Model augmentation training method and apparatus, electronic device, and storage medium

Abstract

The present application discloses a model augmentation training method and apparatus, an electronic device, and a storage medium. The method comprises: acquiring an analysis information request of a service consumer, and transmitting the analysis information request to a multi-node cluster; acquiring a plurality of network data analysis function groups on the basis of the analysis information request, and acquiring corresponding machine learning models; when the machine learning models are trained for non-common members, generating a key on the basis of the acquired network data analysis function groups and corresponding machine learning models, and obtaining an encrypted sample on the basis of the key; obtaining a fusion machine learning model on the basis of the machine learning models in combination with the encrypted sample; performing model training on the fusion machine learning model by means of all local samples, and transmitting the trained fusion machine learning model to the network data analysis function groups; and updating and iterating the machine learning models until a preset condition is met. The present application can efficiently perform model augmentation training, and can be widely applied to the technical field of data processing.

Description

Model enhancement training method, device, electronic device and storage medium Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a model enhancement training method, device, electronic device and storage medium. Background Art

[0002] In the 5G network architecture, the NWDAF network element (NWDAF) is introduced in the core network as a carrier entity for customized data collection and intelligent analysis. It not only collects data from the NF, AF, and OAM components of the 5G core network (5GC), but also provides intelligent analysis capabilities (such as computation, training, inference, and prediction) and outputs analysis results to the NF, AF, or OAM for decision-making. As standards continue to evolve, enhancements have been made to the NWDAF element itself, the network analysis process, and the data collection process. Release 16 and Release 17 define 14 and 7 UCs, respectively, primarily focused on network self-optimization. These include assisting with UPF selection, QoS parameter adjustment, AMF mobility management, network element / slice load balancing, UE-related data collection and analysis, and anomaly detection and location, such as congestion feedback. Currently, NWDAF deployment and implementation are primarily focused on each UC, with model training focused on each UC, incorporating user profiles, terminal behavior, network experience profiles, and connection profiles.

[0003] Summary of the Invention

[0004] The present application aims to solve at least one of the technical problems in the related art to a certain extent. To this end, the present application proposes a model enhancement training method, device, electronic device and storage medium that can efficiently perform model enhancement training.

[0005] On the one hand, an embodiment of the present application provides a model enhancement training method, comprising:

[0006] Obtaining analysis information requests from service consumers and transmitting the analysis information requests to a multi-node cluster; the multi-node cluster includes several network data analysis function groups;

[0007] Obtain multiple network data analysis function groups from the multi-node cluster based on the analysis information request; and obtain the machine learning model in each network data analysis function group;

[0008] When the machine learning models trained in the obtained multiple network data analysis function groups are model training for non-common members, a key is generated based on the obtained network data analysis function groups and the corresponding machine learning models; and an encrypted sample is obtained based on the key;

[0009] Based on the obtained machine learning model corresponding to the network data analysis function group, the encrypted samples are combined to obtain a fusion machine learning model; the fusion machine learning model is trained using all local samples, and the trained fusion machine learning model is transmitted to each network data analysis function group in the multi-node cluster;

[0010] Based on the trained fusion machine learning model, the machine learning model corresponding to each network data analysis function group of the multi-node cluster is updated and iterated until the preset conditions are met.

[0011] Optionally, the request for obtaining analysis information of the service consumer includes at least one of the following:

[0012] Get subscription messages for machine learning models sent by service consumers;

[0013] Gets the request message for the machine learning model sent by the service consumer.

[0014] Optionally, the analysis information request includes an analysis ID; and according to the analysis information request, multiple network data analysis function groups are obtained from the multi-node cluster, including:

[0015] The pre-selected model type is determined based on the analysis ID, and then cluster members are dynamically selected through a multi-node cluster to obtain multiple network data analysis function groups.

[0016] Optionally, before the step of obtaining multiple network data analysis function groups from the multi-node cluster, the method further includes:

[0017] Each network data analysis function group uses local data to train the machine learning model of the network data analysis function group.

[0018] Optionally, obtaining an encrypted sample based on a key includes:

[0019] The sample data of the obtained multiple network data analysis function groups are encrypted using a key to obtain encrypted samples.

[0020] Optionally, based on the trained fusion machine learning, the machine learning model corresponding to each network data analysis function group of the multi-node cluster is updated and iterated until a preset condition is met, including:

[0021] Based on the trained fusion machine learning model, new sample training is performed on each network data analysis function group of the multi-node cluster, and then the machine learning model of each network data analysis function group is updated accordingly until the model training reaches the preset accuracy.

