example of a customer's list of analytics functions, model types, and addresses, according to one embodiment of the present disclosure.

FIG. 10 illustrates an exemplary configuration of a computing device in which embodiments according to the present disclosure may be implemented.

Detailed Description

The following detailed description is made with reference to the accompanying drawings and is provided to assist in a comprehensive understanding of various example embodiments of the disclosure. The following description includes various details to aid in understanding, but these are to be considered merely examples and are not intended to limit the disclosure, which is defined by the appended claims and their equivalents. The words and phrases used in the following description are only intended to provide a clear and consistent understanding of the present disclosure. In addition, descriptions of well-known structures, functions and configurations may be omitted for clarity and conciseness. Those of ordinary skill in the art will recognize that various changes and modifications of the examples described herein can be made without departing from the spirit and scope of the present disclosure.

In the current 3GPP NWDAF architecture, more than ten kinds of intelligent analysis can be provided for clients. But the architecture mechanism does not distinguish between general public users and customers such as businesses and governments (hereinafter referred to as customers), resulting in customer concerns about the security of data analysis.

Fig. 1 is a schematic diagram showing a method of requesting data analysis by a general public user in the related art. The method for data analysis of the general public user request comprises the following steps:

nf (network function) or AF (application function) sends a data analysis request to the reasoning NWDAF (Network Data Analytics Function, network data analysis function), carrying an analysis ID (Analytics ID).

2. The auxiliary reasoning NWDAF queries the NRF (Network Repository Function ) for the model address corresponding to the analysis ID and gathers the data used.

3. And accessing a training NWDAF address used for analyzing the data, and returning to the auxiliary reasoning NWDAF to make decisions after the analysis is finished. As shown in fig. 1, the training NWDAF is trained by the operator.

From the above flow, in the prior art, the training NWDAF for data analysis is trained and managed by the operator, and thus this may lead to a concern for the security of data analysis for the client.

To relieve customer concerns, the present disclosure proposes enabling customers to autonomously conduct business data analysis.

"customer" in this disclosure refers to any entity such as an enterprise, business, industry, association, school, public institution, any organization, group, and government. The terms enterprise or industry referred to in the following description and figures may be equivalent to any entity as described above.

In one embodiment, the present disclosure proposes to decouple the analysis functions into a secondary reasoning analysis function (i.e., a first analysis function) and a customer analysis function (i.e., a second analysis function).

Fig. 2 shows a schematic diagram of analysis functional decoupling according to one embodiment of the present disclosure. The supplementary reasoning NWDAF in fig. 2 is one example of a first analysis function and the training model NWDAF is one example of a second analysis function.

By decoupling the analysis function, a second analysis function can be established autonomously by the client, and the service data of the client and the data provided by the operator are analyzed autonomously by the second analysis function, so that the privacy of the client can be protected, and the worry of the client on the analysis safety of the data is relieved.

According to one embodiment of the present disclosure, a method for autonomous analysis of service data by a client is provided, including: receiving, by the first analysis function, a data analysis request of the client via the Network Function (NF) or the Application Function (AF), the data analysis request comprising an autonomous analysis service field, an address of the second analysis function to be used, and a client identifier (client ID); identifying, by the first analysis function, a value of an autonomous analysis service field in the data analysis request; in the case where the value indicates that the data analysis request is a client autonomous analysis request, confirming, by the first analysis function, whether the client indicated by the client ID is a contracted client, and in the case where the client is a contracted client, acquiring, by the first analysis function, contract information of the client; querying a network element corresponding to the subscription information and providing service for the client by a first analysis function; collecting, by a first analysis function, operator data relating to the customer from the network element and transmitting the collected operator data to a second analysis function in dependence of the address; the data analysis results are received by the first analysis function from the second analysis function and decisions are made based on the data analysis results.

In the present disclosure, for example, the first analysis function may be a network data analysis function (NWDAF) of an operator in a 5G network, and the second analysis function may be an NWDAF or similar AI system autonomously established by a client for performing autonomous analysis. The second analysis function may be a function of performing analysis using a fixed model, or a function of using a training model obtained through training, which enables intelligent analysis.

