164 results about "Functional network" patented technology

Provided is a method for driving behavior modeling based on spatio-temporal information fusion, relating to the field of driving behavior simulations. The method includes: constructing a driving behavior model, where the driving behavior model includes a spatial information encoding network, a temporal information encoding network, a feature fusion network, and a feature decoding network, with the feature fusion network being connected to both the spatial information encoding network and the temporal information encoding network, and the feature decoding network being connected to the feature fusion network; determining a future trajectory sequence of a target main vehicle at future time points based on the trained driving behavior model according to spatial information and temporal information of the target main vehicle, where the target main vehicle is controlled to travel according to the future trajectory sequence at the future time points.

The invention provides a spatial-temporal feature fusion-based dynamic graph convolution electroencephalogram depression detection method, which comprises the following steps of: firstly, segmenting a sample into fragments with the length of 1 second, and calculating power spectral density (PSD) as an input feature by adopting a Welch method; the time sequence and spatial characteristics of the EEG signals are synchronously extracted through a double-branch architecture, wherein one branch captures the long-term time sequence dependence of the EEG signals by using a GRU; and the other branch adopts an improved TSCN (separable convolution is introduced), multi-scale spatial features from fine to rough are extracted through causal convolution and expansion convolution of residual layered stacking, after double-branch features are adaptively fused based on an attention mechanism, a dynamic graph structure is constructed, functional connection evolution of brain intervals is modeled by using a graph convolution network, and a dynamic graph structure is constructed. The topological structure of the network is optimized through a back propagation process, and finally depression identification is realized through a Softmax classifier. According to the method, the time sequence modeling capability of the GRU and the multi-scale spatial analysis capability of the TSCN are fused, the representation limitation of a single model is broken through, the dynamic change of a brain function network is adaptively captured through dynamic graph convolution, the physiological interpretability is enhanced, deep complementary fusion of EEG spatial and temporal characteristics is realized, the depression recognition accuracy is remarkably improved, and the method is suitable for popularization and application. And an efficient tool is provided for auxiliary diagnosis of mental diseases.

The invention discloses a tumor space-occupying brain network neural image alignment method based on multi-modal fusion, and belongs to the technical field of medical image processing and artificial intelligence crossing. The method comprises the following core steps of multi-modal image heterogeneous feature decoupling, tumor occupation deformation field modeling, functional network topological structure maintenance, cross-modal feature adversarial alignment, dynamic deformation constraint optimization and clinical interpretability verification, and construction of a three-dimensional non-rigid registration network based on a double attention mechanism. And differential homeomorphic mapping of a tumor focus area and normal brain tissue is realized through the cascaded spatial transformation module. Aiming at the problems of insufficient multi-modal feature alignment and brain network topology distortion in the prior art, the invention provides a function connection constrained cross-modal fusion strategy, a graph convolution network is adopted to encode resting state function connection features, and network node displacement caused by tumor occupation is dynamically corrected in combination with deformable convolution and a bidirectional feature competition mechanism; a space consistency loss function based on white matter fiber bundle tracing is designed, and through diffusion tensor imaging feature guide structure-function bimodal joint optimization, the problems of insufficient registration precision in a focus area and whole brain network connection distortion of a traditional method are solved. Experiments show that the registration precision of the method in glioma cases reaches 0.82 mm and is improved by 37% compared with that of a traditional method, and dissection-function consistency of functional network reconstruction around tumors is remarkably improved.

The invention provides a lightweight satellite network security management system and method. According to the system, unified management of a satellite network security control plane is realized through the centralized network controller. The system has the functions of satellite node security authentication, dynamic security policy generation and issuing, real-time network monitoring, rapid security event response and the like. The network controller comprises an authentication module, a strategy generation module, a monitoring module, an event response module and the like, a security strategy can be customized according to satellite characteristics, and security threats can be dealt with through real-time monitoring and timely adjustment. The invention also comprises a lightweight design which is particularly suitable for a satellite environment with limited resources, and a method for continuously optimizing a security policy based on monitoring data and event records. In addition, the provided user interface allows a satellite network administrator to conveniently perform policy configuration and event processing. According to the system, through centralized management and dynamic adjustment, the safety and the operation efficiency of the satellite network are remarkably improved, the system has the advantages of light weight, flexibility, real-time performance and the like, and the safety management requirement of the modern satellite network is met.

