Data processing method and device, equipment and storage medium

Abstract

The invention provides a data processing method and device, equipment and a storage medium, and relates to the technical field of communication. The method comprises the steps of receiving perception service request information sent by a consumer network element, determining whether a target intelligent model exists or not based on the perception service request information, obtaining perception reasoning data under the condition that the target intelligent model exists, determining a perception result based on the target intelligent model and the perception reasoning data, and sending the perception result to the consumer network element. And the sensing result is sent to the consumer network element, so that the communication intelligence level is improved.

Description

Background Art

[0002] With the development of wireless communication technology and the increasing demand for high-quality data transmission, communication systems are evolving towards a higher level. Among the related technologies, the integration of sensing and communication technology has become a highly anticipated cutting-edge technology. However, the current level of intelligence in sensing and communication technology is relatively low. How to improve the level of intelligence in sensing and communication technology is an urgent problem to be solved by those skilled in the art. Summary of the invention

[0003] The present disclosure provides a data processing method, apparatus, device and storage medium, which at least to a certain extent improve the intelligence level of communication.

[0004] Other features and advantages of the present disclosure will become apparent from the following detailed description, or may be learned in part by the practice of the present disclosure.

[0005] According to one aspect of the present disclosure, a data processing method is provided, which is applied to a sensing network element (Sensing Function, SF), including: Receiving the sensing service request information sent by the consumer network element; Determine whether a target intelligent model exists based on the perceived service request information; In the presence of a target intelligent model, acquiring perception and reasoning data; Determine the perception result based on the target intelligent model and the perception reasoning data; The sensing results are sent to the consumer network element.

[0006] In one embodiment of the present disclosure, the method further includes: In the absence of a target intelligent model, a network data analysis function NWDAF for training the target intelligent model is determined.

[0007] In one embodiment of the present disclosure, the method further includes: Send a subscription request to NWDAF to enable NWDAF to train the target intelligent model.

[0008] In one embodiment of the present disclosure, the method further includes: Receive the training results sent by NWDAF; determining training instruction information based on the training results; The training instruction information is sent to the NWDAF, so that the NWDAF stops or changes the training of the target intelligent model based on the training instruction information.

[0009] According to another aspect of the present disclosure, a data processing method is provided, which is applied to a network data analysis function NWDAF, including: In case of receiving a subscription request sent by the perception network element SF, training the target intelligent model based on the analysis identifier in the subscription request; When the training stop condition is met, sending the training result to the SF, so that the SF determines the training instruction information based on the training result; Stop or change the training of the target intelligent model based on the training instruction information.

[0010] In one embodiment of the present disclosure, the method further includes: Upon receiving a subscription request sent by SF, determine whether NWDAF meets the federated learning requirements based on the data privacy requirement data and computing power requirement data in the subscription request; In case the NWDAF does not meet the federated learning requirements, a subscription request is sent to a second NWDAF.

[0011] According to another aspect of the present disclosure, a data processing device is provided, which is applied to a SF network element, including: A first receiving module, configured to receive the perception service request information sent by the consumer network element; A first determination module is used to determine whether a target intelligent model exists based on the perceived service request information; An acquisition module, used to acquire perception and reasoning data in the presence of a target intelligent model; A second determination module is used to determine the perception result based on the target intelligent model and the perception reasoning data; The first sending module is used to send the sensing result to the consumer network element.

[0012] In one embodiment of the present disclosure, the device further includes: The fourth determination module is used to determine the network data analysis function NWDAF for training the target intelligent model when there is no target intelligent model.

[0013] In one embodiment of the present disclosure, the device further includes: The second sending module is used to send a subscription request to NWDAF so that NWDAF can train the target intelligent model.

[0014] In one embodiment of the present disclosure, the device further includes: The second receiving module is used to receive the training result sent by NWDAF; A fifth determining module, configured to determine training instruction information based on the training result; The change module is used to send the training instruction information to the NWDAF so that the NWDAF stops or changes the training of the target intelligent model based on the training instruction information.

[0015] According to another aspect of the present disclosure, there is provided a data processing device, comprising: A training module, configured to train a target intelligent model based on an analysis identifier in a subscription request sent by a sensing network element SF when receiving the subscription request; A third determination module is used to send the training result to the SF when the training stop condition is met, so that the SF determines the training instruction information based on the training result; The change module is used to stop or change the training of the target intelligent model based on the training instruction information.