One customer may have multiple second analysis functions and different customers may have different second analysis functions. In one embodiment, different customers may also share some second analysis functionality. The address of the second analysis function may be used to locate the second analysis function to be used.

In the present disclosure, the function may be a module such as any one of software, firmware, and hardware, or may be a network element in a network.

In one embodiment, the second analysis function may analyze based on operator data provided by the operator and the customer's own business data to obtain data analysis results. Since the second analysis function for analyzing the customer's own business data is a function autonomously established by the customer, the customer's data privacy can be more effectively protected.

In one embodiment, in the case where the value of the autonomic analysis services field indicates that the data analysis request is a customer autonomic analysis request, the step of confirming, by the first analysis function, whether the customer represented by the customer ID is a contracted customer may include: a query request is issued by the first analysis function to the unified data management function (Unified Data Management, UDM) to confirm whether the client represented by the client ID is a contracted client. The contracted client refers to a client contracted with an operator and registered as a client capable of autonomously analyzing service data.

In one embodiment, in the case where the value of the autonomic analysis services field indicates that the data analysis request is a customer autonomic analysis request, the step of confirming, by the first analysis function, whether the customer represented by the customer ID is a contracted customer may include: the subscription information of the client stored in advance in the first analysis function is referred to by the first analysis function to confirm whether the client indicated by the client ID is a subscribed client. Since the subscription information of the client is stored in the first analysis function in advance, the first analysis function can confirm whether the client is a subscribed client by referring only to the subscription information stored in the first analysis function itself without sending a query request to other functions.

In one embodiment, the subscription information may include a customer ID of the subscribed customer.

In one embodiment, the first analysis function may query a network repository function for network elements corresponding to subscription information that serve the customer. The network element may include an AMF (access and mobility management function), an SMF (session management function), and the like. By querying the network elements serving the customers, operator data relating to the corresponding customers may be collected from these network elements by the first analysis function, so that these operator data may be provided to the second analysis function to assist the second analysis function in data analysis.

In one embodiment, the second analysis function may be located at the customer's own device. That is, the second analysis function may be completely separate from the operator's equipment. In one embodiment, the second analysis function may be located in a device of the network operator, but logically separate and independent from other functions of the network operator. Whether or not the second analysis function is located in the network operator's device, the concern of the customer about the security of its own traffic data can be relieved as it is logically separate and independent from the other functions of the network operator.

In one embodiment, the second analysis function may comprise an intelligent model that has been trained by the customer autonomously based on the operator data and the customer's business data. Because the intelligent model is trained based on the operator data and the business data of the customer, the intelligent model in the second analysis function can perform autonomous analysis on the data more accurately, efficiently or intelligently.

In one embodiment, one second analysis function may contain multiple smart models, and the data analysis request may also contain the addresses of the smart models in the second analysis function to be used. In this case, the step of transmitting the collected operator data to the second analysis function according to the address of the second analysis function to be used may include: the address of the smart model is sent to a second analysis function for data analysis by the smart model represented by the address of the smart model in the second analysis function. In this case, since the data analysis request also contains the address of the smart model in the second analysis function to be used, the data analysis can be performed by a specific smart model in a specific second analysis function, whereby the pertinence and the specialization of the data analysis can be improved by different types of smart models in different types of analysis functions.

In one embodiment, the second analysis function may be a network data analysis function or an artificial intelligence system or any similar function.

Fig. 3 illustrates a flow diagram of a method for customer-autonomous analysis of business data according to one particular embodiment of the present disclosure. The customer autonomy analysis method of this particular embodiment of the present disclosure is described below in connection with fig. 3.

The flow example shown in fig. 3 mainly includes the following steps:

(1) the NF or AF sends a data analysis request to the reasoning NWDAF (which is one example of the first analysis function) carrying the autonomous analysis service field 1, address information of the industry NWDAF used to analyze the data (i.e. the business NWDAF or the customer industry NWDAF, which is one example of the second analysis function), and the customer ID.