The invention relates to a communication method, device and system. And the first core network element determines that a service corresponding to the terminal equipment is locally processed according to the first information, and a control plane signaling corresponding to the locally processed service is forwarded to a local function network element by the first network element and does not pass through the first core network element. The first information comprises one or more of the following items: information of the terminal equipment, information of the first network element, or information of at least one core network element. And the first core network element sends indication information to the first network element so as to indicate the service corresponding to the terminal equipment to be locally processed or indicate the information of the service locally processed by the terminal equipment. In the embodiment of the invention, the first core network element can determine whether the service corresponding to the terminal equipment is locally processed or not according to the first information, namely, a method for determining whether the service of the terminal equipment can be locally processed or not is given, so that the service of the terminal equipment can be locally processed.

The invention discloses a transcranial electrical stimulation physiotherapy system based on EEG-fNIRS feedback. The transcranial electrical stimulation physiotherapy system comprises an EEG-fNIRS signal acquisition and processing module, a relaxation degree evaluation module, a transcranial electrical stimulation control module and a transcranial electrical stimulation execution module. The transcranial electrical stimulation physiotherapy system provided by the invention can accurately extract spatial and temporal characteristics related to relaxation degree, constructs a personalized brain function network model, accurately locates stimulation sites and neural activity modes based on the characteristic that EEG and fNIRS data complement each other in terms of advantages in terms of time resolution and spatial resolution, and improves the rehabilitation effect of the brain function network model on the basis of the characteristic that the advantages of the EEG and fNIRS data complement each other in terms of time resolution and spatial resolution. The method realizes personalized dynamic adjustment of the relaxation degree, and is suitable for multiple fields of education, medical treatment, rehabilitation and the like.

The invention belongs to the technical field of computer-aided diagnosis, and particularly relates to a dynamic brain network feature learning method based on Mamba, which comprises the following steps of: performing data processing, including data collection, data preprocessing, dynamic function network sequence construction and state conversion sequence construction; performing sample enhancement by taking the processed data as sample data; constructing a Mama model comprising a linear embedding layer, a Mama layer, a linear mapping layer I, an activation function and a loss function as a classification model; training the Mama model by using the state conversion sequence, and optimizing hyper-parameters of the Mama model; and based on the trained Mama model, predicting the enhanced sample data, and determining a classification result of the sample data through a label output by the Mama model. According to the method, the relation hidden in the dynamic brain network state transition sequence can be efficiently captured, and high-precision classification of each brain network sequence is realized.

The invention discloses an epilepsy abnormal brain network identification method based on a multi-scale static-dynamic fusion network, and belongs to the field of brain image analysis. The method comprises the following steps: firstly, constructing a static function connection weighted graph and a dynamic function connection graph; and fusing the static and dynamic representations by adopting a cross attention module. In order to describe a multi-scale spatial relationship, performing lexical meta-processing on brain connection according to anatomical partition and a functional network; and the local-global fusion module is used for integrating the fine granularity and the macroscopic relationship, so that the brain region with diagnostic significance is highlighted. In the training stage, cross entropy, reverse contrast loss and sparse regularization based on contrast graph adjacency matrix entropy are jointly used. The method is verified on multi-center functional magnetic resonance data, compared with other mainstream depth models, the classification accuracy, generalization and interpretability are remarkably improved, an abnormal brain region consistent with an epilepsy network can be positioned, and brain image markers with biological significance can be connected and recognized.

The invention relates to the technical field of medical image processing, and discloses a brain network image analysis method, system and device for a glaucoma patient and a medium, and the method comprises the steps: carrying out the brain region scanning of the glaucoma patient, and obtaining a high-resolution structure image, a resting state function image and a diffusion tensor image; pre-processing the resting state function image and the diffusion tensor image, and respectively registering the resting state function image and the diffusion tensor image with the high-resolution structure image to obtain a registered resting state function image and a registered diffusion tensor image; performing brain region division on a glaucoma patient, wherein each region is used as a node; on the basis of registration resting state functional imaging, Pearson's correlation coefficients between the nodes are calculated to construct a whole-brain functional network; on the basis of registration diffusion tensor imaging, the number of fiber bundle connections between nodes is calculated to construct a whole-brain structure network; and carrying out coupling analysis on the whole brain function network and the whole brain structure network to obtain an F-S coupling coefficient. The method is helpful for deeply researching the influence of glaucoma on the brain network.