[0016] In one embodiment of the present disclosure, the device further includes: A fifth determination module, configured to determine whether NWDAF meets the federated learning requirements based on the data privacy requirement data and the computing power requirement data in the subscription request when receiving the subscription request sent by the SF; The third sending module is used to send the subscription request to the second NWDAF when the NWDAF does not meet the federated learning requirements.

[0017] According to another aspect of the present disclosure, an electronic device is provided, comprising: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to perform the above-mentioned data processing method by executing the executable instructions.

[0018] According to another aspect of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the above-mentioned data processing method is implemented.

[0019] The data processing method, apparatus, device and storage medium provided by the embodiments of the present disclosure receive perception service request information sent by a consumer network element, determine whether there is a target intelligent model based on the perception service request information, obtain perception reasoning data when a target intelligent model exists, determine the perception result based on the target intelligent model and the perception reasoning data, and send the perception result to the consumer network element, thereby improving the level of communication intelligence.

[0020] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The accompanying drawings herein are incorporated into the specification and constitute a part of the specification, illustrate embodiments consistent with the present disclosure, and together with the specification are used to explain the principles of the present disclosure. Obviously, the accompanying drawings described below are only some embodiments of the present disclosure, and for ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without creative work.

[0022] Figure 1 A flow chart of a data processing method in an embodiment of the present disclosure is shown; Figure 2 Another data processing method flow chart in the embodiment of the present disclosure is shown; Figure 3 A flow chart of another data processing method in an embodiment of the present disclosure is shown; Figure 4 A flow chart of another data processing method in an embodiment of the present disclosure is shown; Figure 5 A flow chart of another data processing method in an embodiment of the present disclosure is shown; Figure 6 A flow chart of another data processing method in an embodiment of the present disclosure is shown; Figure 7 A signaling diagram of a data processing method in an embodiment of the present disclosure is shown; Figure 8 A data processing device architecture diagram according to an embodiment of the present disclosure is shown; Fig. 9 Another data processing device architecture diagram according to an embodiment of the present disclosure is shown; Fig.10 A structural block diagram of an electronic device in an embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0023] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that the disclosure will be more comprehensive and complete and to fully convey the concepts of the example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0024] In addition, the accompanying drawings are only schematic illustrations of the present disclosure and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor devices and / or microcontroller devices.

[0025] It should be understood that the various steps described in the method embodiments of the present disclosure may be performed in different orders and / or in parallel. In addition, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present disclosure is not limited in this respect.

[0026] It should be noted that the concepts such as "first" and "second" mentioned in the present disclosure are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0027] It should be noted that the modifications of "one" and "plurality" mentioned in the present disclosure are illustrative rather than restrictive, and those skilled in the art should understand that unless otherwise clearly indicated in the context, it should be understood as "one or more".

[0028] In order to solve the above problems, the embodiments of the present disclosure provide a data processing method, apparatus, device and storage medium.

[0029] Figure 1 A flow chart of a data processing method in an embodiment of the present disclosure is shown.

[0030] like Figure 1 As shown, the method may include: S110, receiving the perception service request information sent by the consumer network element.

[0031] In some embodiments, the consumer network element may include an application function (AF) network element and a perception service consumer.

[0032] In some embodiments, the sensing service request information may include service type data, service requirement data, and sensing node information.

[0033] In some embodiments, before S110, the method may further include SF and NWDAF registering with the Network Repository Function (NRF) through Nnrf_NFManagement_NFRegister service, respectively. The information sent by NWDAF to NRF may include NWDAF's support identification for AI and / or ML-based perception capabilities, federated learning capability identification, ML model interoperability information, a list of vendors sharing ML models with NWDAF, the format of ML models, and an operating environment of ML models.

[0034] Exemplarily, the federated learning capability identifier may include a federated learning client and a federated learning server.

[0035] The information sent by SF to NRF may include SF's support identification for AI and / or ML-based perception, ML model interoperability information, SF vendor identification, the format of ML models supported by SF, and SF operating environment.

[0036] S120, determining whether a target intelligent model exists based on the perceived service request information.

[0037] In some embodiments, before S120, the method may further include the SF determining whether to use an AI model or an ML model based on its own configuration information and perceived service request information.