(2) The supplementary reasoning NWDAF identifies the autonomous analysis service field, i.e. the field value 1 of this field.

(3) In one embodiment, the assisted reasoning NWDAF may query the UDM to confirm whether the client is a subscribing client and obtain subscription information. If the client is a contracted client, executing the next step, if the confirmation fails, terminating the flow, and returning a refusal service notification.

In another embodiment, if the NWDAF storage mode is adopted at the time of signing, i.e. the signing information is already stored in the supplementary reasoning NWDAF, the UDM may not be accessed and the supplementary reasoning NWDAF may directly make the decision by querying the local information.

(4) The supplementary reasoning NWDAF queries the NRF for network element information corresponding to the subscription information, e.g. network elements such as AMF and/or SMA providing services for the enterprise (i.e. customer), and starts to collect operator data from these network elements.

(5) After the data collection is finished, the corresponding enterprise NWDAF is accessed according to the enterprise NWDAF address, and the enterprise NWDAF performs comprehensive data analysis based on the collected operator data and the enterprise (i.e. client) own business data, and after the analysis is finished, the data is returned to the auxiliary reasoning NWDAF to make a decision.

In order to implement the above method, the customer needs to register with the operator and open an autonomous analysis service. For this purpose, an autonomous analysis service field needs to be added to the client. Fig. 4 illustrates an example of an autonomic analysis service field, according to one embodiment of the present disclosure. As shown in fig. 4, 0 represents data analysis of a general user (i.e., the aforementioned general public user) request, and 1 represents autonomous analysis of a contracted client request.

The customer needs to sign up with the operator and register for provisioning services. Fig. 5 shows a schematic diagram of a process in which a customer signs up with an operator and registers for provisioning services according to an embodiment of the present disclosure. In one embodiment, the customer may register to open the autonomic analysis service through a NEF (Network Exposure Function, network open function), industry portal, open system, or the like. In one embodiment, the operator may store a customer ID, DNN (data network name), slice ID, etc. associated with the customer in the UDM.

Fig. 6 illustrates an example of a usage pattern of customer subscription information according to one embodiment of the present disclosure. Fig. 7 illustrates an example of a usage pattern of customer subscription information according to another embodiment of the present disclosure.

As shown in fig. 6, in one embodiment, after receiving the data analysis request, the auxiliary reasoning NWDAF may identify that the field value of the autonomous analysis service field is 1, and initiate a query request to the UDM, compare the client ID, confirm that the client is a subscribed client, and then trigger the autonomous analysis service.

In another embodiment, as shown in fig. 7, after the registration procedure, the UDM may issue the associated client ID, DNN, slice ID to the assisted reasoning NWDAF. In the subsequent flow, according to the autonomous analysis service field and the client ID, the auxiliary reasoning NWDAF can directly confirm the client identity and know the subscription information by querying the client information stored in itself after receiving the data analysis request.

As shown in fig. 2, the decoupling of conventional NWDAF functions is divided in this disclosure into a reasoning-aided NWDAF and a training NWDAF, where the training NWDAF is responsible for developing model training and data analysis from the customer service data set.

As shown in fig. 8, the training NWDAF of the operator (which is one example of the first analysis function) uses the data collected by the operator and can perform training of different models according to the analysis ID (Analyt ics ID) and registers the trained models and corresponding analysis IDs to the NRF. The customer NWDAF, which is one example of a second analysis function, is shown in fig. 8 as an enterprise autonomous training NWDAF, may obtain business data of the customer, such as business APP, UE, background data, and train in conjunction with operator data to meet the specific needs of the customer.

When a client requests data analysis, the address of the client model can be carried in the request so as to be correctly routed to the client NWDAF, thereby performing autonomous analysis. In one embodiment, a plurality of analytical models or smart models may be included in one second analytical function, in which case the analytical model or smart model to be used may be determined by the address of the client model.