The invention discloses a brain disease diagnosis model based on a multilevel causal intervention network, and relates to the field of brain function network analysis and medical auxiliary research. The method specifically comprises the following steps: a brain network pre-construction stage: performing data preprocessing, extracting an fMRI time sequence, and constructing input features required by a model by using tangent Pearson embedding; feature extraction of a brain network is realized by using a three-layer Transform network of a self-attention mechanism; fusing multiple layers of attention by using an HCI module and correspondingly dividing brain network features into causal and non-causal parts; applying DCI to apply a first-order causal constraint to the model to reduce interference of non-causal factors, introducing ICAC into a causal part, and applying a second-order causal constraint to the model to further optimize the model; and realizing classification of brain diseases by using an orthogonal clustering (OCR) readout function. According to the method, non-causal interference factors can be effectively filtered, and high-order function connection information is extracted, so that the accuracy and robustness of brain disease analysis are improved.

The invention discloses an electroencephalogram multi-index network standardized modeling method which comprises the following steps: S1, adopting a multi-country electroencephalogram cross-spectrum data set based on a large-scale full life cycle as original electroencephalogram data, calculating a cross-spectrum matrix of the original electroencephalogram data, and preprocessing the original electroencephalogram cross-spectrum matrix; s2, constructing a weighted undirected network by using the preprocessed original electroencephalogram cross spectrum matrix to serve as a brain function network; s3, calculating network features of three dimensions of brain network function integration, function separation and centrality of the brain function network, and establishing an evolution trajectory of the network features; and S4, designing an interpretable lightweight neural network regression model, and reconstructing upper triangular elements of the brain function network to obtain a complete brain function network, namely a normalized brain function network corresponding to the age. The invention further discloses an electroencephalogram multi-index network standardized modeling system. The principle of the brain function network can be reflected more accurately.

System and methods that improve the use of network functions on edge data networks are disclosed. A mechanism that supports edge-assisted UE context and trigger collection is described herein, where a UE can actively send its context information and / or any trigger (e.g. a request for a network function / service to be deployed in edge data networks, etc.) to an edge enabler server, which processes the received context and triggers from its UEs. The edge enabler server may also forward UE context and triggers to 5G core network (5GC) upon receiving a solicitation request from the 5GC, or if the 5GC has already made a subscription for getting notification on UE context and triggers. A network repository function is provided as a new network function collect, store, and manage edge data network information.

The invention relates to the technical field of medical data processing and nerve regulation and control, in particular to a method for synchronously activating post-stroke neuroplasticity through photoacoustic, which comprises the following steps of: 1, constructing a stroke specific brain region target map: fusing brain structure connection data and functional network data of a patient, generating a three-dimensional target map containing the high-metabolism semi-dark band coordinate set and the cross-hemisphere compensatory connection intersection; 2, calculating a time-space synchronization focusing parameter; 3, synchronous stimulation and dynamic regulation are executed, wherein the ultrasonic transducer and the laser source are controlled to emit time-space synchronous sound waves and light pulses; monitoring the blood perfusion variable quantity and the neurotransmitter concentration ratio of the target brain area in real time; when the blood perfusion variable quantity does not reach the expectation or the transmitter ratio is unbalanced, the stimulation intensity is dynamically adjusted, and the focusing coordinate is translated in the direction away from the infarction area. Through photoacoustic stimulation of time-space synchronization, the difference between the propagation speeds of sound waves and light waves is overcome, and accurate collaboration of neural restoration is achieved.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for determining a mental state of a patient based on a natural language input and determining whether a relevant subset of brain data is anomalous. One of the methods includes receiving a natural language input describing at least one aspect of a mental state or of a behavior of an individual; generating a prompt based on at least in part the natural language input; submitting the prompt to a large language model; receiving at least one functional network that influences the at least one aspect of a mental state or of a behavior; for each network of the at least one functional network, analyzing MRI data for the individual to determine whether the network is anomalous; displaying to a user each network and whether it is anomalous; and taking an action in response to the displaying.

The invention provides a communication method and a communication device applied to the field of wireless communication. In the technical scheme provided by the invention, when a user deploys a home base station and the home base station is in a closed mode and there is a demand for allowing a visitor terminal to access the home base station, a management terminal of the home base station can send first information to a first functional network element, and the first information indicates that the visitor terminal which is allowed to access the home base station is allowed to access the first functional network element. Therefore, the first function network element can trigger the flow of updating the signing data of the visitor terminal based on the first information, and the closed access group information corresponding to the home base station is stored in the signing data of the visitor terminal, so that the visitor terminal can access the network through the home base station to obtain network service.