[0022] Optionally, the method further comprises:

[0023] Return the training results of the machine learning model corresponding to each network data analysis function group to the multi-node cluster;

[0024] The training results are pushed to the service consumers through a multi-node cluster.

[0025] On the other hand, an embodiment of the present application provides a model enhancement training device, comprising:

[0026] The first module is used to obtain analysis information requests from service consumers and transmit the analysis information requests to a multi-node cluster; the multi-node cluster includes several network data analysis function groups;

[0027] The second module is used to obtain multiple network data analysis function groups from the multi-node cluster according to the analysis information request; and obtain the machine learning model in each network data analysis function group;

[0028] The third module is configured to generate a key based on the obtained network data analysis function groups and the corresponding machine learning models when the machine learning models trained in the obtained multiple network data analysis function groups are model training for non-common members; and then obtain an encrypted sample based on the key;

[0029] The fourth module is used to obtain a fusion machine learning model based on the obtained machine learning model corresponding to the network data analysis function group and the encrypted sample; the fusion machine learning model is trained using all local samples, and the trained fusion machine learning model is transmitted to each network data analysis function group in the multi-node cluster;

[0030] The fifth module is used to update and iterate the machine learning model corresponding to each network data analysis function group of the multi-node cluster based on the trained fusion machine learning model until the preset conditions are met.

[0031] Optionally, the device further comprises:

[0032] The sixth module is used to return the training results of the machine learning model corresponding to each network data analysis function group to the multi-node cluster;

[0033] The seventh module is used to push the training results to service consumers through a multi-node cluster.

[0034] On the other hand, an embodiment of the present application provides an electronic device, including: a processor and a memory; the memory is used to store programs; the processor executes the program to implement the above-mentioned model enhancement training method.

[0035] On the other hand, an embodiment of the present application provides a computer storage medium, which stores a program executable by a processor, and the program executable by the processor is used to implement the above-mentioned model enhancement training method when executed by the processor.

[0036] The embodiment of the present application obtains the analysis information request of the service consumer and transmits the analysis information request to the multi-node cluster; the multi-node cluster includes several network data analysis function groups; according to the analysis information request, multiple network data analysis function groups are obtained from the multi-node cluster; and the machine learning models in each network data analysis function group are obtained; when the machine learning models trained in the obtained multiple network data analysis function groups are model training for non-common members, a key is generated based on the obtained network data analysis function group and the corresponding machine learning model; and then an encrypted sample is obtained based on the key; based on the machine learning model corresponding to the obtained network data analysis function group, a fusion machine learning model is obtained in combination with the encrypted sample; the fusion machine learning model is trained through all local samples, and the trained fusion machine learning model is transmitted to each network data analysis function group of the multi-node cluster; based on the trained fusion machine learning model, the machine learning model corresponding to each network data analysis function group of the multi-node cluster is updated and iterated until the preset conditions are met. The embodiment of the present application can efficiently perform model enhancement training through the collaborative training of different network data analysis function groups and the model training for non-common members. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] The accompanying drawings are used to provide a further understanding of the technical solution of the present application and constitute a part of the specification. Together with the embodiments of the present application, they are used to explain the technical solution of the present application and do not constitute a limitation on the technical solution of the present application.

[0038] FIG1 is a schematic diagram of an implementation environment for model enhancement training provided in an embodiment of the present application;

[0039] FIG2 is a flow chart of a model enhancement training method provided in an embodiment of the present application;

[0040] FIG3 is a schematic diagram of the expansion process of obtaining an analysis information request according to an embodiment of the present application;

[0041] FIG4 is a schematic diagram of the deployment process of the model enhancement training method based on local data training provided in an embodiment of the present application;

[0042] FIG5 is a schematic diagram of the expanded flow of training result push provided in an embodiment of the present application;

[0043] FIG6 is a schematic diagram of the overall business process of the model enhancement training method provided in an embodiment of the present application;

[0044] FIG7 is a schematic diagram of a business process for subscription to an NWDAF service provided in an embodiment of the present application;

[0045] FIG8 is a schematic structural diagram of a model enhancement training device provided in an embodiment of the present application;

[0046] FIG9 is a schematic structural diagram of an electronic device provided in an embodiment of the present application;

[0047] FIG10 is a block diagram of a computer system structure of an electronic device provided in an embodiment of the present application and suitable for implementing the embodiment of the present application. DETAILED DESCRIPTION

[0048] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0049] It should be noted that although the system diagrams illustrate functional module divisions and the flowcharts illustrate a logical sequence, in certain circumstances, the steps shown or described may be performed in a sequence that differs from the module divisions in the system or the sequence in the flowcharts. The terms "first / S100," "second / S200," and the like in the specification, claims, and drawings are used to distinguish similar objects and are not necessarily intended to describe a specific sequence or precedence.

