3055 results about "Data classification" patented technology

The invention discloses a human resource management method and system based on data security. The method comprises the steps that multi-source human resource data are collected through a standardized interface, and sensitive levels are marked in a classified mode; calling a dynamic encryption engine based on the sensitive level and the data type, performing asymmetric encryption on the structured data, and performing hybrid encryption on the unstructured data; a dynamic hierarchical access control strategy is generated in combination with user roles and service scenes, and the permission range is adjusted in real time; laws and regulations such as GDPR and CCPA are analyzed through a compliance rule base, data operation legality is automatically verified, and illegal behaviors are blocked; k-anonymity and differential privacy technologies are adopted to desensitize sensitive data, and privacy protection and data availability balance are ensured; and recording a full-process operation log based on the block chain and generating a non-tampering audit report. The system comprises a data classification acquisition module, a dynamic encryption engine module, an access control engine module and the like. According to the method, the problems of data protection rigidness, compliance response lag and privacy-utility imbalance of a traditional HR system are solved.

The invention discloses a government affair data sharing system based on a data security law risk control mode, and relates to the technical field of data management. According to the method, full-process closed-loop management from data classification and grading, differential desensitization, law and regulation compliance review, access authority precise control to behavior trajectory real-time analysis and risk trend prediction is realized, and the risk trend prediction is realized by introducing key technologies such as a context sensitive desensitization algorithm, legal knowledge graph reasoning, behavior portraits, semantic trajectory similarity scoring and risk rise rate monitoring. The method effectively improves the recognition and response capability of the hidden data re-recognition risk, and achieves the safe, controllable, compliant and ordered intelligent risk control management of government affair data in the sharing process.

A federated mining method for multimodal data based on multiple security policies includes: a federated learning framework is used as a data mining model for distributed data mining; a multiple authentication mechanism is designed; a local edge node is selected to participate in a federated computation to obtain authenticated local edge node and aggregate a dataset; a multimodal data fusion and a multimodal data classification are performed on the dataset by a generalized multimodal data feature fusion model based on a multi-headed attention mechanism; and an adaptive perturbation mechanism based on cyclic correlation analysis and differential privacy is designed to add noise round by round and dynamically. A federated mining method for multimodal data is further provided.

The invention relates to a multi-modal data and RAG technology fused power design knowledge base construction method, and belongs to the technical field of power software development. The method comprises the following steps: carrying out collection and information extraction on multi-source heterogeneous original data; the method comprises the following steps of: constructing a multi-dimensional knowledge element structure containing parameters, specifications and case relationships by carrying out classification, specialized and precise processing and cross-modal association on data; based on a vector, graph and relational database mixed storage architecture, semantic vector efficient retrieval, knowledge graph relation management and business data synchronization are achieved respectively; and a dynamic optimization result is subjected to hybrid retrieval, a dual-drive reasoning mechanism outputs compliance conclusions and bases, and a retrieval enhancement generation service ensures that output contents conform to specifications. And systematic management and intelligent application of the electric power design knowledge are realized.

The invention relates to the technical field of data processing, and discloses a cross-border e-commerce commodity recommendation system and method based on multi-source data fusion. The system comprises an acquisition module for performing data acquisition to obtain a cross-border unified data warehouse and federal learning cooperation data; the classification module performs text classification processing to obtain a user preference analysis result and an interpretable attention mark; a quantization module carries out quantization processing to obtain a cross-border selection feature matrix; the fusion module carries out weighted fusion processing to obtain a basic comprehensive score and a weight convergence detection result; the evaluation module performs risk evaluation processing to obtain risk probability distribution and cross-border compliance evaluation results; and the sorting module carries out real-time processing through lightweight preprocessing and a flow-type calculation pipeline to obtain a comprehensive score sorting list of cross-border selected products. The problem that a traditional cross-border e-commerce product selection method cannot effectively integrate multi-source heterogeneous data and cannot analyze user feedback text semantic information is solved.

