679 results about "Automatic learning" patented technology

The invention discloses an intelligent power distribution network monitoring and fault detection system, and relates to the technical field of power distribution network detection. Comprising a power distribution network region division module, a periodic data acquisition and monitoring module, a data preprocessing and feature extraction module, an intelligent load state evaluation module, a sub-region state division module, a conventional monitoring module and a dynamic adjustment and anomaly response module, therefore, finer monitoring and management can be realized. According to the method, the power distribution network is divided into the sub-regions, and periodic state scanning is combined, so that the system can accurately identify local overload and abnormal load and respond to abnormal fluctuation in time. LSTM is combined to evaluate load fluctuation, automatic learning adapts to load change, the intelligence, flexibility and stability of the system are improved, and safety and reliability of power supply are ensured.

The invention relates to the technical field of power equipment fault diagnosis, in particular to a power equipment fault intelligent diagnosis method and system based on deep learning. The method comprises the following steps: automatically learning high-dimensional space-time correlation features in original time series data through a deep feature extraction network, and generating feature vectors representing potential abnormal modes of equipment; performing adaptive weight distribution on the high-dimensional space-time correlation features by using an attention enhancement mechanism, and marking a fault sensitive area to form enhanced fault features; inputting the enhanced fault features into a multi-level classifier for joint fault mode recognition and severity evaluation, and outputting a diagnosis result tensor containing a fault type and confidence; and an equipment maintenance decision signal is triggered based on the diagnosis result tensor, and the feature extraction network and classifier parameters are iteratively optimized according to feedback data, so that the intelligent level of operation and maintenance of the power equipment can be comprehensively improved.

The invention discloses an intelligent working place dust monitoring and early warning system. The system comprises a dust sensor module, a data processing and storage module, a wireless communication module, an alarm module and a visual interface. The dust sensor detects the dust concentration in the air in real time, dust with different particle sizes is included, the data processing module carries out concentration calculation, trend analysis and acceleration monitoring, and dynamic threshold adjustment and trend prediction are supported. The system has a multi-stage alarm function, and when the dust concentration reaches a preset threshold value, early warning signals with different intensities are triggered to remind a user to take protective measures in time. The system not only improves the real-time performance and accuracy of dust monitoring, but also has automatic learning and intelligent early warning functions, is suitable for various high-dust environments such as mines, construction sites, workshops and the like, and remarkably improves the safety management level of workplaces.

The invention discloses a massive multi-source and multi-modal data fusion method, which is a technology for efficiently fusing and processing various types of data from different sources, realizes effective integration, utilization and seamless integration of multi-source and multi-modal data, and improves the breadth and depth of data analysis. According to the multi-modal feature extraction and fusion model based on deep learning, the deep features of all modal data can be automatically learned and extracted, and efficient fusion is carried out in the model. Besides, a data quality evaluation and self-adaptive adjustment mechanism is introduced, parameters and strategies in the data fusion process are dynamically adjusted according to the quality and distribution condition of the data so as to ensure the stability and reliability of the fusion result, and the method can be widely applied to multiple fields such as big data analysis, artificial intelligence, social media analysis, medical health and smart cities.

The invention discloses a wind turbine generator operation and maintenance knowledge base construction method based on a large model and mechanism self-learning. The wind turbine generator operation and maintenance knowledge base construction method comprises the steps of wind turbine generator operation and maintenance domain knowledge Schema definition and large model cue word template design used for wind turbine generator operation and maintenance knowledge extraction; obtaining operation and maintenance multi-modal data of the wind turbine generator, performing preprocessing, and performing knowledge extraction through a large model based on a designed cue word template; a dynamic knowledge association and wind turbine generator operation and maintenance knowledge base fault mechanism self-learning updating mechanism is established, operation and maintenance data and a knowledge graph are associated in real time, and the knowledge base is automatically learned and updated through an exception triggering mechanism; and constructing and storing a wind turbine generator operation and maintenance knowledge graph based on a knowledge extraction result, generating a semantic association sub-graph through clustering, generating a sub-graph clustering report, and realizing efficient knowledge retrieval. Based on the above content, the wind turbine generator operation and maintenance knowledge base which is efficient, accurate and updated in real time is constructed.