The invention discloses a coupling tensor decomposition method based on sparse low-rank constraint, which is used for characteristic decomposition of a multi-frequency dynamic function network connection tensor in resting state function magnetic resonance imaging data. According to the algorithm, on the basis of the traditional coupling canonical factorization (CCPD), an optimization model of sparse and low-rank constraint is constructed, and the sparse and low-rank constraint optimization model is constructed by the algorithm. On the spatial connectivity dimension, redundant function connection is reduced through an L1 sparse penalty term, and the spatial specificity of the key brain network is enhanced; and in time and frequency band dimensions, low-rank regularization constraint is adopted to improve discrimination of cross-subject time sequence characteristics. Generally speaking, the method can effectively extract connectivity characteristics with statistical significance and time states of different frequency bands from dynamic function network connection tensors of multiple frequency bands, thereby effectively identifying functional connection heterogeneity characteristics between schizophrenia patients and healthy control groups.

The invention relates to a depression risk detection method based on self-adaptive multi-scale neighborhood perception fusion space-time diagram convolution, and aims to solve the problems that traditional depression diagnosis is subjective and existing electroencephalogram detection space-time feature modeling is insufficient. The method comprises the following steps: acquiring electroencephalogram signals of a testee, and extracting frequency spectrum features through a frequency domain feature extraction module; in combination with an adjacent matrix (independent of an electrode physical distance) which is adaptively learned and normalized in training, embedded features are generated through a graph convolutional neural network; and then capturing long-time-history dependence through a dynamic feature extraction module, realizing feature multi-level fusion by means of a multi-cascade multi-scale convolution module, finally integrating local and global features through an adaptive feature fusion module, and inputting a prediction layer to output a detection result. According to the method, electroencephalogram signal time-space domain joint modeling is achieved, brain region function connection dynamic and multi-scale neural features are effectively captured, information redundancy and key feature loss are avoided, classification accuracy and generalization ability are remarkably improved, depression-related key brain region and function network features can be revealed, and the method has high interpretability and potential clinical application value.

The invention provides an automatic test system and method for a third-party high-power repeater, and relates to the technical field of automatic test equipment, and the system comprises an integrated reconfigurable passive link box, a control host, a vector signal generator and a vector signal analyzer. The integrated reconfigurable passive link box comprises a box body, a radio frequency switch matrix network, an embedded function network, a control port, an input port, an output port, an uplink interface and a downlink interface. The radio frequency switch matrix network and the embedded function network are arranged in the box body. The input port, the output port, the uplink interface and the downlink interface are arranged on the surface of the box body. The vector signal generator is connected with the input port, the vector signal analyzer is connected with the output port, and a third-party high-power repeater to be tested is arranged between the uplink interface and the downlink interface. According to the invention, the radio frequency switch matrix network and the embedded function network are packaged in the integrated reconfigurable passive link box, and the control host is utilized to carry out automatic testing on the third-party high-power repeater according to the preset process, so that frequent operation processes are avoided, and damage to instruments is reduced.

The invention relates to a marine ship integrated network scheduling management system and method, the system comprises a Panabit intelligent gateway, a ship communication device and a ship local area network, and the ship communication device is connected with the ship local area network through the Panabit intelligent gateway. The Panabit intelligent gateway has the functions of link detection, link intelligent switching, monitoring management, application identification, Internet access authentication, intelligent flow control, network analysis, fault location, SD-WAN networking and Bypass, and the network scheduling management method adopts the SD-WAN networking technology of the Panabit intelligent gateway. Through flow acquisition nodes and flow safety control levels of a marine ship integrated network scheduling management system, division, management and control capabilities of a safety management domain of a ship-shore communication network are carded, a multi-line satellite line preferential route selection and a command center are realized, real-time networking and intelligent reconnection technologies are realized, ship-shore communication service smoothness is guaranteed, and a ship-shore communication system is ensured to have a good application prospect. Timeliness and safety are realized.