[0050] References herein to "embodiments" mean that a particular feature, structure, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute an independent or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0051] It should be noted that, in order to facilitate the understanding of the technical solution of this application, the professional and technical terms that may appear in the embodiments of this application are first explained:

[0052] NWDAF (Network Data Analytics Function);

[0053] OAM (Operation Administration and Maintenance);

[0054] In the 5G core network (5GC), each node is called a network function (NF), among which the node of the external application server is called AF (application function);

[0055] QoS (Quality of Service) refers to a network's ability to use various basic technologies to provide better service for specified network communications. It is a network security mechanism and a technology used to solve problems such as network delays and congestion.

[0056] UC (Use Case).

[0057] It is understandable that the model enhancement training method provided in the embodiment of the present application can be applied to any computer device with data processing computing capabilities, and this computer device can be various terminals or servers. When the computer device in the embodiment is a server, the server is an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud data blocks, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN (Content Delivery Network, content distribution network), and big data and artificial intelligence platforms. Optionally, the terminal is a smart phone, tablet computer, laptop computer, desktop computer, etc., but is not limited to this.

[0058] As shown in Figure 1, a schematic diagram of an implementation environment provided by an embodiment of the present application is shown. Referring to Figure 1, the implementation environment includes at least one terminal 102 and a server 101. The terminal 102 and the server 101 can be connected to a network via a wireless or wired manner to complete data transmission and exchange.

[0059] Server 101 can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers. It can also be a cloud server that provides basic cloud computing services such as cloud services, cloud data blocks, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN (Content Delivery Network), as well as big data and artificial intelligence platforms.

[0060] In addition, server 101 can also be a node server in a blockchain network. Blockchain is a new application model of computer technologies such as distributed data storage, peer-to-peer transmission, consensus mechanism, and encryption algorithm.

[0061] The terminal 102 may be a smartphone, tablet computer, laptop computer, desktop computer, smart speaker, smart watch, etc., but is not limited thereto. The terminal 102 and the server 101 may be connected directly or indirectly via wired or wireless communication, which is not limited in this embodiment of the present application.

[0062] Exemplarily based on the implementation environment shown in Figure 1, an embodiment of the present application provides a model enhancement training method. The following is an example of the model enhancement training method being applied in the server 101. It can be understood that the model enhancement training method can also be applied in the terminal 102.

[0063] Referring to Figure 2, Figure 2 is a flow chart of a model enhancement training method applied to a server provided in an embodiment of the present application. The execution subject of the model enhancement training method can be any of the aforementioned computer devices (including a server or a terminal). Referring to Figure 2, the method includes the following steps:

[0064] S100, obtaining an analysis information request from a service consumer, and transmitting the analysis information request to a multi-node cluster;

[0065] It should be noted that the multi-node cluster includes several network data analysis function groups; as shown in Figure 3, in some embodiments, obtaining the analysis information request of the service consumer may include at least one of the following: S101, obtaining the subscription message for the machine learning model sent by the service consumer; S102, obtaining the request message for the machine learning model sent by the service consumer.

[0066] For example, in some specific embodiments, the subscription of the NWDAF service:

[0067] 1. NWDAF service consumers subscribe to ML (machine learning) models from NWDAF (including MTLF): ① Parameters that NWDAF service consumers can provide include a list of analysis IDs and the ability to select ML models, such as selecting ML models based on their applicable S-NSSAI and region; ② ML model address, ML model applicable time, and applicable region.

[0068] 2. The NWDAF service consumer requests the ML model from the NWDAF (including the MTLF): ① Request the ML model associated with the analysis ID for analysis and derivation; ② When the NWDAF (including the MTLF) receives the request for ML model information, it determines whether the existing ML model needs further training. If so, the NWDAF needs to start data collection operations and use it for ML model training. After training is completed, it provides the ML model address, etc.

[0069] S200: Obtain multiple network data analysis function groups from a multi-node cluster according to the analysis information request; and obtain a machine learning model in each network data analysis function group;

[0070] It should be noted that the analysis information request includes an analysis ID; in some embodiments, obtaining multiple network data analysis function groups from a multi-node cluster based on the analysis information request may include: determining a pre-selected model type based on the analysis ID, and then dynamically selecting cluster members through the multi-node cluster to obtain multiple network data analysis function groups.