[0038] When it is determined that an AI model or an ML model needs to be used, determine whether a target intelligent model exists.

[0039] S130, in the presence of a target intelligent model, obtaining perception reasoning data.

[0040] In some embodiments, SF can input perception reasoning data into an AI model or an ML model to obtain perception results.

[0041] In some embodiments, SF can obtain perception and reasoning data from UE or RAN based on various methods such as base station autonomous transmission and reception perception, transmission and reception perception between base stations, and UE transmission and base station reception perception.

[0042] S140, determining the perception result based on the target intelligent model and the perception reasoning data.

[0043] In some embodiments, SF can input perception reasoning data into an AI or ML model to obtain perception results.

[0044] S150: Send the sensing result to the consumer network element.

[0045] The data processing method provided by the embodiments of the present disclosure receives perception service request information sent by a consumer network element, determines whether there is a target intelligent model based on the perception service request information, obtains perception reasoning data when a target intelligent model exists, determines a perception result based on the target intelligent model and the perception reasoning data, and sends the perception result to the consumer network element, thereby improving the level of communication intelligence.

[0046] Figure 2 A flow chart of another data processing method in an embodiment of the present disclosure is shown.

[0047] like Figure 2 As shown, the method may include: S210, receiving the perception service request information sent by the consumer network element; S220, determining whether a target intelligent model exists based on the perceived service request information; S230: If the target intelligent model does not exist, determine a network data analysis function NWDAF for training the target intelligent model.

[0048] In some embodiments, SF may discover NWDAF that supports training a target intelligent model through NRF.

[0049] The data processing method provided by the embodiments of the present disclosure receives perception service request information sent by a consumer network element, determines whether there is a target intelligent model based on the perception service request information, obtains perception reasoning data when a target intelligent model exists, determines a perception result based on the target intelligent model and the perception reasoning data, and sends the perception result to the consumer network element, thereby improving the level of communication intelligence.

[0050] Figure 3 A flow chart of another data processing method in an embodiment of the present disclosure is shown.

[0051] like Figure 3 As shown, the method may include: S310, receiving the perception service request information sent by the consumer network element; S320, determining whether a target intelligent model exists based on the perceived service request information; S330: If the target intelligent model does not exist, determine a network data analysis function NWDAF for training the target intelligent model.

[0052] S340: Send a subscription request to NWDAF so that NWDAF can train the target intelligent model.

[0053] In some embodiments, the SF network element may send a subscription request to the NWDAF determined for training the target intelligent model through the Nnwdaf_MLModelProvision_Subscribe service.

[0054] In some embodiments, the subscription request may include an analysis identification, ML model filtering information, and ML model interoperation information.

[0055] In some embodiments, after the NWDAF receives the subscription request, it can determine whether to use federated learning to train the model based on the data privacy requirement data and computing power requirement data required for training the target intelligent model.

[0056] When it is determined that the model is trained in a federated learning manner, the target model training logical function (MTLF) can be determined by NWDAF as the HFL client.

[0057] In some embodiments, the target model training logic function determined by NWDAF may include an analysis identifier based on whether the request is supported, whether AI and / or intelligent perception is supported, and whether the model can be shared with the HFL server.

[0058] In some embodiments, when the target MTLF determines that it cannot currently serve as an HFL client, a new MTLF may serve as the HFL client.

[0059] The data processing method provided by the embodiments of the present disclosure receives perception service request information sent by a consumer network element, determines whether there is a target intelligent model based on the perception service request information, obtains perception reasoning data when a target intelligent model exists, determines a perception result based on the target intelligent model and the perception reasoning data, and sends the perception result to the consumer network element, thereby improving the level of communication intelligence.

[0060] Figure 4 A flow chart of another data processing method in an embodiment of the present disclosure is shown.

[0061] like Figure 4 As shown, the method may include: S410, receiving the perception service request information sent by the consumer network element; S420, determining whether a target intelligent model exists based on the perceived service request information; S430: If the target intelligent model does not exist, determine a network data analysis function NWDAF for training the target intelligent model.

[0062] In some embodiments, the NWDAF training target model may include the HFL client in the NWDAF determining the data required for training the target model based on information such as the analysis ID in the subscription request, collecting data if there is no corresponding data locally, and then training based on the local data to update the local model.