The invention relates to the technical field of neurotrauma prognosis image analysis, and discloses a craniocerebral trauma prognosis prediction analysis system based on a three-dimensional model. According to the system, multi-scale segmentation and topology construction are carried out on a craniocerebral three-dimensional image of a patient, the morphological evolution rate of a trauma area is tracked, and key signal events in the trauma evolution process are accurately recognized in combination with an edema signal change curve. The system further quantitatively analyzes dynamic deviations associated with the integrity of normal brain tissue fiber bundles when a signal event occurs, thereby generating a lesion propagation path and mapping it to functional network nodes of a standard brain map, ultimately identifying a prognostic key brain network. According to the technical scheme, key event capture and path foresight prediction in the dynamic propagation process of the secondary injury after the craniocerebral trauma are realized, and the accuracy of prognosis evaluation is improved.

The invention discloses a personalized MCI electrical stimulation intervention method based on a dynamic brain network, and belongs to the technical field of electrical stimulation target determination. Individual heterogeneity of a brain function network of a patient is considered, specific frequency band selection is carried out on electroencephalogram data sets of a normal group and a cognitive impairment group, an effective data set is constructed, and the effective data set is determined. The method comprises the following steps: respectively constructing average dynamic brain network connection of total test times of a normal group and a cognitive impairment group based on an adaptive method of dynamic time-varying weight optimization of reaction time, and then determining a main abnormal frequency band through the difference of the average dynamic brain network connection of the two groups; and then target points are determined for the core nodes connected with the average dynamic brain network of the main abnormal frequency band, and compared with traditional single-frequency-band analysis, the application proposes that the effective data set is constructed by the specific frequency band to determine the main abnormal frequency band; in addition, stimulation targets are positioned according to the core nodes connected with the average dynamic brain network of the abnormal frequency band, and the reliability of target selection is improved.

The embodiment of the invention provides a method and a device for determining a service strategy and a communication system. The method comprises the following steps: a first function network element acquires user information of a first service; determining policy information of the first service according to the user information of the first service; the policy information of the first service comprises execution range information of the first service and service quality information of the first service; and the first function network element sends the policy information of the first service to the second function network element. According to the method, the first function network element determines the strategy information of the service from the service dimension, the service quality of the service can be provided, the execution range of the service can also be provided, and any new service of the communication network service can be supported as well. Therefore, the method can effectively meet and support strategy requirements of various services.

Disclosed in the embodiments of the present application are a quantization-based data processing method and apparatus, and a device and a medium. The method comprises: using a plurality of bit quantization modes to respectively perform quantization processing on an original model parameter corresponding to a model function network, so as to obtain a quantized model parameter corresponding to each bit quantization mode; then, acquiring a first output result of the model function network under the original model parameter, and acquiring a second output result of the model function network under each quantized model parameter; and next, on the basis of the first output result and a plurality of second output results, selecting a target bit quantization mode from among the plurality of bit quantization modes, wherein the target bit quantization mode is used for performing quantization processing on the original model parameter during an inference phase of the model function network. The technical solution of the present application improves the accuracy of quantization processing, and achieves good model performance.

The invention discloses an intelligent power transmission and transformation monitoring and fault early warning system based on Internet of Things, which relates to the technical field of power transmission and transformation monitoring and Internet of Things and comprises a data acquisition layer, a network transmission layer, a data processing layer and an early warning display layer. The functions of quantum encryption communication and self-diagnosis repair are achieved; multi-element networking is adopted for network transmission, quantum key distribution and a block chain are combined, an SDN and SD-WAN fusion architecture is used, a three-layer architecture is used for data processing, a quantum neural network and knowledge graph assistance is used, an MR technology and a brain-computer interface are used for early warning display, and all layers collaboratively guarantee efficient operation of the system. The system has obvious advantages, data acquisition is comprehensive and safe, and the sensor can be self-diagnosed and repaired; network transmission is stable and reliable, and data security is guaranteed; data processing is accurate and efficient, and early warning accuracy is improved; the early warning display interactivity is high, the equipment can be scientifically evaluated, the maintenance decision is optimized, the cost is reduced, and the stable operation of the power transmission and transformation system is ensured.

The invention provides a brain network construction and analysis method based on multivariable analysis. The method is mainly used for multi-view analysis of a brain structure and a functional network. The technical problem to be solved is that information loss is caused by neglecting necessary multivariate relationships among brain region nodes when univariate analysis is carried out on a brain function network and a brain structure network. According to the method, the canonical correlation analysis method based on data driving is used for constructing the single-mode brain network, the problem that a traditional univariate analysis method neglects necessary multivariate relations is avoided, the complex relation between the brain structure and functions can be reflected more accurately, and a more reliable basis is provided for deep research of a brain working mechanism.

