3375 results about "Information data" patented technology

The present application relates to the technical field of power digital twin, and discloses a digital twin system for a power grid. The system comprises: a data access module, which accesses multi-source heterogeneous data of a power grid; a modeling module, which is used for receiving the data output by the data access module and establishing digital twin models in multiple service scenes; a fusion module, which is used for performing association and fusion of the digital twin models in the multiple service scenes; a computing module, which is used for carrying out multi-scene collaborative simulation and derivation by using the digital twin models; and an optimization module, which is used for optimizing the digital twin models, wherein the multi-source heterogeneous data comprises real-time monitoring data, historical operation data, meteorological environment data, device model data, and geographic information data. The problem in the prior art of low fusion efficiency of multi-source heterogeneous data of a power grid is solved. A standardized data access interface is constructed, and association analysis, knowledge graph technology, etc. are applied, so that seamless integration of the multi-source heterogeneous data of the power grid is achieved, thereby improving the data fusion efficiency.

The invention discloses a geographic information big data-oriented multi-source data processing system, and relates to the technical field of data acquisition and sensors, and the system comprises a data acquisition module which accesses a remote sensing satellite, an unmanned aerial vehicle, an Internet of Things sensor and social media in real time through a multi-source heterogeneous interface, and carries out the adaptive analysis of a data format and metadata marking; the distributed storage module is used for performing partition storage on the geographic information data based on a space-time database and an object storage architecture, and establishing dynamic space-time index and version control; the data fusion module is used for realizing coordinate system conversion, time sequence calibration and semantic knowledge graph matching based on a multi-source data alignment method of dynamic weight distribution; and an intelligent analysis module and a security control module. According to the method, core pain points such as data splitting, low storage efficiency, extensive analysis and compliance risks in the geographic information field are systematically solved, and a full-stack type technical base is provided for scenes such as smart cities, emergency disaster relief and environment monitoring.

The invention relates to the technical field of management and control systems, in particular to a digital intelligence system applied to collaborative management and control of water-power engineering construction participating units, which comprises a project data management module covering water-power engineering construction full life cycle management, extracting multi-source information data from various subsystems, and constructing a project view and a project management information base; the project risk analysis module is used for constructing an intelligent risk analysis model for risk analysis and generating a dynamic evaluation result, a graded early warning notification and an auxiliary decision scheme; the project collaborative management module is used for processing graded early warning notification and auxiliary decision-making schemes by using a multi-layer perceptron model, and generating finalizing service achievements and management process records; and the project document management module is used for performing compliance automatic checking and processing on the electronic documents needing to be archived, and dynamically updating and optimizing the project management information base. Through the closed-loop management and control system, the cooperative management and control efficiency of water-power engineering construction participation units is improved.

The invention discloses a person and post matching method, system and equipment based on Transform and a medium, belongs to the technical field of cross application of natural language processing and human resource management, and aims to solve the technical problem of how to reduce subjective errors in a person and post matching process, improve matching accuracy, fairness and objectivity and improve manual matching efficiency at the same time. According to the technical scheme, the method comprises the following steps: data collection: collecting post information data and job seeker resume data from a plurality of data sources, and cleaning and sorting the collected data to obtain a post description text and a resume text; text preprocessing: preprocessing the acquired and sorted post description text and resume text to obtain the preprocessed post description text and resume text; the method comprises the following steps of: fusing a Transform model and a BERT model; constructing an encoder-decoder structure based on the Transform model; optimizing the model; multi-label text classification: constructing a multi-label classification system; and outputting and evaluating a matching result.

The invention discloses an algorithm for automatically extracting and identifying a photovoltaic abnormal fault, and the algorithm comprises the steps: S1, importing photovoltaic power station data, collecting and classifying power station design information data, power station historical working data and power station historical meteorological data in a database, so as to obtain first data; s2, photovoltaic power station data preprocessing is carried out, integrity and rationality checking is carried out on first data, and type conversion and normalization processing are carried out on the checked first data to obtain actual photovoltaic system operation condition data; and S3, identifying the fault of each link of the photovoltaic system. According to the algorithm for automatically extracting and identifying the photovoltaic abnormal faults, an abnormal fault sequence is rapidly and accurately extracted through data acquisition and analysis and intelligent fault prediction and diagnosis, and digital, intelligent, remote and unmanned operation and maintenance of a photovoltaic power station are achieved through accurate positioning and calculation judgment.