[0071] For example, in some specific embodiments, non-common members need to dynamically select NWDAF based on parameters such as user type, request analysis ID, inference ML model, and model training time.

[0072] It should also be noted that, as shown in Figure 4, in some embodiments, before the step of obtaining multiple network data analysis function groups from a multi-node cluster, the method may also include: each network data analysis function group uses local data to train the machine learning model of the network data analysis function group.

[0073] For example, in some specific embodiments, the multi-node cluster NWDAF selects the corresponding NWDAF group based on the requested analysis ID and the allowed selection of the ML model, and uses local data to train the model. The local data includes terminal information, network information, user information, and application information, etc. The multi-node cluster will dynamically select cluster members.

[0074] S300: When the machine learning models trained in the obtained plurality of network data analysis function groups are model training for non-common members, a key is generated based on the obtained network data analysis function groups and the corresponding machine learning models; and an encrypted sample is obtained based on the key.

[0075] It should be noted that, in some embodiments, obtaining an encrypted sample based on a key may include: encrypting sample data of multiple network data analysis function groups obtained using a key to obtain an encrypted sample.

[0076] For example, in some specific embodiments, non-common members need to dynamically select NWDAF based on parameters such as user type, request analysis ID, inference ML model, model training time, etc., and dynamically generate a one-time key based on the data thereon.

[0077] In some embodiments, common data (associated with common members) will be trained using public keys. Specifically, by distributing public keys to multiple NWDAF groups, multiple NWDAF groups align encrypted samples of common users and jointly perform encryption training with the central NWDAF.

[0078] It should also be noted that, based on the service consumer's request or subscription information, the multi-node cluster NWDAF selects the relevant NWDAF according to the user and subscription type. NWDAF1,…,NWDAFN are responsible for different training models ML1,…,MLQ according to different UCs and have different samples (such as: UE ID, slice ID, group ID, DNN, application ID, priority, expected UE behavior parameters, UE communication behavior [rate, start time], UE location, timestamp, TAC, base station hardware and software resource group, user ID, network element ID used, network element load, etc.). Since the participants have different data, the model trained by the terminal finally has different model parameters, and the cost factor of the algorithm will also be considered.

[0079] S400, based on the obtained machine learning model corresponding to the network data analysis function group, combined with the encrypted sample to obtain a fusion machine learning model; the fusion machine learning model is trained using all local samples, and the trained fusion machine learning model is transmitted to each network data analysis function group in the multi-node cluster;

[0080] For example, in some specific embodiments, after the training of each NWDAF1, ..., NWDAFN is completed, the multi-node cluster NWDAF forms a fusion ML model based on the encrypted samples and models ML1, ..., MLQ, and performs fusion ML model training based on all existing samples.

[0081] S500. Based on the trained fusion machine learning model, the machine learning model corresponding to each network data analysis function group of the multi-node cluster is updated and iterated until the preset conditions are met.

[0082] It should be noted that, in some embodiments, step S500 may include: based on the trained fusion machine learning model, performing new sample training on each network data analysis function group of the multi-node cluster, and then updating and iterating the machine learning model corresponding to each network data analysis function group until the model training reaches the preset accuracy.

[0083] For example, in some specific embodiments, the fused ML model is sent to NWDAF1, ..., NWDAFN for respective new sample training, and the original ML model 1*, ..., ML model N* is updated, and the update iteration is completed until the accuracy meets the result requirements and the result is returned to the multi-node cluster NWDAF for model preservation.

[0084] Among them, in some embodiments, as shown in Figure 5, the method may also include: T100, returning the results of the completed machine learning model training corresponding to each network data analysis function group to the multi-node cluster; T200, pushing the completed training results to the service consumer through the multi-node cluster.

[0085] For example, in some specific embodiments, the multi-node cluster NWDAF model saves the inference output and pushes it to the service consumer through a response or notification.

[0086] In order to explain the principles of the technical solution of this application in detail, the overall process of this application is described below in combination with some specific embodiments. It is easy to understand that the following is an explanation of the technical principles of this application and cannot be regarded as a limitation of this application.