The invention relates to the technical field of brain function network analysis, in particular to a post-stroke aphasia patient fMRI focal recognition method based on deep learning. The method comprises the following steps of: acquiring a region consistency value for geometric structures of matched local regions of two adjacent fMRI images in an fMRI image sequence, similarity of gray distribution and an image matching degree, and dividing a same time window by using the region consistency value; and obtaining a functional connection coefficient according to the difference of the region consistent values of the fMRI images in two adjacent time windows and the difference of the geometric structure features and the gray level distribution features of the local regions, determining the convolution kernel size of deep learning of the fMRI images in the time windows by using the functional connection coefficient, and performing focal recognition on the fMRI images in the time windows. According to the method, the function connection coefficient presenting the cooperation degree of the brain region activity modes in the adjacent time windows is analyzed, the convolution kernel size of the time windows is determined in a self-adaptive mode, and the recognition effect on the local focus of the patient is improved.

Various systems and methods for providing decentralized reputation management in a named-function network are described herein. A compute node is configured to access an information centric network (ICN) interest packet from a user device, the ICN interest packet including a function name and a data name; construct a named-function network (NFN) interest packet using the function name; transmit the NFN interest packet to a function provider; receive an NFN data packet with a version of a function corresponding to the function name; construct a named-data network (NDN) interest packet using the data name; receive an NDN data packet with a data value corresponding to the data name; determine that the version of the function is not on a denylist; and initiate execution of the version of the function with the data value in response to determining that the version of the function is not on the denylist.

The invention discloses a coal gasification device key parameter diagnosis method and system based on digital twinning, and relates to the field of coal gasification device parameter diagnosis, and the method comprises the following operation steps: S1, multi-source data acquisition and intelligent preprocessing, S2, dynamic digital twinning hybrid modeling, S3, dynamic digital twinning hybrid modeling, S4, dynamic digital twinning hybrid modeling, and S5, dynamic digital twinning hybrid modeling. Constructing a dynamic digital twinborn body in which the mechanism model and the data driving model are deeply coupled; s3, performing real-time diagnosis on the health degree of the key parameters; s4, fault root cause tracing and model dynamic correction; and S5, optimizing the coal types. According to the coal gasification device key parameter diagnosis method and system based on the digital twinning, based on strong correlation parameter linkage verification of a function network topology library, the misjudgment rate of a single parameter can be greatly reduced; the graded response mechanism realizes stepped processing of digital twinborn optimization in a normal state, automatic fine adjustment of an early warning state and rapid alarm in an abnormal state, so that potential faults can be found in advance, and the problem of non-planned shutdown is avoided.

The invention provides a brain function network causal analysis method based on phase-space reconstruction and unified GCA, and relates to the field of functional brain network analys.The method comprises the steps that fMRI data are collected and preprocessed, and a time sequence of interested nodes is extracted from the preprocessed fMRI data; for extracting time sequences X and Y of any two to-be-analyzed interested nodes, constructing a variable time delay unified Granger causal model based on phase space reconstruction; and traversing all to-be-analyzed node pairs of interest, calculating the causal direction and strength between each pair of nodes to construct a whole-brain directed causal connection matrix, and performing network metric attribute analysis. According to the method, phase-space reconstruction is taken as a core, a causal analysis framework is provided by unifying GCA, and end-to-end modeling is realized. The final target is to generate a high-fidelity fMRI data model, so that the causal connection relationship is closer to a brain real neural mechanism, and the reliability and the application value of functional brain network research are improved.

The invention provides a communication method and device, and the method comprises the steps that a first network element sends a network element discovery message to a network storage function network element, the network element discovery message is used for discovering an analysis function network element, the network element discovery message comprises a first interoperation identifier, and the first interoperation identifier comprises a first manufacturer identifier list; the first network element receives information of one or more analysis function network elements from the network storage function network element; wherein the identification of the manufacturer of the analysis function network element is one in the first manufacturer identification list. By adopting the method, the network storage function network element can use the model provided by the model training function network element corresponding to the first interoperation identifier for the analysis function network element discovered by the first network element, so that the first network element can select the analysis function network element as the target analysis function network element; therefore, the service can be better provided for the service consumption network element.