The invention provides an artificial intelligence-based ultrasonic image data classification method and system, and the method comprises the steps: firstly obtaining a real-time ultrasonic scanning signal sequence containing the time sequence change characteristics of a tissue elastic parameter and a hemodynamic parameter, carrying out the noise suppression and motion artifact compensation processing, generating a standardized ultrasonic image sequence, and marking the coordinates of an anatomical boundary; then performing multi-scale anatomical structure decomposition on the ultrasonic image to obtain a local feature map set of different organization levels, inputting the local feature map set into a cascade deep classification network, and realizing cross-frame feature fusion and dynamic weight adjustment through spatial-temporal feature alignment and a multi-granularity attention distribution module to obtain a spatial-temporal feature fusion model; and the abnormal region classification probability distribution and the spatial topological relation graph are output, finally, a multi-modal diagnosis report is generated according to the abnormal region classification probability distribution and the spatial topological relation graph, an interactive three-dimensional visual interface containing risk level labels and treatment suggestions is generated after the multi-modal diagnosis report is compared with historical cases, and ultrasonic image classification accuracy and diagnosis efficiency are improved.

The invention relates to the technical field of network security, in particular to a network security situation awareness method and system, and the method comprises the following steps: extracting a source IP address and a target IP address based on a network behavior record, carrying out the statistics of the number of used ports, the transmission direction and the time interval value, analyzing the direction change times and the time interval difference, and screening abnormal communication pairs. And generating an abnormal communication pair set. According to the invention, through analyzing port usage, transmission direction and time interval value, deeply mining communication features, screening abnormal communication pairs and improving identification accuracy, dividing a time sequence window, analyzing rate fluctuation and frequency distribution, locking an unstable time window, combining with a multi-dimensional data classification behavior mode, and extracting a switching path and priority, a multi-dimensional data classification behavior mode is combined. Potential threat paths are identified independently, global threat situations are identified through node interaction relation statistics and correlation analysis and an expansion range, threat assessment precision and efficiency are enhanced, and comprehensive and reliable risk protection capability is provided for network managers.

Wireless sensing using classifier probing and refinement is described. In one example, a described method comprises: computing output analytics by a classifier based on input data constructed based on raw measurement data; mapping each output analytics to a respective mapped outcome; identifying at least one reference input data each associated with a reference output analytics and a reference mapped outcome; each reference input data being one of the input data for the classifier for which a respective reference outcome is available and is different from the reference mapped outcome; and for each reference input data for the classifier: computing a respective plurality of perturbed output analytics by the classifier, generating selected perturbed input data based on the plurality of perturbed output analytics; and re-training the classifier based on the selected perturbed input data and the associated reference outcome.

The invention relates to the technical field of agricultural data security, and discloses an agricultural data security sharing method based on federated learning, through cooperative operation of a user terminal module, a data classification module, a federated learning module, an agricultural cloud database and a security protection module, a user terminal encrypts, uploads, decrypts and feeds back agricultural data through an edge computing device; the data classification module performs multi-level label labeling on data of planting industry, animal husbandry and the like by using a differential privacy technology; the federated learning module is combined with homomorphic encryption to realize local model training and parameter aggregation; the agricultural cloud database stores encrypted knowledge assets by using an IPFS technology; the security protection module integrates block chain evidence storage and a dynamic access control mechanism, the workflow comprises data encryption uploading, privacy protection classification, federated model training, solution generation and security feedback, and by adopting a federated normal form of a data immobile model, the security protection module can effectively protect the security of the original data on the premise of ensuring that the original data is not out of the domain. And value mining and safe sharing of cross-regional agricultural data are realized.

The invention relates to the technical field of data security, in particular to a data asset classification and grading and authority dynamic management system, which comprises an asset parameter acquisition module, a classification feature judgment module, a grading weight calculation module and an authority dynamic adaptation module. According to the method, metadata and path parameters are automatically collected through a distributed crawler in combination with a regular expression, naming parameters and content feature parameters are separated to generate a structured parameter set, field naming similarity is quantized through a Levenshtein distance, content character distribution features are verified through chi-square verification, and a naming similarity and distribution feature dual verification mechanism is constructed. According to the method, sensitivity, access frequency and data volume weight are dynamically distributed through an entropy weight method, grading parameters are generated through linear superposition, an RBAC model is combined with a Dijkstra algorithm to verify access path topology legality, unauthorized access is blocked, and the heterogeneous data classification grading and authority control dynamic adaptive capacity is improved.