The invention discloses an evaluation method based on water ecological environment monitoring, and relates to the technical field of water ecological environment evaluation. Comprising the steps of data collection: obtaining a river basic information data set, a monitoring equipment information data set and a historical monitoring information data set, and building a cloud server to store the river basic information data set, the monitoring equipment information data set and the historical monitoring information data set. According to the method, each region is accurately monitored through region division of river landforms and a targeted monitoring method, the timeliness of monitoring data is ensured, the water quality condition can be evaluated in real time and the water quality of the corresponding region under unique weather can be evaluated by acquiring a region monitoring data set and a weather information data set in real time to cooperate with an evaluation model, and the water quality evaluation accuracy is improved. In addition, weather changes can be monitored and early warned, timely support is provided for dealing with emergencies, and by summarizing the method, related departments can formulate more reasonable treatment schemes, the water quality management efficiency and effect are improved, and the water quality problem is found and solved in time.

The invention discloses a runoff prediction method and terminal based on a space-time heterogeneous graph neural network, and the method comprises the steps: obtaining hydrological time series data and space station information data collected by each station, and carrying out the preprocessing of the hydrological time series data and the space station information data; performing weighted fusion according to the features of the preprocessed hydrological time series data and the space station information data, generating a feature embedding vector representing a global variable time-space relationship through a graph attention network, and inputting the feature embedding vector into a full connection layer to obtain an output multi-station runoff prediction sequence. Constructing a space-time heterogeneous graph flood forecasting model; performing model parameter optimization on the space-time heterogeneous graph flood forecasting model; and carrying out runoff prediction on the hydrological time sequence data and the space station information data which are received in real time according to the optimized space-time heterogeneous graph flood forecasting model. In this way, hydrological space-time correlation characteristics can be effectively captured in forecasting, so that the accuracy and reliability of forecasting are improved.

The invention relates to the technical field of material verification management, and discloses a government affair material intelligent verification method and system based on machine learning, and the method comprises the steps: obtaining the text information data and verification rules of historical materials; multi-modal feature extraction is carried out; establishing a dynamic verification rule knowledge graph, and verifying the multi-modal features and the target feature points of the text segments to generate a primary verification strategy; performing deep verification processing on the material text fragment to generate a secondary verification strategy; performing third-time verification on the material text fragment subjected to the second-time verification by triggering cross-page feature matching to realize cross-page text fragment association and multi-modal feature fitting so as to generate a third-time verification strategy; performing iterative comparison on the results of the three rounds of verification to generate a credibility score; and the comparison result is adjusted according to the credibility score in combination with the verification risk abnormity assessment, so that a final verification decision strategy can be obtained, and efficient, accurate, dynamic and comprehensive intelligent verification can be realized.

Disclosed is a cloud-native application programming interface (API) recommendation method fusing data augmentation and contrastive learning. Service information is included on the basis of a service information double-graph structure, and a mutual attention mechanism is designed to compute an importance degree of each layer of information. A data optimization method for sequence information based on functional similarity and a computation method for similarity between services based on two parts of information are provided; on this basis, data of a service invocation sequence is augmented with the idea of contrastive learning to form an augmented sequence pair; a computational contrastive loss function is combined with a pair-wise recommendation loss function to optimize an overall model, thereby improving the effect of a service recommendation model; and according to a feature embedding representation result of a service, pair-wise recommendation scores are computed to complete service recommendation.

The invention discloses a text keyword desensitization method, system and equipment based on a large model and a medium, belongs to the technical field of large model and natural language processing, and aims to solve the technical problem of how to automatically identify and desensitize sensitive information, improve the accuracy of sensitive information identification and further improve the text processing efficiency. According to the technical scheme, the method comprises the steps of data collection; preprocessing the data; marking sensitive information; segmenting the data; selecting a basic model; finely adjusting the model; training the fine-tuned large language model, and obtaining and storing the trained fine-tuned large language model; preprocessing the to-be-processed text data; detecting sensitive information, formulating a desensitization strategy and implementing desensitization processing, performing desensitization verification on the text after desensitization processing, and optimizing and improving the fine-tuned large language model according to a verification result; and packaging the optimized and improved fine-tuned large language model into an API (Application Program Interface) service for an application program to call.