[0087] The embodiments of the present application implement enhanced model training based on NWDAF, including: based on the subscription / request of ML models, the multi-node cluster NWDAF selects the corresponding NWDAF group based on the requested analysis ID and the allowed ML model selection; the multi-node cluster NWDAF members dynamically select their members based on relevant algorithms, and dynamic key generation; if the existing ML model needs further training, relevant data is collected from the NF / OAM / AF for ML model training; after the training of each NWDAF is completed, a fused ML model is formed based on the encrypted full set of samples, UC, and different ML models; the fused ML model is trained based on all existing samples, and the fused ML model is sent to each NWDAF for training on their own new samples, updating the original ML model 1*, ..., ML model Q*, completing the update iteration, and returning the results to the multi-node cluster NWDAF for saving model 1*, ..., model Q* and the fused model, completing the aggregation and update of the trained model; the multi-node cluster NWDAF is more applicable to different users, different UCs, and different trained ML models, and the data of the multi-node cluster NWDAF is all encrypted data, which is more applicable to NF service consumers and has a certain degree of practicality.

[0088] This application is mainly used to improve NWDAF enhanced model training, mainly for NWDAF service consumers to send subscription / request ML models, multi-node cluster NWDAF selects the corresponding NWDAF group based on the requested analysis ID and the allowed selection of ML models, and uses local data to train the model. Local data includes terminal information, network information, user information and application information, etc. The multi-node cluster will dynamically select cluster members; if the existing ML model needs further training, relevant data will be collected from NF / OAM / AF to train the ML model. Common data will be trained through the public key, and non-common data will be based on the selected data type, training difficulty, etc. Based on the dynamic selection of UC and different ML models to form a fusion ML model, and Based on the dynamically generated key pair, the fused ML model is sent to each NWDAF for training on new samples, updating the original ML model 1*,…, ML model N*, completing the update iteration, and returning the result to the multi-node cluster NWDAF for saving model 1*,…, model N* and the fused model. The aggregation and update of the trained model are completed, and the next iteration is started as required. The timestamp is also saved. The multi-node cluster NWDAF can be called by NF service consumers or actively pushed after subscription. It is more applicable to different users, different UCs, and different trained ML models, and can be used in scenarios where different UCs are integrated. Multi-node cluster data is encrypted data, which is more applicable to NF service consumers and has a certain degree of practicality.

[0089] The technical principles of the embodiments of the present application are described below with reference to the accompanying drawings. As shown in FIG6 , the process is as follows:

[0090] 1-2 are existing processes: Service consumers can send analysis information requests to NRF or multi-node cluster NWDAF;

[0091] Specifically, as shown in Figure 7, the subscription of the NWDAF service:

[0092] 1. NWDAF service consumers subscribe to ML models from NWDAF (including MTLF): ① Parameters that NWDAF service consumers can provide include a list of analysis IDs and the ability to select ML models, such as selecting ML models based on the S-NSSAI and region to which the ML model applies; ② ML model address, ML model applicable time, and applicable region, etc.

[0093] 2. The NWDAF service consumer requests the ML model from the NWDAF (including the MTLF): ① Request the ML model associated with the analysis ID for analysis and derivation; ② When the NWDAF (including the MTLF) receives the request for ML model information, it determines whether the existing ML model needs further training. If so, the NWDAF needs to start data collection operations and use it for ML model training. After training is completed, it provides the ML model address, etc.

[0094] Process 3 to 5: In the analysis information request, the NF service consumer can provide one or more requested analysis IDs for inferring the ML model, such as UE mobility analysis of analysis ID4, QoS parameter adjustment and one or more requested areas of interest. After the multi-node cluster NWDAF is discovered, the cluster members are dynamically formed. The common member method specifically distributes the public key to multiple NWDAF groups. Multiple NWDAF groups align the encrypted samples of common users and jointly perform encryption training with the central NWDAF; non-common members need to dynamically select NWDAF based on parameters such as user type, requested analysis ID, inference ML model, model training time, etc., and dynamically generate a one-time key based on the data on it.

[0095] 6. Based on the service consumer's request or subscription information, the multi-node cluster NWDAF selects the relevant NWDAF according to the user and subscription type. NWDAF1,...,NWDAFN are responsible for different training models ML1,...,MLQ according to different UCs and have different samples (such as: UE ID, slice ID, group ID, DNN, application ID, priority, expected UE behavior parameters, UE communication behavior [rate, start time], UE location, timestamp, TAC, base station hardware and software resource group, user ID, used network element ID, network element load, etc.). Since the participants have different data, the final terminal training model has different model parameters, and the cost factor of the algorithm will also be considered.

[0096] 7, 8. After the training of each NWDAF1, ..., NWDAFN is completed, the multi-node cluster NWDAF forms a fused ML model based on the encrypted samples and models ML1, ..., MLQ, trains the fused ML model based on all existing samples, sends the fused ML model to NWDAF1, ..., NWDAFN for training on their respective new samples, updates the original ML model 1*, ..., ML model N*, completes the update iteration, and returns the result to the multi-node cluster NWDAF for model storage until the accuracy meets the result requirements.