The invention relates to the technical field of data classification management, in particular to a knowledge graph-based foreign trade data classification management system, which comprises a term word order modeling module, a graph path generation module, a path cross analysis module, a semantic category judgment module and a classification structure output module. According to the method, morpheme-level word segmentation and word order extraction are performed on foreign trade terms, an original word order template is constructed, the reduction capability of a semantic expression sequence is enhanced, a directed path is established through morpheme-level label sorting, and the level definition of a term structure and the logic relation between nodes are enhanced; a semantic intersection area is extracted through node intersection and end point frequency analysis, the accuracy of term association judgment is improved, semantic affiliation is selected according to end point label frequency, a node classification closed loop is formed by combining labels and path mapping, links of term graph modeling, path construction, intersection analysis and affiliation output are broken through, and the term association judgment accuracy is improved. And the classification management process of foreign trade data is fully improved.

The invention relates to the technical field of cloud computing storage, in particular to a cloud storage dynamic optimization method and system, and the method comprises the following steps: based on a cloud storage data access record, recognizing an access time interval sequence, analyzing the variation amplitude of adjacent time intervals, and calculating the standard deviation of the access time intervals; and determining the access period stability according to the standard deviation and the change amplitude, and classifying the data objects to obtain a data access period stability classification value. According to the method, task scheduling is more accurate by identifying a data access time interval sequence and changes thereof and refining data classification and storage resource allocation, a high-throughput and low-delay storage path is matched, the health condition of a storage node is evaluated, a fault node is identified in advance, data stability is guaranteed, and data copy distribution is adjusted; the storage reliability and stability are improved, the storage path for accessing the intensive data is optimized, the dynamic load balance of the storage resources is realized, and the storage efficiency is improved.

The invention discloses a wind turbine generator data analysis and fault diagnosis method and system based on big data and artificial intelligence. According to the method, a blade image, a vibration signal, audio data and operation parameters are synchronously acquired through an unmanned aerial vehicle multi-mode sensor and a ground monitoring system, and a multi-source heterogeneous data set is constructed; after the data is classified and preprocessed, image features, vibration time-frequency domain features and operation parameter key value pairs are extracted respectively; dimensionality reduction is carried out by using an auto-encoder, feature-level space-time alignment is realized through an improved DTW algorithm, and a multi-dimensional fault feature matrix is generated; a hierarchical diagnosis model including a GRU auto-encoder, an MLP network and an attention mechanism CNN is constructed, and training is carried out by taking minimization of sub-model deviation as an optimization target; and finally, fusing multi-source features to realize fault classification, and generating a visual diagnosis report. According to the method, efficient fusion and accurate diagnosis of multi-source heterogeneous data are realized, and the accuracy and the real-time performance of fault detection of the wind turbine generator are remarkably improved.

The invention relates to the technical field of artificial intelligence and data processing, in particular to a roadbed settlement data identification method based on artificial intelligence, and the method specifically comprises the following steps: collecting time sequence monitoring data of a plurality of roadbed settlement sensors, and carrying out the manual marking; carrying out adaptive time domain segmentation normalization on the collected time sequence monitoring data, designing a conditional diffusion process, and generating enhanced data conforming to a soil mass mechanics constraint in a submerged space; constructing a roadbed settlement data classification model based on a one-dimensional convolutional neural network, and inputting the enhanced data into the model for training to obtain a trained roadbed settlement data classification model; and preprocessing new time sequence monitoring data collected by the roadbed settlement sensor, and inputting the preprocessed data into the roadbed settlement data classification model to obtain a settlement category classification result. According to the invention, the roadbed settlement data classification model based on the one-dimensional convolutional neural network is constructed and trained, so that the discrimination capability of the model can be enhanced, and the accuracy and reliability of model identification can be improved.

The invention relates to a digital twin workshop management and control method and system based on data multilayer fusion, and belongs to the field of service-oriented industrial system data science, and the method comprises the steps: obtaining the global multi-source heterogeneous data of a digital twin workshop, and carrying out the data classification, data processing and data storage of the global multi-source heterogeneous data, carrying out data-level fusion to obtain digital twin workshop standard data; extracting standard data of the digital twin workshop, performing semantic association, feature extraction and feature analysis on the standard data of the digital twin workshop, and performing feature level fusion to obtain key state features of the digital twin workshop; the method comprises the following steps: constructing a decision-making model oriented to a digital twin workshop, inputting key operation features of the digital twin workshop, performing decision-making level fusion based on the decision-making model, decision-making optimization and decision-making method solution, and outputting a workshop production management and control decision. The method can provide support for digital twin workshop operation data governance analysis and production management and control accurate decision.