[0063] S440: Send a subscription request to NWDAF to enable NWDAF to train the target intelligent model.

[0064] In some embodiments, the subscription request may include federated learning initial parameters, which include initial global model machine parameters, client maximum response time, model subscription request, and ML model interoperability information.

[0065] S450: Receive the training result sent by NWDAF.

[0066] In some embodiments, the HFL client may report the trained local model parameters to the HFL server NWDAF via Nnwdaf_MLModelTraining_Notify or Nnwdaf_MLModelTrainingInfo_Response, and the HFL server aggregates these model parameters to update the global model.

[0067] S460: Determine training instruction information based on the training result.

[0068] S470: Send the training instruction information to the NWDAF, so that the NWDAF stops or changes the training of the target intelligent model based on the training instruction information.

[0069] In some embodiments, after HFL training is completed, the subscribed ML model information is sent to SF through the Nnwdaf_MLModelProvision_Notify service.

[0070] In some embodiments, after SF receives the target intelligent model, it collects perception reasoning data to perform perception result reasoning and collects measurement data according to different perception models.

[0071] The data processing method provided by the embodiments of the present disclosure receives perception service request information sent by a consumer network element, determines whether there is a target intelligent model based on the perception service request information, obtains perception reasoning data when a target intelligent model exists, determines a perception result based on the target intelligent model and the perception reasoning data, and sends the perception result to the consumer network element, thereby improving the level of communication intelligence.

[0072] Based on the same inventive concept, another data processing method is also provided in the embodiments of the present disclosure, such as the following embodiments. Since the principle of solving the problem in the embodiment of the method is similar to that in the above embodiment of the method, the implementation of the embodiment of the method can refer to the implementation of the above embodiment of the method, and the repeated parts will not be repeated.

[0073] Figure 5 A flow chart of another data processing method in an embodiment of the present disclosure is shown. It should be noted that the data processing method is applied to NWDAF.

[0074] like Figure 5 As shown, the method may include: S510: When receiving a subscription request sent by the perception network element SF, a target intelligent model is trained based on an analysis identifier in the subscription request.

[0075] S520, when the training stop condition is met, sending the training result to the SF, so that the SF determines the training instruction information based on the training result; S530, stop or change the training of the target intelligent model based on the training instruction information.

[0076] The data processing method provided by the embodiments of the present disclosure receives perception service request information sent by a consumer network element, determines whether there is a target intelligent model based on the perception service request information, obtains perception reasoning data when a target intelligent model exists, determines a perception result based on the target intelligent model and the perception reasoning data, and sends the perception result to the consumer network element, thereby improving the level of communication intelligence.

[0077] Figure 6 A flow chart of another data processing method in an embodiment of the present disclosure is shown. It should be noted that the data processing method is applied to NWDAF.

[0078] like Figure 6 As shown, the method may include: S610, when receiving a subscription request sent by the perception network element SF, training a target intelligent model based on an analysis identifier in the subscription request; S620, when the training stop condition is met, sending the training result to the SF, so that the SF determines the training instruction information based on the training result; S630: Stop or change the training of the target intelligent model based on the training instruction information.

[0079] S640, upon receiving a subscription request sent by the SF, determining whether the NWDAF meets the federated learning requirements based on the data privacy requirement data and the computing power requirement data in the subscription request; S650: When the NWDAF does not meet the federated learning requirement, send a subscription request to a second NWDAF.

[0080] The data processing method provided by the embodiments of the present disclosure receives perception service request information sent by a consumer network element, determines whether there is a target intelligent model based on the perception service request information, obtains perception reasoning data when a target intelligent model exists, determines a perception result based on the target intelligent model and the perception reasoning data, and sends the perception result to the consumer network element, thereby improving the level of communication intelligence.

[0081] Figure 7 A signaling diagram of a data processing method in an embodiment of the present disclosure is shown.

[0082] like Figure 7 As shown, the method may include: S701, AF sends a sensing service request to SF.

[0083] S702, SF determines whether there is a trained intelligent model.

[0084] S703, Sever NWDAF determines whether it is necessary to train the intelligent model using federated learning.

[0085] S704, the Sever NWDAF sends a model training subscription request to the Client NWDAF.

[0086] S705, training based on local data.