The present invention relates to the technical field of data simplification, in particular to a point cloud data simplification method for power line towers, and a system. The simplification method comprises: collecting point cloud data of power line towers, and carrying out data processing; performing data feature extraction on the processed data; classifying the point cloud data on the basis of the extracted features; and simplifying the classified point cloud data by means of using a point cloud simplification algorithm. The present invention introduces a deep learning technology to automatically learn and extract rich semantic features in point cloud data, thereby providing efficient feature representations for subsequent classification and simplification; by using extracted advanced semantic features and by means of a deep learning algorithm, the present invention can perform accurate classification and recognition on power line towers, thereby achieving automatic target detection and recognition; the present invention achieves intelligent simplification of point cloud data of power line towers by means of an intelligent merging strategy, so as to greatly reduce the data volume while reserving key features, thereby improving storage and transmission efficiency.

The invention relates to the technical field of medical diagnosis systems, in particular to an early gonitis diagnosis system and method based on multi-modal deep learning, a data collection module is used for collecting and preprocessing multi-modal data including knee joint X-ray image data, MRI (Magnetic Resonance Imaging) image data and clinical evaluation data; the model construction module constructs a deep learning model by combining a convolutional neural network and a Transform architecture, realizes mapping from multi-modal data to a clinical assessment phenotype label by adopting a feature automatic learning module and a potential relationship mining module, and integrates a Grad-CAM interpretability method for understanding a model prediction basis; diagnostic data are continuously collected to be fed back to the data collection module for model optimization, the convolutional network is good at capturing local image features, the Transform architecture is good at modeling a long-range dependency relationship, and the feature extraction and relationship modeling capabilities of the model are greatly improved through combination of the convolutional network and the Transform architecture.

The invention relates to the technical field of artificial intelligence, and discloses an anti-fraud model training method based on deep learning, which comprises the following steps: step 1, preprocessing and constructing multi-modal data, carrying out abnormal value processing, missing value filling and feature standardization on original data, representing processed transaction data and geographic information through a feature extraction module, and carrying out feature extraction; the equipment information and the social relation graph are embedded through an embedding layer and a graph neural network; step 2, based on data enhancement of self-supervised learning, pre-training unlabeled data by designing a behavior prediction task and a mask filling task so as to initialize a main model by using generated model parameters, and generating a synthetic sample extension training data set; by designing a behavior prediction task and a mask filling task, automatic learning and enhancement of features are realized by using label-free data, the dependence on labeled data is reduced, the detection capability of the model on fraudulent behaviors is remarkably improved, and the diversity of training data is enriched.

The invention belongs to the technical field of forest fire early warning, and discloses a forest fire prevention method based on smoke recognition, which comprises the following steps: acquiring heterogeneous image data from different monitoring devices, performing format unification and quality enhancement through a data preprocessing module, eliminating the difference of data from different sources, and obtaining preprocessed image data; a multi-scale feature extraction network based on deep learning is adopted, key features such as the color, texture and form of smoke are automatically learned and extracted from the preprocessed image data, and feature representation is obtained; according to the method, corresponding early warning threshold values are set according to different grades of fire judgment results, when the fire grade judgment results meet early warning conditions, early warning signals are triggered automatically, and early warning of the fire is achieved in the modes of message pushing, sound-light alarm and the like.

The invention discloses a network intrusion prevention method, apparatus and device, and a storage medium. The method comprises the steps of extracting feature information representing network behaviors from collected network flow data; inputting the feature information into a reinforcement learning model, and performing reinforcement learning on the feature information through the reinforcement learning model to obtain an intrusion prevention strategy; and acquiring environment feedback corresponding to execution of the intrusion prevention strategy, and adjusting the reinforcement learning model according to the environment feedback. Through the reinforcement learning model, automatic learning is carried out from a large amount of network flow data and adjustment is carried out to cope with continuously changing network threats, so that the accuracy and efficiency of network intrusion prevention are improved, and through automatic optimization of the model, the requirement of manual intervention is reduced, and the operation cost is reduced.

The invention provides an urban road traffic risk prediction method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining the multi-source data of each road of a to-be-predicted target city, and carrying out the preprocessing of the multi-source data, and obtaining the standardized data; on the basis of a trained time-space sequence deep learning model, traffic risk prediction is carried out on the standardized data, the traffic risk probability of each road of the target city in the future time period is obtained, and the time-space sequence deep learning model comprises a convolutional neural network CNN model and a long short-term memory network LSTM model which are connected in series. According to the method, the acquired multi-source data is preprocessed, the generalization ability and adaptability of the model are enhanced, traffic risk prediction is performed on the standardized data based on the trained time-space sequence deep learning model, a complex mode can be automatically learned from a large amount of time-space data, the risk prediction accuracy is improved, and convenience is brought to urban traffic management.