The invention provides a mutual inductor test data cloud edge cooperative processing method and device, and relates to the technical field of data processing, and the method comprises the steps: carrying out the time domain and frequency domain combined feature extraction of the original measurement data of a mutual inductor test through a multi-mode decoupling preprocessing model, and forming a data feature vector set, further generating a confidence label flow through state recognition and Bayesian inference, evaluating a prediction error, and realizing data classification screening and priority queue construction; the data are uploaded to a cloud end in a semantic compression and multi-resolution representation vector mode, so that the transmission efficiency is improved; carrying out deep modeling and model performance monitoring at the cloud, and if degradation is detected, returning edge original data to update the model; and finally, generating a scheduling weight factor according to the prediction error distribution graph, and dynamically optimizing an edge cloud task allocation proportion. According to the invention, the problem of low resource allocation efficiency caused by lack of a dynamic scheduling mechanism based on prediction errors and confidence driving in existing mutual inductor test data edge cloud cooperative processing can be solved.

The invention discloses an unmanned aerial vehicle autonomous navigation system based on a hierarchical reinforcement learning strategy. The unmanned aerial vehicle autonomous navigation system is suitable for a three-dimensional flight task in an unknown environment. The system comprises a state sensing module, a hierarchical strategy network module, a control execution module, a data classification module and a data playback module. The state sensing module extracts obstacle position information based on a deep neural network, and fuses the target, the obstacle position and the flight state to generate a state vector and a time sequence. The hierarchical strategy network adopts a high-layer DQN to generate a navigation intention, and a low-layer LSTM and PPO are combined to output a continuous control action; the control execution module adjusts the attitude of the unmanned aerial vehicle according to the instruction and performs closed-loop correction. The system introduces a double dynamic memory mechanism (DDM), improves strategy training efficiency and stability through experience classification and proportional sampling, and adopts a multi-target award function guide strategy to optimize convergence among task completion, obstacle avoidance safety and flight rationality. The system has good environmental adaptability and generalization ability, and is suitable for autonomous navigation tasks in complex scenes.

The invention relates to a method and a system for storing and backing up electric power data of a data-in-data station based on use popularity, and belongs to the technical field of data-in-data stations, and the method comprises the following steps: classifying electric power business data into hot data, temperature data and cold data based on preset access frequency, time window and data volume index; according to a classification result and a business importance degree, storing hot data in an SSD cloud disk or a memory database, storing temperature data in a distributed column database, and compressing cold data and then archiving the cold data to an object storage or a tape library; dynamically configuring differentiated backup strategies for different types of data; automatic scheduling of storage and backup is realized through a cloud platform component, and hot switching of master and slave databases, OSS archived data recovery by a DataWorks tool and a remote multi-copy recovery mechanism are supported; and migrating data in advance in combination with a historical access prediction popularity trend, and triggering cold data compression and archiving when storage resources exceed 80%. The method is suitable for power industry scenes, the storage cost is optimized, and the data calling efficiency is improved.

The invention provides a vehicle-mounted soil nutrient detection method and device based on soil spectral data classification, and relates to the technical field of soil detection.The method comprises the steps that soil near infrared spectral data of soil of a current site in a farmland area to be detected is collected through vehicle-mounted equipment; then obtaining first spectrum difference information and second spectrum difference information based on the soil near infrared spectrum data; further determining the similarity degree of the near infrared spectrum data of the soil; dividing the soil near infrared spectrum data into a plurality of different soil near infrared spectrum data subsets through a spectral clustering algorithm, and performing soil nutrient detection through a farmland soil nutrient detection model. According to the vehicle-mounted soil nutrient detection method based on soil spectral data classification, the total nitrogen content and the organic matter content of the soil can be accurately output in real time, and the detection effect of soil nutrient detection is improved.

The invention relates to the technical field of data sharing, and discloses a multi-department data sharing method and device, equipment and a storage medium. The method comprises the following steps: carrying out acquisition and standardized preprocessing on multi-department data required by enterprise project application to generate an intermediate data packet; establishing an event trigger distributed data control mechanism based on the intermediate data packet, calculating a dynamic trigger threshold, monitoring data change, and generating a target control data stream; performing template-free adaptive learning to generate an optimal data processing flow definition file; executing data classification, self-adaptive compression and increment processing at the edge computing node to generate an optimized data packet and metadata thereof; and inputting the optimized data packet and the metadata thereof into a SaaS cloud server central data warehouse to generate a project declaration data packet, thereby realizing consistency coordination and conflict automatic resolution of data of multiple departments, and realizing unified access and standardized processing of heterogeneous data sources.