[0087] S706, Client NWDAF sends the trained intelligent model parameters to Sever NWDAF.

[0088] S707, aggregate the intelligent model parameters to obtain aggregated intelligent model parameters.

[0089] S708: The Sever NWDAF determines with the SF whether continuous training is required.

[0090] S709, the Sever NWDAF sends the aggregated intelligent model parameters to the Client NWDAF.

[0091] S710, Client NWDAF updates the intelligent model based on the aggregated intelligent model parameters.

[0092] S711, Sever NWDAF sends the updated intelligent model to SF.

[0093] S712, SF performs perception reasoning based on the intelligent model to obtain perception reasoning results.

[0094] S713, SF sends the sensing result to AF.

[0095] Figure 8 FIG. 8 is a structural diagram of another data processing device in an embodiment of the present disclosure. It should be noted that the data processing device 800 is applied to SF.

[0096] like Figure 8 As shown, the device may include: The first receiving module 810 is used to receive the perception service request information sent by the consumer network element; A first determination module 820 is used to determine whether a target intelligent model exists based on the sensing service request information; An acquisition module 830, for acquiring perception reasoning data when a target intelligent model exists; A second determination module 840 is used to determine a perception result based on the target intelligent model and the perception reasoning data; The first sending module 850 is used to send the sensing result to the consumer network element.

[0097] The data processing device provided by the embodiments of the present disclosure receives perception service request information sent by a consumer network element, determines whether there is a target intelligent model based on the perception service request information, obtains perception reasoning data when a target intelligent model exists, determines a perception result based on the target intelligent model and the perception reasoning data, and sends the perception result to the consumer network element, thereby improving the level of communication intelligence.

[0098] In one embodiment of the present disclosure, the device further includes: The fourth determination module is used to determine the network data analysis function NWDAF for training the target intelligent model when there is no target intelligent model.

[0099] The data processing device provided by the embodiments of the present disclosure receives perception service request information sent by a consumer network element, determines whether there is a target intelligent model based on the perception service request information, obtains perception reasoning data when a target intelligent model exists, determines a perception result based on the target intelligent model and the perception reasoning data, and sends the perception result to the consumer network element, thereby improving the level of communication intelligence.

[0100] In one embodiment of the present disclosure, the device further includes: The second sending module is used to send a subscription request to NWDAF so that NWDAF can train the target intelligent model.

[0101] The data processing device provided by the embodiments of the present disclosure receives perception service request information sent by a consumer network element, determines whether there is a target intelligent model based on the perception service request information, obtains perception reasoning data when a target intelligent model exists, determines a perception result based on the target intelligent model and the perception reasoning data, and sends the perception result to the consumer network element, thereby improving the level of communication intelligence.

[0102] In one embodiment of the present disclosure, the device further includes: The second receiving module is used to receive the training result sent by NWDAF; A fifth determining module, configured to determine training instruction information based on the training result; The change module is used to send the training instruction information to the NWDAF so that the NWDAF stops or changes the training of the target intelligent model based on the training instruction information.

[0103] The data processing device provided by the embodiments of the present disclosure receives perception service request information sent by a consumer network element, determines whether there is a target intelligent model based on the perception service request information, obtains perception reasoning data when a target intelligent model exists, determines a perception result based on the target intelligent model and the perception reasoning data, and sends the perception result to the consumer network element, thereby improving the level of communication intelligence.

[0104] Fig. 9 FIG. 9 is a block diagram of another data processing device in an embodiment of the present disclosure. It should be noted that the data processing device 900 is applied to SF.

[0105] like Fig. 9 As shown, the device may include: The training module 910 is used to train the target intelligent model based on the analysis identifier in the subscription request when receiving the subscription request sent by the perception network element SF; A third determining module 920 is configured to send the training result to the SF when the training stop condition is met, so that the SF determines the training instruction information based on the training result; The changing module 930 is used to stop or change the training of the target intelligent model based on the training instruction information.

[0106] The data processing device provided by the embodiments of the present disclosure receives perception service request information sent by a consumer network element, determines whether there is a target intelligent model based on the perception service request information, obtains perception reasoning data when a target intelligent model exists, determines a perception result based on the target intelligent model and the perception reasoning data, and sends the perception result to the consumer network element, thereby improving the level of communication intelligence.