The invention discloses a BIM (Building Information Modeling) data automatic classification and tagging method based on an intelligent rule. The method comprises the following steps: S1, data preprocessing; s2, feature extraction; s3, intelligent classification and tagging are carried out; s4, verifying and optimizing a result; and S5, user interaction and report generation. According to the method, efficient processing of BIM data is realized by integrating a multi-dimensional feature fusion technology and an advanced deep learning algorithm. BIM data generally comprises various complex information such as huge three-dimensional models, design drawings, material lists and the like, and a traditional method is often low in efficiency and prone to making mistakes when processing the data. The feature fusion technology adopted by the invention can effectively integrate information from different dimensions, and automatically learn and extract key features through a deep learning model, thereby greatly improving the accuracy and efficiency of data processing.

The invention provides a video monitoring early warning method based on a multi-modal behavior mode, and the method comprises the following steps: extracting multi-modal data according to a video stream, carrying out the preprocessing, carrying out the alignment and fusion of the preprocessed multi-modal data, and enabling the multi-modal data to comprise RGB frames, an optical flow field, human skeleton key points, and a scene semantic segmentation map; performing short-time behavior pattern feature and behavior pattern library establishment and scene behavior baseline construction on the fused multi-modal data; updating a dynamic behavior pattern library based on online learning, reducing a historical data weight in combination with a time decay factor, and adjusting an anomaly judgment threshold according to the complexity of a current scene; performing multi-level early warning according to the abnormal judgment threshold value, and performing feedback optimization; according to the method, an intelligent video monitoring early warning scheme which can automatically learn a scene behavior mode, dynamically adjust an early warning threshold value and fuse multi-dimensional feature analysis is provided, and the problems of high false alarm rate and poor adaptability in the prior art are solved.

The invention discloses an unmanned aerial vehicle intelligent inspection system based on AI vision and a detection switch cabinet, and relates to the technical field of unmanned aerial vehicle intelligent inspection, the unmanned aerial vehicle intelligent inspection system comprises an unmanned aerial vehicle inspection platform, and the unmanned aerial vehicle inspection platform is in communication connection with the following modules: an unmanned aerial vehicle end, which is used for collecting and preprocessing video stream data of an inspection area; extracting a key frame from the preprocessed video stream data; and the AI visual analysis module is used for analyzing the extracted key frame by using an AI visual algorithm and identifying key information and abnormal fragments in the key frame. According to the invention, through the AI vision algorithm based on the convolutional neural network model, abnormal features can be automatically learned and identified, the abnormal types and specific conditions can be rapidly determined through deep analysis of the key frames, accurate positioning of abnormal segments and comparison with the preset abnormal feature database, compared with manual detection, the accuracy is greatly improved, and the detection efficiency is improved. Tiny abnormal changes can be found in time, and potential faults can be warned in advance.

The invention provides a container cloud task scheduling method based on deep reinforcement learning. The method comprises the steps that container cloud task scheduling items are defined; modeling as a Markov decision model, which comprises the following steps: taking a task demand of each task and a current resource state of each available node as a state set, and taking a set of available nodes as an action set; the intelligent agent schedules the task to a certain available node according to the task requirement of the task, the current resource state of the available node and the task scheduling strategy; defining a reward function for calculating a reward after the agent schedules the task to an available node; and optimizing the Markov decision model through the deep reinforcement learning model to obtain an optimized task scheduling strategy. The invention further provides a container cloud task scheduling device based on deep reinforcement learning, a storage medium and electronic equipment. Therefore, according to the method, the expected scheduling strategy can be automatically learned from the operation information of the system, and the method automatically adapts to the changing environment without depending on expert knowledge.