The invention discloses a method and a system for automatically classifying heterogeneous data based on business deep learning, particularly relates to the technical field of data classification, and is used for solving the problems of limited classification accuracy and insufficient interpretability caused by cross-modal feature entanglement in the existing method. The method comprises the following steps: decoupling and separating an independent feature and a redundant feature vector through a modal implicit feature, generating a noise correction instruction based on orthogonality residual evaluation, carrying out cross-modal noise distribution consistency correction on the redundant feature, quantifying a gradient direction conflict rate in combination with a vector space projection relation, and dynamically adjusting a back propagation path. Fusion path switching is triggered through semantic matching degree and contribution degree balance monitoring, a self-adaptive classification decision boundary function is constructed, and high-precision classification of heterogeneous data and strong generalization adaptation of service scenes are achieved.

The embodiment of the invention discloses a medical data privacy protection method and a computer device. The method comprises the following steps: firstly, acquiring medical industry laws and regulations and target medical institution data asset information; building a logic relation graph containing data classification and security level according to laws and regulations, and importing the laws and regulations information into a first graph database to obtain a laws and regulations graph; extracting a logical relationship in the data asset information, constructing a data logical relationship graph containing a library table column structure, and importing the data logical relationship graph into a second graph database to obtain an asset graph; business process information is collected, and a data flow relation is identified to perfect an asset map; analyzing the name and content of the data assets to obtain a classification and grading analysis result; on the basis of the result, constructing a heterogeneous graph neural network model by using a graph attention network, establishing a mapping relationship between the two maps, and updating the asset map; and finally, carrying out privacy protection on the data assets according to the updated asset atlas. According to the method, the accuracy and efficiency of medical data classification and grading can be improved, and the privacy protection capability is enhanced.

The invention relates to the technical field of computers, and discloses a server cluster security control method combined with multi-level encryption strategy joint scheduling, which comprises the following steps: S1, carrying out sensitivity evaluation on calculation tasks and data in a cluster, and classifying the data into low-sensitivity data, medium-sensitivity data and high-sensitivity data according to a preset rule; s2, encrypting the low-sensitivity data by adopting a first encryption algorithm, encrypting the medium-sensitivity data by adopting a second encryption algorithm, and encrypting the high-sensitivity data by adopting a homomorphic encryption algorithm; and S3, based on a joint scheduling mechanism of reinforcement learning, calculating task requirements and data sensitivity information by acquiring the load of the cluster in real time. Through a multi-level encryption strategy, dynamic resource scheduling based on deep Q learning and a security protection mechanism combining a zero-trust architecture and deep learning, the problems that cluster resource utilization efficiency is low, data security is insufficient and security threats cannot be responded in real time are solved.

The invention provides a self-adaptive color depth optimization method for a liquid crystal display screen, which comprises the following steps of: acquiring manual adjustment operations of a user on brightness, contrast and color parameters of the liquid crystal display screen in different use scenes to form a user preference data set; according to the user preference data set, a data classification method is adopted to classify manual adjustment operations in different use scenes, and features of ambient light and content types are extracted to determine preference feature distribution of a user in a specific scene, and a personalized color preference model is constructed; preliminary calibration is carried out in combination with a dynamic balance algorithm, and display parameters are optimized through a continuous learning mechanism and secondary calibration. The problem that in the prior art, color adjustment of a liquid crystal display screen lacks individuation and scene self-adaption capacity is solved, continuous self-adaption of the display effect is achieved, manual intervention of a user is reduced, and the individuation degree and comfort degree of visual experience are improved.

The embodiment of the invention provides a hydropower station knowledge graph construction method and system, and belongs to the technical field of knowledge graphs. The method comprises the following steps: collecting multi-source heterogeneous data related to a hydropower station, and executing format standardization processing and semantic normalization processing to obtain basic data; performing data classification on the basic data to obtain a plurality of data sets; matching a corresponding extraction model based on each data set to respectively execute entity extraction, relation extraction and attribute extraction to obtain an extraction result set; screening out repeated extraction results to obtain an atlas construction data set; and executing knowledge graph construction of the ternary array based on the graph construction data set, and obtaining and outputting a hydropower station knowledge graph. According to the scheme, the problem of heterogeneous data integration is solved, the practicability and expandability of the atlas in application scenes such as intelligent question answering and operation monitoring are remarkably improved, and reliable data support and technical guarantee are provided for intelligent application in the field of hydropower stations.