In one embodiment, the client may also carry a training model type in the request to more explicitly specify the type of data analysis when requesting the data analysis. Fig. 9 shows an example of a second list of analysis functions (shown as NWDAF as an example) of a client and corresponding model types and addresses, according to one embodiment of the present disclosure. Although not shown, one customer may have a plurality of second analysis functions, and each second analysis function may have a plurality of models. Furthermore, a specific second analysis function may be located by an address of the second analysis function, and a specific model in the specific second analysis function may be located by an address of the model.

Compared with the prior art, the method disclosed by the invention has the main advantages that:

1. the prior art provides model training reasoning and intelligent analysis for 5G users. But is limited to operator side provisioning only. Security-sensitive high-value clients are reluctant to analyze on the operator side for data and analysis training. The present disclosure may avoid security concerns for the customer.

2. In the method, in the process of grasping the enterprise client through data analysis and model training, operators only provide cooperation, so that the worry of enterprises on data and data analysis safety is effectively avoided, the application range and the scene of 5G intelligent analysis are expanded, the client requirements are met, and the application of NWDAF is easier to fall to the ground.

3. The existing international standard and industry standard do not consider the autonomous analysis requirement of key enterprise clients and do not provide a corresponding technical scheme.

4. The technology of the present disclosure provides a method for supporting autonomous analysis of clients, which includes corresponding autonomous analysis service registration opening, data model training, field definition and autonomous service flow. Makes up for the defect of standardization.

One particular embodiment is described below. A customer signs up with an operator, and requires to provide autonomous analysis service, and data are acquired from network elements such as AMF used by the customer when autonomous service is performed.

The steps are briefly described:

NF or AF sends a data analysis request to the supplementary inference NWDAF, carrying the autonomous analysis service field 1, m_address and the client ID.

The supplementary inference NWDAF identifies a field value of 1 for the autonomous analysis service field.

The auxiliary reasoning NWDAF inquires and confirms whether the client A is a subscriber or not from the UDM to acquire subscription information.

The auxiliary reasoning NWDAF inquires the NRF about network element information corresponding to the subscription information, and starts data collection.

After the data collection is finished, accessing M_address, analyzing the data by using a model M, and returning to the auxiliary reasoning NWDAF for decision after the analysis is finished.

In one embodiment of the present disclosure, a method for supporting autonomous development of intelligent analysis by a client is provided, including corresponding autonomous analysis service registration opening, data model training, field definition, and autonomous service flow. In the autonomic analysis service registration provisioning, the UDM adds an analytical service field identifying both options for providing self-service and operator service. The operator NWDAF (assisted reasoning NWDAF) can query or pre-store the requirements and rights of the autonomous analysis of the client by means of UDM, obtain the relevant data according to the autonomous requirements and route to an analysis function such as NWDAF or AI-like system of the autonomous analysis of the client itself.

The method and the system can be used for operators to meet important clients, provide the clients with the capabilities of autonomous intelligent analysis and the like, and avoid the worry of the clients on the privacy of data analysis.

The patent provides a mechanism for autonomous intelligent analysis of enterprise clients. Through the cooperation of operators, customers can fully exert the advantages of background business data resources, carry out intelligent analysis of data by dead weight, and avoid the worry of privacy leakage.

In the implementation, the client applies for registering and opening the autonomous intelligent analysis service through a portal or an opening system and signs up for the triggering condition. After the client is online, judging whether to trigger an autonomous intelligent analysis service, and performing auxiliary reasoning, namely, acquiring relevant NF data corresponding to the client event by the NWDAF, and then routing the NF data to an analysis function, such as the NWDAF or a similar intelligent system, of the subscribed client for performing the autonomous service. The system integrates data provided by operators and business data of clients, and performs autonomous intelligent analysis.

Fig. 10 illustrates an exemplary configuration of a computing device 1200 capable of implementing embodiments in accordance with the present disclosure.

Computing device 1200 is an example of a hardware device that can employ the above aspects of the disclosure. Computing device 1200 may be any machine configured to perform processing and/or calculations. Computing device 1200 may be, but is not limited to, a workstation, a server, a desktop computer, a laptop computer, a tablet computer, a Personal Data Assistant (PDA), a smart phone, an in-vehicle computer, or a combination thereof.