[0107] In one embodiment of the present disclosure, the device further includes: A fifth determination module, configured to determine whether NWDAF meets the federated learning requirements based on the data privacy requirement data and the computing power requirement data in the subscription request when receiving the subscription request sent by the SF; The third sending module is used to send the subscription request to the second NWDAF when the NWDAF does not meet the federated learning requirements.

[0108] The data processing device provided by the embodiments of the present disclosure receives perception service request information sent by a consumer network element, determines whether there is a target intelligent model based on the perception service request information, obtains perception reasoning data when a target intelligent model exists, determines a perception result based on the target intelligent model and the perception reasoning data, and sends the perception result to the consumer network element, thereby improving the level of communication intelligence.

[0109] The data processing device provided in the embodiments of the present disclosure can be used to execute the positioning methods provided in the above-mentioned method embodiments. The implementation principles and technical effects thereof are similar and will not be described in detail here for the sake of brevity.

[0110] Those skilled in the art will appreciate that various aspects of the present disclosure may be implemented as systems, methods or program products. Therefore, various aspects of the present disclosure may be specifically implemented in the following forms, namely: complete hardware implementation, complete software implementation (including firmware, microcode, etc.), or a combination of hardware and software, which may be collectively referred to herein as "circuits", "modules" or "systems".

[0111] Refer to the following Fig.10 The electronic device 1000 according to this embodiment of the present disclosure is described. Fig.10 The electronic device 1000 shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0112] like Fig.10 As shown, the electronic device 1000 is in the form of a general computing device. The components of the electronic device 1000 may include but are not limited to: the at least one processing unit 1010, the at least one storage unit 1020, and a bus 1030 connecting different system components (including the storage unit 1020 and the processing unit 1010).

[0113] The storage unit stores program codes, which can be executed by the processing unit 1010, so that the processing unit 1010 performs the steps of various exemplary embodiments of the present disclosure described in the above “Exemplary Method” section of this specification. For example, the processing unit 1010 can perform the following steps of the above method embodiment: Receiving the sensing service request information sent by the consumer network element; Determine whether a target intelligent model exists based on the perceived service request information; In the presence of a target intelligent model, acquiring perception and reasoning data; Determine the perception result based on the target intelligent model and the perception reasoning data; The sensing results are sent to the consumer network element.

[0114] The storage unit 1020 may include a readable medium in the form of a volatile storage unit, such as a random access storage unit (RAM) 10201 and / or a cache storage unit 10202 , and may further include a read-only storage unit (ROM) 10203 .

[0115] The storage unit 1020 may also include a program / utility 10204 having a set (at least one) of program modules 10205, such program modules 10205 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0116] Bus 1030 may represent one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0117] The electronic device 1000 may also communicate with one or more external devices 1040 (e.g., keyboards, pointing devices, Bluetooth devices, etc.), may also communicate with one or more devices that enable a user to interact with the electronic device 1000, and / or may communicate with any device that enables the electronic device 1000 to communicate with one or more other computing devices (e.g., routers, modems, etc.). Such communication may be performed through an input / output (I / O) interface 1050. In addition, the electronic device 1000 may also communicate with one or more networks (e.g., local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) through a network adapter 1060. As shown, the network adapter 1060 communicates with other modules of the electronic device 1000 through a bus 1030. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device 1000, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, etc.

[0118] Through the description of the above implementation, it is easy for those skilled in the art to understand that the example implementation described here can be implemented by software, or by software combined with necessary hardware. Therefore, the technical solution according to the implementation of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which can be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the implementation of the present disclosure.

[0119] In an exemplary embodiment of the present disclosure, a computer-readable storage medium is also provided, which may be a readable signal medium or a readable storage medium. A program product capable of implementing the above method of the present disclosure is stored thereon. In some possible implementations, various aspects of the present disclosure may also be implemented in the form of a program product, which includes a program code, and when the program product is run on a terminal device, the program code is used to enable the terminal device to execute the steps according to various exemplary implementations of the present disclosure described in the above “Exemplary Method” section of this specification.

[0120] More specific examples of computer-readable storage media in the present disclosure may include, but are not limited to, an electrical connection having one or more conductors, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0121] In the present disclosure, a computer readable storage medium may include a data signal propagated in baseband or as part of a carrier wave, wherein a readable program code is carried. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable signal medium may also be any readable medium other than a readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0122] Alternatively, the program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination of the foregoing.