[0097] In summary, the embodiment of the present application is based on the NWDAF service consumer sending a subscription / request ML model. The multi-node cluster NWDAF selects the corresponding NWDAF group based on the requested analysis ID and the allowed selection of the ML model, and uses local data to train the model. The local data includes terminal information, network information, user information, and application information, etc. The multi-node cluster will dynamically select cluster members; if the existing ML model needs further training, relevant data will be collected from NF / OAM / AF to train the ML model. Common data will be trained using the public key, and non-common data will be based on the selected data type, training difficulty, etc. Based on the dynamic selection of UC and different ML models to form a fused ML model, and based on the dynamically generated key pair, the fused ML model will be sent to each NWDAF for their own new sample training, updating the original ML model 1*,…,ML Model N*, completes the update iteration, and returns the result to the multi-node cluster NWDAF for model 1*,…, model N* and fusion model preservation, completes the aggregation and update of the training model, starts the next iteration as needed, and saves the timestamp. The multi-node cluster NWDAF is available for NF service consumers to call or actively push after subscription; it is more applicable to different users, different UCs and different training ML models, and can be used in scenarios that integrate different UCs. Multi-node cluster data is encrypted data, which is more applicable to NF service consumers. A fusion ML model is formed based on the encrypted samples and the original training ML model. The fusion ML model is trained based on all existing samples, and the fusion ML model is sent to the original NWDAF for training with new samples to form ML model 1*,…, ML model N*, completing the update iteration. Compared with the existing technology, this application is more suitable for fusion scenario training under different UCs and has stronger adaptability.

[0098] On the other hand, as shown in Figure 8, an embodiment of the present application provides a model enhancement training device 800, including: a first module 810, used to obtain analysis information requests from service consumers and transmit the analysis information requests to a multi-node cluster; the multi-node cluster includes several network data analysis function groups; a second module 820, used to obtain multiple network data analysis function groups from the multi-node cluster according to the analysis information request; and obtain the machine learning models in each network data analysis function group; a third module 830, used to generate a key based on the obtained network data analysis function group and the corresponding machine learning model when the machine learning models trained in the obtained multiple network data analysis function groups are model training for non-common members; and then obtain an encrypted sample based on the key; a fourth module 840, used to obtain a fused machine learning model based on the machine learning model corresponding to the obtained network data analysis function group and the encrypted sample; the fused machine learning model is trained through all local samples, and the trained fused machine learning model is transmitted to each network data analysis function group in the multi-node cluster; a fifth module 850, used to update and iterate the machine learning model corresponding to each network data analysis function group in the multi-node cluster based on the trained fused machine learning model until the preset conditions are met.

[0099] In some embodiments, the device may also include: a sixth module for returning the completed training results of the machine learning model corresponding to each network data analysis function group to the multi-node cluster; and a seventh module for pushing the completed training results to the service consumer through the multi-node cluster.

[0100] The contents of the method embodiments of this application are all applicable to the device embodiments. The functions specifically implemented by the device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method.

[0101] On the other hand, as shown in Figure 9, an embodiment of the present application also provides an electronic device 900, which includes at least one processor 910 and at least one memory 920 for storing at least one program; taking a processor 910 and a memory 920 as an example.

[0102] The processor 910 and the memory 920 may be connected via a bus or other means.

[0103] The memory 920 is a non-transient computer-readable storage medium that can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory 920 may include a high-speed random access memory and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory 920 may optionally include a memory remotely located relative to the processor, and these remote memories may be connected to the device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0104] The electronic device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, i.e., they may be located in one location or distributed across multiple network units. Some or all of these modules may be selected based on actual needs to achieve the objectives of this embodiment.

[0105] Specifically, FIG10 schematically shows a block diagram of a computer system structure of an electronic device for implementing an embodiment of the present application.

[0106] It should be noted that the computer system 1000 of the electronic device shown in FIG10 is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0107] As shown in Figure 10, the computer system 1000 includes a central processing unit (CPU) 1001, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 1002 or the program loaded from the storage part 1008 into the random access memory (RAM) 1003. Various programs and data required for system operation are also stored in the random access memory 1003. The CPU 1001, the read-only memory 1002, and the random access memory 1003 are connected to each other via a bus 1004. An input / output interface 1005 (i.e., an I / O interface) is also connected to the bus 1004.