As shown in fig. 10, computing device 1200 may include one or more elements that may be connected to or in communication with a bus 1202 via one or more interfaces. The bus 1202 may include, but is not limited to, an industry standard architecture (Industry Standard Architecture, ISA) bus, a micro channel architecture (Micro Channel Architecture, MCA) bus, an Enhanced ISA (EISA) bus, a Video Electronics Standards Association (VESA) local bus, and a Peripheral Component Interconnect (PCI) bus. Computing device 1200 may include, for example, one or more processors 1204, one or more input devices 1206, and one or more output devices 1208. The one or more processors 1204 may be any kind of processor and may include, but are not limited to, one or more general purpose processors or special purpose processors (such as special purpose processing chips). The processor 1204 may be configured, for example, to implement the customer-autonomous analysis method of business data described above. Input device 1206 may be any type of input device capable of inputting information to a computing device, and may include, but is not limited to, a mouse, keyboard, touch screen, microphone, and/or remote controller. The output device 1208 may be any type of device capable of presenting information and may include, but is not limited to, a display, speakers, video/audio output terminals, vibrators, and/or printers.

The computing device 1200 may also include or be connected to a non-transitory storage device 1214, which non-transitory storage device 1214 may be any storage device that is non-transitory and that may enable data storage, and may include, but is not limited to, disk drives, optical storage devices, solid state memory, floppy disks, flexible disks, hard disks, magnetic tape, or any other magnetic medium, compact disk or any other optical medium, cache memory and/or any other memory chip or module, and/or any other medium from which a computer may read data, instructions, and/or code. Computing device 1200 may also include Random Access Memory (RAM) 1210 and Read Only Memory (ROM) 1212. The ROM 1212 may store programs, utilities or processes to be executed in a non-volatile manner. The RAM 1210 may provide volatile data storage and stores instructions related to the operation of the computing device 1200. The computing device 1200 may also include a network/bus interface 1216 coupled to the data link 1218. The network/bus interface 1216 can be any kind of device or system capable of enabling communication with external equipment and/or networks, and can include, but is not limited to, modems, network cards, infrared communication devices, wireless communication devices, and/or chipsets (such as bluetooth @) ^TM Devices, 802.11 devices, wiFi devices, wiMax devices, cellular communication facilities, etc.).

The present disclosure may be implemented as any combination of apparatuses, systems, integrated circuits, and computer programs on a non-transitory computer readable medium. One or more processors may be implemented as an Integrated Circuit (IC), application Specific Integrated Circuit (ASIC), or large scale integrated circuit (LSI), system LSI, super LSI, or ultra LSI assembly that performs some or all of the functions described in this disclosure.

The present disclosure includes the use of software, applications, computer programs, or algorithms. The software, application, computer program or algorithm may be stored on a non-transitory computer readable medium to cause a computer, such as one or more processors, to perform the steps described above and depicted in the drawings. For example, one or more memories may store software or algorithms in executable instructions and one or more processors may associate a set of instructions to execute the software or algorithms to provide various functions in accordance with the embodiments described in this disclosure.

The software and computer programs (which may also be referred to as programs, software applications, components, or code) include machine instructions for a programmable processor, and may be implemented in a high-level procedural, object-oriented, functional, logical, or assembly or machine language. The term "computer-readable medium" refers to any computer program product, apparatus or device, such as magnetic disks, optical disks, solid state memory devices, memory, and Programmable Logic Devices (PLDs), for providing machine instructions or data to a programmable data processor, including computer-readable media that receives machine instructions as a computer-readable signal.

By way of example, computer-readable media can comprise Dynamic Random Access Memory (DRAM), random Access Memory (RAM), read Only Memory (ROM), electrically erasable read only memory (EEPROM), compact disk read only memory (CD-ROM) or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to carry or store desired computer-readable program code in the form of instructions or data structures and that can be accessed by a general purpose or special purpose computer or general purpose or special purpose processor. Disk or disc, as used herein, includes Compact Disc (CD), laser disc, optical disc, digital Versatile Disc (DVD), floppy disk and blu-ray disc where disks usually reproduce data magnetically, while discs reproduce data optically with lasers. Combinations of the above are also included within the scope of computer-readable media.