The invention discloses an entity digitization and link framework algorithm based on a heterogeneous graph attention network, and the algorithm comprises the following steps: S1, heterogeneous information network construction: carrying out the unified modeling of all multi-source heterogeneous data into a heterogeneous information network containing various types of nodes and various types of edges, s2, meta-path definition and guidance: defining "meta-paths" connecting different types of nodes to capture a complex deep semantic relationship, S3, heterogeneous graph attention network embedding: adopting an attention mechanism to enable a model to automatically learn importance of different neighbor nodes and different meta-paths, generating a final embedding vector of each entity, and establishing a heterogeneous graph attention network model; according to the method, the information fidelity is higher, modeling is directly conducted on different types of nodes and relations on a heterogeneous graph, more abundant and heterogeneous semantic information in data can be reserved compared with a multi-view method, and the end-to-end learning ability is higher; and the complexity of manually designing a fusion strategy is reduced.

The invention provides a point cloud semantic segmentation method for improving a RandLA-Net network. The method comprises the following steps: step 1, carrying out data preprocessing grid downsampling; step 2, calculating a point cloud normal vector by a local curved surface fitting method; thirdly, local multi-dimensional feature fusion is carried out, and local geometric information is captured; fourthly, important features are automatically learned and aggregated through a self-attention module and attention pooling; 5, cascading an up-sampling result with the intermediate feature of the coding layer through jump connection; and 6, predicting the semantic tag by using a full connection layer. According to the invention, high-precision and high-efficiency automatic point cloud semantic segmentation in a large-scale outdoor scene is realized.

The invention belongs to the technical field of digital twinning and water conservancy management, and discloses a digital twinning water conservancy dynamic prediction and simulation method based on an AI technology, the dynamic prediction and simulation method is realized based on a digital twinning platform, and the specific steps are as follows: S1, constructing a digital twinning scene; s2, preparing model prediction training data; s3, training a prediction model; s4, realizing future prediction; and S5, performing future digital twinning simulation. By introducing the AI learning model and constructing the three-dimensional water conservancy digital simulation scene, a large amount of data is provided for the model, the model is further enabled to automatically learn and extract a complex nonlinear relationship from a large amount of historical data and real-time monitoring data, the prediction precision of hydrological data is remarkably improved, and the prediction efficiency of the hydrological data is improved. Compared with traditional static prediction, the method not only can adaptively adjust prediction parameters, but also can perform dynamic adjustment according to continuously updated environment data, and has higher real-time performance and reliability.

The invention relates to a distributed early warning log-based automatic learning and fault prediction method and system. The method comprises the following steps: analyzing historical log traffic characteristics, generating a log rate prediction value, dynamically adjusting a sampling frequency according to the rate prediction value, and outputting a lightweight feature vector with a timestamp; generating a real-time contribution weight of each node through a dynamic weight calculation function to obtain a region-level model parameter; inputting the regional-level model parameters into a feature distiller to generate representation vectors, and performing network superposition on the representation vectors to obtain a network topological relation matrix; tensor splicing is carried out on the feature vectors and a real-time network topology relation matrix, a graph neural network model is input, and a fault probability prediction value is generated in combination with a historical feature sequence; and when the real-time contribution weight of the node continuously decreases for N times and exceeds a preset threshold value, triggering an anomaly detection instruction, injecting a feature compensation vector into the FPGA preprocessing unit, and synchronizing the model updating frequency of the feature distiller.

The invention belongs to the technical field of dry quenching, and provides a dry quenching boiler inlet temperature prediction method based on a DeepSeek large model. According to the method, the inlet temperature T6 of the coke dry quenching boiler is predicted by using the data, namely the process variable, changed in real time in the coke dry quenching process; the key control variable is a manually controlled variable, the change of the process variable is controlled / adjusted by changing the key control variable, and automatic control is realized by utilizing a prediction-control joint optimization method and by means of reinforcement learning. According to the prediction method provided by the invention, a complex nonlinear relationship and interaction among multiple variables can be captured, and strong coupling and nonlinear influence among multiple process parameters in the dry quenching process can be processed; through fine tuning on a large amount of historical data, the method can automatically learn and adapt to different working conditions, reduces the dependence on artificial feature engineering, and improves the generalization capability and prediction performance of the model.

The invention relates to the technical field of rainfall type loess side slope instability early warning methods, and discloses a rainfall type loess side slope instability early warning method and system based on multi-source data fusion, and the method comprises the steps: S1, arranging a sensor network on a side slope, and collecting rainfall capacity, surface displacement and soil pressure data in real time; s2, preprocessing the collected multi-source data, including data cleaning, missing value filling and data standardization; and S3, extracting characteristic indexes capable of reflecting the stability state of the slope from the preprocessed data. Environmental data of the loess slope, such as rainfall, evaporation capacity, temperature and humidity, are acquired by using a sensor network; the deformation data comprises earth surface displacement, deep displacement and crack width; the mechanical data comprises soil pressure, pore water pressure and soil moisture content; image data: a slope surface image and a crack development image are collected, an early warning model is constructed based on a machine learning algorithm, and data features can be automatically learned.