[0108] The following components are connected to the input / output interface 1005: an input section 1006 including a keyboard, a mouse, and the like; an output section 1007 including devices such as a cathode ray tube (CRT), a liquid crystal display (LCD), and a speaker; a storage section 1008 including a hard disk; and a communication section 1009 including a network interface card such as a local area network card or a modem. The communication section 1009 performs communication processing via a network such as the Internet. A drive 1010 is also connected to the input / output interface 1005 as needed. Removable media 1011, such as a magnetic disk, an optical disk, a magneto-optical disk, or a semiconductor memory, is installed in the drive 1010 as needed, so that computer programs read therefrom can be installed into the storage section 1008 as needed.

[0109] In particular, according to an embodiment of the present application, the processes described in the various method flow charts can be implemented as computer software programs. For example, an embodiment of the present application includes a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for executing the methods shown in the flow charts. In such an embodiment, the computer program can be downloaded and installed from a network via the communication portion 1009 and / or installed from a removable medium 1011. When the computer program is executed by the central processing unit 1001, the various functions defined in the system of the present application are performed.

[0110] It should be noted that the computer-readable medium shown in the embodiments of the present application may be a computer-readable signal medium or a computer-readable storage medium or any combination of the two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or component, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an 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 above. In the present application, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or device. In the present application, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, which carries a computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer-readable medium may be transmitted using any suitable medium, including but not limited to wireless, wired, or any suitable combination thereof.

[0111] Another aspect of the embodiments of the present application further provides a computer-readable storage medium, which stores a program, and the program is executed by a processor to implement the above method.

[0112] The contents of the method embodiments of the present application are all applicable to the computer-readable storage medium embodiments. The functions specifically implemented by the computer-readable storage medium embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method.

[0113] The present application also discloses a computer program product or computer program, which includes computer instructions stored in a computer-readable storage medium. A processor of a computer device can read the computer instructions from the computer-readable storage medium and execute the computer instructions, causing the computer device to perform the above method.

[0114] The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present application. In this regard, each box in the flowchart or block diagram can represent a module, program segment, or a part of code, and the above-mentioned module, program segment, or a part of code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of the boxes in the block diagram or flowchart, can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0115] It should be noted that, although several modules of the device for action execution are mentioned in the above detailed description, this division is not mandatory. In fact, according to the embodiment of the application, the features and functions of two or more modules or units described above can be concretized in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided into multiple modules or units to be concretized.

[0116] Through the description of the above embodiments, it is easy for those skilled in the art to understand that the example embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solution according to the embodiments of the present application 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 touch terminal, or a network device, etc.) to execute the method according to the embodiments of the present application.

[0117] In some optional embodiments, the functions / operations mentioned in the block diagram may not occur in the order mentioned in the operation diagram. For example, depending on the functions / operations involved, the two boxes shown in succession may actually be executed substantially simultaneously or the boxes can sometimes be executed in reverse order. In addition, the embodiments presented and described in the flow chart of the present application are provided in an exemplary manner for the purpose of providing a more comprehensive understanding of the technology. The disclosed method is not limited to the operations and logic flows presented herein. Optional embodiments are contemplated in which the order of the various operations is changed and the sub-operations described as a part of a larger operation are performed independently.

[0118] In addition, although the present application is described in the context of functional modules, it should be understood that, unless otherwise stated, one or more of the functions and / or features may be integrated into a single physical device and / or software module, or one or more functions and / or features may be implemented in separate physical devices or software modules. It is also understood that a detailed discussion of the actual implementation of each module is not necessary for understanding the present application. More specifically, given the properties, functions, and internal relationships of the various functional modules in the devices disclosed herein, the actual implementation of the module will be understood within the routine skills of an engineer. Therefore, a person skilled in the art can implement the present application as set forth in the claims using ordinary techniques without undue experimentation. It is also understood that the specific concepts disclosed are merely illustrative and are not intended to limit the scope of the present application, which is determined by the full scope of the appended claims and their equivalents.

[0119] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the various embodiments of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0120] The logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution apparatus, device, or apparatus (e.g., a computer-based apparatus, a device including a processor, or other apparatus that can fetch instructions from and execute instructions on an instruction execution apparatus, device, or apparatus). For purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by, or in conjunction with, an instruction execution apparatus, device, or apparatus.

[0121] More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk cartridge (magnetic devices), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disc read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering, or processing in another suitable manner as necessary, and then stored in a computer memory.