The invention relates to the technical field of human resource information processing, in particular to a position resume intelligent matching method and system based on multi-dimensional analysis, and the system comprises a data collection module, a data processing module and a data matching module, and the data collection module is used for obtaining resume information data of job seekers and recruitment information data of enterprises; and the data processing module is used for extracting information dimensions in the resume information data and the recruitment information data. According to the method, all the information dimension scores in the hard information data and the soft information data are quantitatively calculated, and the matching score is calculated after the scores are combined, so that the matching degree of the job seekers and the positions can be more comprehensively evaluated, the demand difference of the job seekers is automatically identified according to the attention degree of the job seekers on the information dimensions, and the job seekers can be more accurately evaluated. And the weight coefficient is updated, so that the personalized demand of the job seeker is accurately met, the accurate matching rigidity requirement is provided for the job seeker and the enterprise, and a flexible secondary selection scheme can also be provided for the job seeker and the enterprise.

The invention relates to the technical field of unmanned aerial vehicle-unmanned ship data fusion, and discloses an unmanned aerial vehicle-unmanned ship multi-source heterogeneous big data fusion system and method, and the system comprises a task scheduling module which is used for executing the path planning and task distribution of operation tasks; the control and data acquisition module is used for controlling the unmanned aerial vehicle and the unmanned ship to execute a synchronous measurement task; the space-time reference unification module is used for performing dynamic synchronization and consistency alignment processing; the multi-source data fusion module is used for carrying out fusion processing on the aligned heterogeneous sensing information data; the self-adaptive robust enhancement module is used for monitoring the noise level and the change condition of the signal-to-noise ratio of the sensor in the fusion process; and the intelligent analysis and visualization module is used for executing environment modeling, target identification and geographic information expression processing. According to the method, the fusion weight self-adjusting mechanism driven by the maximum entropy is introduced, so that the internal structure can be intelligently adjusted when the data quality fluctuates. According to the technical effect, the target that the weight is dynamically adjusted along with signal noise is achieved.

The invention provides a servo motor real-time adaptive control method and system based on artificial intelligence, and the method comprises the steps: injecting a tiny excitation signal into a servo motor, collecting temperature and electromagnetic response data under different working conditions, and constructing a multi-dimensional information data set; performing real-time reasoning on the relationship between the temperature gradient and the magnetic path characteristics by using deep transfer learning to obtain multi-dimensional parameter deviation vectors such as stator flux linkage unbalance degree, permanent magnet flux weakening degree and torque coefficient change; generating a multi-scale compensation strategy through a physical enhancement reinforcement learning method and the multi-dimensional parameter deviation vector; and carrying out Lie group transformation processing on the control law under a differential geometric framework, and realizing accurate compensation on the temperature gradient effect through a dual-channel-dual-time scale framework. According to the method, heterogeneous sensing and multi-dimensional compensation strategies are adopted, the problem that the control precision of a traditional method is insufficient under the complex temperature gradient condition is solved, and the high-precision motion control performance is maintained under the dynamic working condition that the temperature distribution of the motor is rapidly changed.

The invention provides a lightweight real-time semantic segmentation method for a three-dimensional Gaussian scene, and belongs to the field of computer vision and three-dimensional scene understanding. The process comprises the following steps: S1, data preparation: acquiring multi-modal information data including point cloud, images and poses, and preprocessing the data; s2, carrying out two-dimensional semantic segmentation, and generating a pixel-level semantic segmentation map and a semantic tag for the image by utilizing PP-LiteSeg; s3, performing three-dimensional scene modeling and semantic projection, fusing multi-modal information to complete three-dimensional Gaussian scene reconstruction, and projecting two-dimensional semantics to a Gaussian function to form preliminary Gaussian function semantic features; and S4, semantic optimization and segmentation output: considering image difference, semantic difference and smoothness, establishing a loss function, optimizing semantic features and outputting a segmentation result. According to the method, efficient, real-time and accurate three-dimensional Gaussian semantic segmentation work can be completed, and the method is suitable for scene understanding tasks of light-weight and resource-limited equipment.

The invention relates to the technical field of equipment health assessment, in particular to a power plant equipment health assessment model construction method and system. The method comprises the following steps: acquiring station information data of power plant equipment; site equipment data transmission channel configuration is carried out on the power plant equipment site information data, and a site equipment data transmission channel is generated; carrying out information integration on the power plant equipment station information data by utilizing the station equipment data transmission channel to generate a power plant equipment operation data integration platform; performing power plant equipment operation monitoring on the power plant equipment operation data integration platform to obtain power plant equipment operation monitoring data; and performing equipment multi-working-condition trend fluctuation calculation on the power plant equipment operation monitoring data to obtain equipment working condition trend fluctuation data. Through data integration, refined working condition analysis, unsteady state abnormal state transition analysis and dynamic loss assessment, the precision of the power plant equipment health assessment model is improved.