The subject matter of the present disclosure is provided as examples of apparatuses, systems, methods, and programs for performing the features described in the present disclosure. However, other features or variations are contemplated in addition to the features described above. It is contemplated that the implementation of the components and functions of the present disclosure may be accomplished with any emerging technology that may replace any of the above-described implementation technologies.

In addition, the foregoing description provides examples without limiting the scope, applicability, or configuration set forth in the claims. Changes may be made in the function and arrangement of elements discussed without departing from the spirit and scope of the disclosure. Various embodiments may omit, replace, or add various procedures or components as appropriate. For example, features described with respect to certain embodiments may be combined in other embodiments.

In addition, in the description of the present disclosure, the terms "first," "second," "third," etc. are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or order.

Similarly, although operations are depicted in the drawings in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed, to achieve desirable results. In some cases, multitasking and parallel processing may be advantageous.

Claims

1. A method for autonomous analysis of business data by a customer, comprising:

receiving, by the first analysis function, a data analysis request of the client through the network function or the application function, the data analysis request including an autonomous analysis service field, an address of the second analysis function to be used, and a client ID;

identifying, by the first analysis function, a value of an autonomous analysis service field in the data analysis request;

in the case where the value indicates that the data analysis request is a client autonomous analysis request, confirming, by a first analysis function, whether the client indicated by the client ID is a contracted client, and in the case where the client is a contracted client, acquiring, by the first analysis function, contract information of the client;

querying a network element corresponding to the subscription information and providing service for the client by a first analysis function;

collecting, by a first analysis function, operator data relating to the customer from the network element and transmitting the collected operator data to a second analysis function in dependence of the address; and

the data analysis results are received by the first analysis function from the second analysis function and decisions are made based on the data analysis results.

2. The method of claim 1, wherein,

the second analysis function is an analysis function established autonomously by the client, and

a second analysis function analyzes based on the operator data and the customer's business data to obtain the data analysis result.

3. The method of claim 1, wherein, in the event that the value indicates that the data analysis request is a customer-autonomous analysis request, validating, by the first analysis function, whether the customer represented by the customer ID is a contracted customer comprises:

a query request is issued by the first analysis function to the unified data management function to confirm whether the client represented by the client ID is a contracted client.

4. The method of claim 1, wherein, in the event that the value indicates that the data analysis request is a customer-autonomous analysis request, validating, by the first analysis function, whether the customer represented by the customer ID is a contracted customer comprises:

the subscription information of the client stored in advance in the first analysis function is referred to by the first analysis function to confirm whether the client indicated by the client ID is a subscribed client.

5. The method of claim 1, wherein a first analysis function queries a network repository function for network elements corresponding to the subscription information that serve the customer.

6. The method of claim 1, wherein,

the second analysis function is located at the customer's own device, or

The second analysis function is located in the network operator's device but logically separate and independent from the other functions of the network operator.

7. The method of claim 1, wherein the second analysis function comprises an intelligent model that has been trained by the customer autonomously based on operator data and customer traffic data.

8. The method of claim 7, wherein the second analysis function contains a plurality of the smart models, and the data analysis request further contains an address of the smart model in the second analysis function to be used, and

sending the collected operator data to a second analysis function according to the address comprises:

the address of the smart model is sent to a second analysis function for data analysis by the smart model represented by the address of the smart model in the second analysis function.

9. The method of claim 7, wherein the second analysis function is a network data analysis function or an artificial intelligence system.

10. A customer-independent analysis device of business data, comprising:

a memory having instructions stored thereon; and

a processor configured to execute instructions stored on the memory to perform the method according to any one of claims 1 to 9.

11. A computer-readable storage medium comprising computer-executable instructions that, when executed by one or more processors, cause the one or more processors to perform the method of any of claims 1-9.