[0122] It should be understood that various parts of the present application can be implemented using hardware, software, firmware, or a combination thereof. In the above-described embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution device. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used to implement the hardware: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application-specific integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0123] Throughout this specification, reference to terms such as "one embodiment," "some embodiments," "examples," "specific examples," or "some examples" means that a specific feature, structure, material, or characteristic described in conjunction with that embodiment or example is included in at least one embodiment or example of the present application. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0124] Although the embodiments of the present application have been shown and described, those skilled in the art will appreciate that various changes, modifications, substitutions, and variations may be made to the embodiments without departing from the principles and intent of the present application, and that the scope of the present application is defined by the claims and their equivalents.

[0125] The above is a specific description of the preferred implementation of the present application, but the present application is not limited to the embodiments. Technical personnel familiar with the art can also make various equivalent modifications or substitutions without violating the spirit of the present application. These equivalent modifications or substitutions are all included in the scope defined by the claims of the present application.

Claims

1. A model enhancement training method, characterized in that, Including: Obtain an analysis information request of a service consumer, and transmit the analysis information request to a multi-node cluster; The multi-node cluster includes several network data analysis function groups; According to the analysis information request, obtain multiple network data analysis function groups from the multi-node cluster; and obtain machine learning models in each of the network data analysis function groups; When the machine learning models trained in the multiple obtained network data analysis function groups are model-trained for non-common members, generate a key based on the obtained network data analysis function groups and the corresponding machine learning models; and then obtain an encrypted sample based on the key; Based on the machine learning models corresponding to the obtained network data analysis function groups, obtain a fused machine learning model in combination with the encrypted sample; Perform model training on the fused machine learning model with all local samples, and transmit the trained fused machine learning model to each network data analysis function group of the multi-node cluster; Update and iterate the machine learning models corresponding to each network data analysis function group of the multi-node cluster based on the trained fused machine learning model until a preset condition is reached.

2. The model enhancement training method according to claim 1, characterized in that The obtaining an analysis information request of a service consumer includes at least one of the following: Obtain a subscription message for the machine learning model sent by the service consumer; Obtain a request message for the machine learning model sent by the service consumer.

3. The model enhancement training method according to claim 1, wherein The analysis information request includes an analysis ID; the obtaining multiple network data analysis function groups from the multi-node cluster according to the analysis information request includes: Determine a preselected model type based on the analysis ID, and then perform dynamic selection of cluster members through the multi-node cluster to obtain multiple network data analysis function groups.

4. The model enhancement training method according to claim 1, wherein Before the step of obtaining multiple network data analysis function groups from the multi-node cluster; the method further includes: Each network data analysis function group trains the machine learning model of this network data analysis function group with local data.

5. The model enhancement training method according to claim 1, wherein The obtaining an encrypted sample based on the key includes: Encrypt the sample data of the multiple obtained network data analysis function groups with the key to obtain an encrypted sample.

6. The model enhancement training method according to claim 1, wherein The updating and iterating the machine learning models corresponding to each network data analysis function group of the multi-node cluster based on the trained fused machine learning model until a preset condition is reached includes: Based on the trained fused machine learning model, perform new sample training on each network data analysis function group of the multi-node cluster, and then correspondingly update and iterate the machine learning models of each network data analysis function group until the model training reaches a preset accuracy.

7. The model enhancement training method according to claim 1, wherein The method further includes: Return the result of the completion of the training of the machine learning model corresponding to each network data analysis function group to the multi-node cluster; Push the result of the completion of the training to the service consumer through the multi-node cluster.

8. A model enhancement training device, characterized in that, Including: The first module is used to obtain the analysis information request of the service consumer and transmit the analysis information request to the multi-node cluster; The multi-node cluster includes several network data analysis function groups; The second module is used to obtain a plurality of the network data analysis function groups from the multi-node cluster according to the analysis information request; and obtain the machine learning models in each of the network data analysis function groups; The third module is used to, when the machine learning models trained in the obtained multiple network data analysis function groups are models trained for non-common members, generate a key based on the obtained network data analysis function groups and the corresponding machine learning models; and further obtain an encrypted sample based on the key; The fourth module is used to obtain a fused machine learning model by combining the encrypted sample based on the machine learning model corresponding to the obtained network data analysis function group; Perform model training on the fused machine learning model with all local samples and transmit the trained fused machine learning model to each network data analysis function group of the multi-node cluster; The fifth module is used to update and iterate the machine learning models corresponding to each network data analysis function group of the multi-node cluster based on the trained fused machine learning model until a preset condition is reached.

9. An electronic device, characterized in that, It includes a processor and a memory; The memory is used to store programs; The processor executes the program to implement the method according to any one of claims 1 to 7.

10. A computer storage medium storing a program executable by a processor, characterized in that, The program executable by the processor, when executed by the processor, is used to implement the method according to any one of claims 1 to 7.

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