A data classification method includes following steps. Text samples are obtained from a dataset. The text samples are converted into text embeddings in a semantic space. An outlier-inlier ranking of the text samples is generated based on an outlier detection algorithm according to distances between the text embeddings in the semantic space. Partial samples are selected from the text samples according to the outlier-inlier ranking. A manual input command is received to assign manual-input labels on the partial samples. A prompt message is generated according to the partial samples with the manual-input labels and unlabeled samples of the text samples. The prompt message is provided to a generative pre-trained transformer model for generating inlier-outlier prediction labels about the unlabeled samples.

The invention discloses a tensor depth semi-supervised learning method for high-dimensional small sample data classification. The tensor depth semi-supervised learning method comprises the steps of preprocessing original high-dimensional small sample data; constructing a deep neural network comprising a feature extraction module and a classifier module; constructing a second-order similarity matrix and a third-order similarity tensor based on the low-dimensional embedding representation obtained by pre-training; combining the second-order similarity matrix and the third-order similarity tensor to construct an objective function containing multi-order smooth constraints; then, taking the low-dimensional embedded representation obtained by pre-training as input, performing iterative optimization on the target function by adopting a gradient descent algorithm through a label propagation network formed by a full connection layer, and generating a pseudo label; and inputting the original high-dimensional small sample data, the low-dimensional embedded features and the pseudo labels into the deep neural network, iteratively updating the network in a semi-supervised mode until convergence, and outputting a final prediction result. According to the method, more accurate label propagation is realized, and the semi-supervised classification precision is improved.

The present application relates to the technical field of artificial intelligence, and in particular to a multi-modal human physiological data classification model and a training method therefor, a multi-modal human physiological data classification method, and a device, aiming to solve the classification problem in cases of missing multi-modal physiological data. The classification model of the present application comprises a multi‑headed self‑attention module, a normalization module, a fusion expert system and a decision-making module. The fusion expert system comprises four expert subsystems: an electroencephalogram expert subsystem, an electrocardiogram expert subsystem, an electrodermal activity expert subsystem and a multi-modal synchronization fusion expert subsystem. The multi-headed self-attention module is configured to perform feature extraction on multi-modal synchronization data; the normalization module is configured to generate normalized feature data on the basis of the extracted features; the four expert subsystems are configured to perform classification tasks on the basis of the normalized feature data corresponding to electroencephalogram data, electrocardiogram data, electrodermal activity data and multi-modal synchronization data, respectively; and the decision-making module is configured to calculate a final classification result on the basis of the four classification results. The present application can improve the accuracy of classification results in cases of missing multi-modal data.

The invention relates to the technical field of intelligent traffic systems, and discloses an expressway multi-source traffic data grading and classification processing system, which comprises a data acquisition standardization module for acquiring and preprocessing multi-source traffic data; the data source quality evaluation module is used for evaluating the credibility of the data source; the scene emergency degree calculation module is used for calculating the flow anomaly degree, the vehicle speed anomaly degree and the meteorological risk score and identifying an emergency event; the data grading module is used for calculating a comprehensive priority score of the data and obtaining a grading data set by adopting a threshold segmentation method; the data classification module is used for carrying out self-adaptive classification on the data; the data fusion module identifies the same traffic parameter, performs conflict detection and performs fusion processing on conflict data; the result output module is used for obtaining a processing result by adopting a grading and classification output and quality feedback mechanism; according to the invention, intelligent refined processing of the highway multi-source traffic data is realized, and the emergency response speed and the data processing accuracy are improved.

The invention relates to the technical field of section design, and discloses a shipyard steel bent frame column and beam section design simulation method and system. The method comprises the following steps: classifying and storing the historical data of the shipyard steel bent, establishing an experience library and extracting initial section parameters; inputting a parametric modeling system to generate a geometric model and a load condition; obtaining an internal force envelope value based on finite element analysis; obtaining a stress ratio and a displacement ratio according to standard combination and checking calculation; using an NSGA-II algorithm to optimize section parameters; and modeling and marking based on an optimization result, and generating a structure optimization scheme and a design drawing. The problems that in the prior art, preliminary section type selection of a steel bent frame structure lacks system optimization, a large amount of manual adjustment is needed in the design process, the overall design efficiency is low, and the material utilization rate is low are effectively solved, and automatic, refined and multi-target comprehensive optimization of steel bent frame column and beam section design is achieved.