[0123] In specific implementation, the program code for performing the operation of the present disclosure may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as "C" language or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as an independent software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., using an Internet service provider to connect through the Internet).

[0124] It should be noted that, although several modules or units of the device for action execution are mentioned in the above detailed description, this division is not mandatory. In fact, according to the embodiments of the present disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. On the contrary, the features and functions of one module or unit described above can be further divided into multiple modules or units to be embodied.

[0125] In addition, although the steps of the method in the present disclosure are described in a specific order in the drawings, this does not require or imply that the steps must be performed in this specific order, or that all the steps shown must be performed to achieve the desired results. Additionally or alternatively, some steps may be omitted, multiple steps may be combined into one step, and / or one step may be decomposed into multiple steps, etc.

[0126] Through the description of the above implementation modes, it is easy for those skilled in the art to understand that the example implementation modes described here can be implemented by software, or by software combined with necessary hardware. Therefore, the technical solution according to the implementation mode of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which can be a personal computer, a server, a mobile terminal, or a network device, etc.) to execute the method according to the implementation mode of the present disclosure.

[0127] Those skilled in the art will readily appreciate other embodiments of the present disclosure after considering the specification and practicing the invention disclosed herein. The present disclosure is intended to cover any variations, uses or adaptations of the present disclosure, which follow the general principles of the present disclosure and include common knowledge or customary techniques in the art that are not disclosed in the present disclosure. The description and examples are intended to be exemplary only, and the true scope and spirit of the present disclosure are indicated by the appended claims.

Claims

1. A data processing method, characterized in that: The SF used in the sensing network element includes: Receiving the sensing service request information sent by the consumer network element; Determining whether a target intelligent model exists based on the perception service request information; In the presence of a target intelligent model, acquiring perception and reasoning data; Determine a perception result based on the target intelligent model and the perception reasoning data; The sensing result is sent to the consumer network element.

2. The method according to claim 1, characterized in that The method further comprises: In the absence of the target intelligent model, a network data analysis function NWDAF for training the target intelligent model is determined.

3. The method according to claim 2, characterized in that The method further comprises: A subscription request is sent to the NWDAF so that the NWDAF trains the target intelligent model.

4. The method according to claim 3, characterized in that The method further comprises: Receiving the training result sent by the NWDAF; Determining training instruction information based on the training result; The training instruction information is sent to the NWDAF so that the NWDAF stops or changes the training of the target intelligent model based on the training instruction information.

5. A data processing method, characterized in that: Applied to the network data analysis function NWDAF, including: In case of receiving a subscription request sent by the perception network element SF, training a target intelligent model based on the analysis identifier in the subscription request; When a training stop condition is met, sending a training result to the SF, so that the SF determines training instruction information based on the training result; Stop or change the training of the target intelligent model based on the training instruction information.

6. The method according to claim 5, characterized in that The method further comprises: Upon receiving the subscription request sent by the SF, determining whether the NWDAF meets the federated learning requirements based on the data privacy requirement data and the computing power requirement data in the subscription request; In case the NWDAF does not meet the federated learning requirement, the subscription request is sent to a second NWDAF.

7. A data processing device, characterized in that: The SF used in the sensing network element includes: A first receiving module, configured to receive the perception service request information sent by the consumer network element; A first determination module, used to determine whether a target intelligent model exists based on the perception service request information; An acquisition module, used to acquire perception and reasoning data in the presence of a target intelligent model; A second determination module, used to determine a perception result based on the target intelligent model and the perception reasoning data; The first sending module is used to send the perception result to the consumer network element.

8. A data processing device, characterized in that: Applied to the network data analysis function NWDAF, including: A training module, configured to, upon receiving a subscription request sent by the perception network element SF, train a target intelligent model based on an analysis identifier in the subscription request; A third determining module is used to send the training result to the SF when the training stop condition is met, so that the SF determines the training instruction information based on the training result; A change module is used to stop or change the training of the target intelligent model based on the training instruction information.

9. An electronic device, characterized in that: include: processor; as well as A memory, configured to store executable instructions of the processor; The processor is configured to execute the data processing method according to any one of claims 1 to 6 by executing the executable instructions.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the data processing method according to any one of claims 1 to 6 is implemented.

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