The invention relates to the technical field of enterprise service intelligent recommendation, and discloses an enterprise service matching method and system based on a large language model, and the method comprises the following steps: S1, receiving an enterprise service demand text input by a user, carrying out the semantic analysis of the service demand text through the large language model, and obtaining a semantic analysis result; constructing a service semantic graph containing service nodes and semantic relationships; s2, establishing a plurality of agents representing different roles in the enterprise, wherein each agent corresponds to one service preference; s3, based on the service semantic graph and the service preference, node embedding is carried out by using a graph neural network, and the service combination potential is estimated; and S4, performing multi-target matching optimization according to the service preferences and the combined potentials of the multiple agents, and outputting a service matching result. A graph neural network is adopted to model service node embedding, so that a combination relationship among services is efficiently captured in a graph structure, and automatic learning of non-uniform importance among the service nodes is realized by introducing a multi-layer graph attention mechanism.

The invention relates to an electric vehicle charging interface identification and positioning system for a charging robot. The system comprises a data acquisition module, a preprocessing module, a charging interface identification module, a positioning module, a posture estimation module, a control module, an environment adaptation module and an automatic learning module. According to the system, images and three-dimensional point cloud data of a vehicle are collected through a camera and a laser radar, the two-dimensional position of a charging interface is automatically recognized through preprocessing and a deep learning model, and the spatial position and posture of the interface are accurately positioned by combining a least square method with three-dimensional coordinates. The control module drives the mechanical arm to be in butt joint with the plug through PID feedback control and A * path planning, and automatic charging is achieved. And the environment adaptation module combines data enhancement and adaptive correction to improve the recognition stability of the system in complex environments such as illumination and background change. The method improves the recognition precision and real-time performance of the charging robot in different environments, and is suitable for an unattended automatic charging scene.

The invention discloses an embroidery stitching method backstepping and dynamic reproduction method based on deep learning, and belongs to the technical field of embroidery stitching method identification, and the method comprises the steps: collecting stitch data; fusing the three-dimensional point cloud data acquired from multiple view angles by using a point cloud registration algorithm, and constructing a multi-modal data set; constructing a dynamic graph space-time interaction network, and extracting feature vectors in the multi-modal data based on the dynamic graph space-time interaction network; constructing a stitch inverse model to obtain a stitch space layout instruction, dynamic process parameters and an optimized array sequence; and constructing a dynamic reproduction model to obtain an actual embroidering action sequence. According to the invention, the deep features of the embroidery method in the embroidery image can be automatically learned, and the accuracy of the embroidery method backstepping and the authenticity of the dynamic reproduction are obviously improved; the method can achieve the precise reverse deduction of a plurality of embroidery methods under the conditions of complex textures, different light conditions and various embroidery angles.

The invention discloses a multi-warehouse logistics path optimization method and related equipment. The method comprises the following steps: acquiring warehouse data and customer data; constructing a distribution graph model according to the obtained data, and defining a Markov decision process; extracting homogeneous features and heterogeneous features of nodes in the distribution graph model, and fusing the extracted features and initial features of the nodes to form super-relation features; inputting the super-relation features into an encoder for feature extraction, and outputting a high-dimensional vector of each node; and the decoder gradually constructs a path decision sequence of vehicle distribution according to the vector provided by the encoder. According to the method, fine description of a heterogeneous relationship is introduced into a neural network model, and end-to-end automatic solution is realized in combination with reinforcement learning. According to the method, the warehouse distribution and path optimization strategy can be automatically learned, the warehouse distribution and path planning strategy can be automatically learned without manual parameter adjustment, a better transportation cost result can be obtained in a large-scale multi-constraint distribution scene, and the method has intelligent and practical values.

The invention belongs to the technical field of hydroelectric generating set fault diagnosis, and particularly discloses a hydroelectric generating set cavitation fault diagnosis method based on multi-channel acoustic emission signal fusion. Through the technical means of acquiring the acoustic emission signals at multiple positions, a more comprehensive cavitation phenomenon data basis is provided; the representation of time-frequency characteristics is optimized through the Mel time-frequency diagram, and the problem that the dynamic characteristics of acoustic emission signals cannot be effectively captured through a traditional method is solved; multi-channel feature extraction is performed on the Mel time-frequency graph through a preset deep convolutional neural network model, so that automatic learning of deep cavitation working conditions is realized; and multi-channel fusion and classification identification are carried out on a feature extraction result through the model, so that effective integration and fault classification of features are realized. Compared with the prior art, a more accurate and comprehensive cavitation fault diagnosis effect is realized, and the diagnosis precision is improved.

The invention relates to the technical field of underground structure management, in particular to an underground structure full life cycle management method and system based on fusion sensing data. According to the technical scheme, the system comprises a sensing layer, a data transmission layer, a data processing and analysis layer and a decision support and application layer. Through cooperation of the distributed optical fiber sensor and the multiple sensors, all-around and multi-parameter monitoring of the underground structure is achieved, the monitoring accuracy and reliability are improved, historical data and operation rules of the structure are automatically learned through a data driving prediction model, different geological environments and load conditions are adapted, a scientific basis is provided for maintenance decision making, and meanwhile, the system has good application prospects. The self-adjusting capability is realized; monitoring system application and data accumulation and utilization in each stage are considered, seamless connection and cooperative work are achieved, full-life-cycle integrated management is achieved, the overall performance of the structure is improved, the service life of the structure is prolonged, the maintenance cost is reduced, and sustainable development is promoted.

The invention discloses a protein palmitoyl transferase prediction method and system based on a multi-branch deep convolutional neural network, and belongs to the technical field of bioinformatics and artificial intelligence. The method comprises the following steps: S1, obtaining a to-be-detected protein sequence; s2, inputting the protein sequence into a pre-trained iPalmT model; and S3, judging whether the target protein is palmitoyl transferase or not according to a model output result. The iPalmT model comprises a coding module, two paths of parallel convolution branches, a feature fusion module and a classification module; and after the convolution layers of each convolution branch are stacked, an SE module is arranged and is used for channel weighting and feature re-calibration. The model extracts multi-level sequence features through convolution kernels of different scales, realizes high-precision prediction through feature fusion and a residual structure, can automatically learn multi-scale features from large-scale data, realizes end-to-end palmitoyl transferase recognition, and has high accuracy and good universality.

The invention discloses a video abnormal event self-adaptive detection system and method based on time sequence modeling, and relates to the technical field of video security, and the system comprises a data preprocessing module for multi-modal feature extraction and data cleaning enhancement; the spatial-temporal feature modeling module is used for constructing an auto-encoder and embedding abnormal features; the anomaly detection and classification module is used for anomaly detection and anomaly classification; the adaptive learning module is used for online incremental learning and feedback adjustment; the event positioning segmentation module is used for event positioning and event segmentation abstract generation; the real-time processing response module is used for performing real-time processing and abnormal alarm; the data management and storage module is used for constructing a video database and compressing and encrypting data; and the system monitoring and evaluation module is used for system performance monitoring and effect evaluation. According to the invention, through online incremental learning and a feedback adjustment mechanism, new abnormal events can be automatically learned and adapted, modeling is carried out in combination with multi-modal features and spatial-temporal features, the system can more accurately capture abnormal behaviors in a video, and the detection accuracy is improved.

The present application provides an endogenous-security network method and architecture, a medium, and a device. The method comprises: extracting forwarding characteristic information of normal service data flows; delivering the forwarding characteristic information of the normal service data flows to a transport network element, so as to form a forwarding table entry, a flow table, and a forwarding white list, wherein forwarding modes in a method for binding service data flow forwarding characteristics to forwarding table entries of transport network elements comprises: an authentication and authorization mode and an automatic learning mode; the transport network element performing packet forwarding according to the forwarding characteristic information of the normal service data flows; and discarding packets of abnormal service data flows, wherein packet identification is performed for forwarding operations on the basis of a whitelist automatically generated from configuration files of switches and routers. The present application solves the technical issue of an IP network having low inherent security protection capabilities due to the openness thereof and thus requiring the deployment of a large number of external security protection facilities. The issue results in poor effectiveness, high costs, and difficulty in establishing low-cost security protection capabilities, and ultimately makes the IP network easy to attack but difficult to defend.