The invention relates to the technical field of artificial intelligence and the field of financial science and technology, and discloses an information processing method based on sentiment analysis, and the method comprises the steps: collecting data and information which comprise text data, user behavior data and event information; performing natural language processing and sentiment analysis on the text data, and identifying sentiment features of the text data; analyzing the user behavior data to determine a behavior pattern; analyzing the event information to determine an event change trend; generating an emotional state index based on the emotional characteristics, the behavior pattern and the event change trend; monitoring the online platform to obtain user emotion feedback; comprehensively analyzing the emotion state index and the user emotion feedback to generate an emotion analysis result; and generating target output content based on the emotion analysis result. Through combination of emotion analysis, behavior data and event change trends, the emotion state of the user can be captured in real time, decision suggestions are dynamically adjusted, and market or environment changes are effectively coped with.

Disclosed in embodiments of the present application are a method and apparatus for acquiring lineage data, a computer device, and a readable storage medium. The method comprises: obtaining lineage information of a real-time generation task and data information of a dependency relationship between the real-time generation task and a predecessor task thereof, the lineage information comprising at least two among identification information of the real-time generation task, a data input database table, a data output database table, an output relationship, and an input relationship, and the data information comprising at least two among identification information of the predecessor task, status, and a first run time; writing the lineage information and the data information into message middleware, and processing the lineage information and the data information in the message middleware, and obtaining a lineage map and an index map of the real-time generation task; and uploading the lineage map and the index map to a graph database and an index database, respectively. In the present application, comprehensive acquisition of lineage data can be implemented, so that impact analysis can be carried out on a data table in a lineage map.

The invention discloses an intelligent building construction operation management method based on big data, and belongs to the technical field of building construction management. Comprising the following steps: step 1, real-time data acquisition of construction site personnel, equipment and environment is realized, and a dynamic information database is established; 2, preprocessing and feature extraction are carried out on the collected multi-source heterogeneous data, and fusion and intelligent analysis of real-time data are achieved; step 3, accurately identifying risk factors and implementing refined early warning and intelligent intervention aiming at a single risk; 4, construction resource configuration is dynamically optimized, the resource utilization efficiency is improved, and meanwhile the cost and the safety risk are reduced; step 5, constructing a block chain data sharing mechanism, and realizing data tracing and intelligent management of a construction full life cycle; and step 6, forming a self-adaptive closed-loop feedback mechanism, and promoting intelligent transformation of construction management from passive response to active prediction.

The invention provides an intelligent boxing method and system. The intelligent boxing method comprises the steps of obtaining boxing task information and generating a boxing scheme. The boxing task information comprises the size, the weight, the stacking limitation, the number and the priority of each cargo, and the size, the bearing capacity and the occupation state of each loading unit. And under the condition that the size, bearing and stacking constraint conditions are met, a plurality of candidate boxing combinations are generated, the loading utilization rate, the residual space distribution and the shape matching degree are evaluated based on a preset optimization target, and an optimal scheme is selected. The system comprises an input terminal, a task information receiving module, a data storage module, a task and scheme storage module, an operation processing module and a final boxing scheme generation module. According to the method, multi-dimensional constraint matching and weight balance are considered, and space waste and transportation risks are avoided; the maximum greedy algorithm and the adaptive large neighborhood search optimization are combined to realize the combination of automation and experience. The method can be expanded to batch task scheduling and grouping sequencing, so that the calculation complexity is reduced, and the overall boxing and transportation efficiency is improved.

The invention discloses a traffic signal controller adaptive control method and device, a storage medium and an electronic device. The method comprises the following steps: performing multi-source data fusion and spatial-temporal position sensing graph convolution initialization on real-time traffic monitoring, historical traffic data, meteorological data and urban activity information data; a space-time position sensing graph convolutional network is adopted, geographical and time information is converted into mathematical vectors to construct a dynamic traffic flow prediction model, space-time relevance among traffic nodes is analyzed through the graph convolutional network, and the future traffic flow state is predicted; performing deep reinforcement learning and signal optimization; self-adaptive feedback and system iteration updating are carried out; monitoring and evaluating system performance; the problems that an existing traffic signal controller control method lacks real-time data comprehensive utilization capacity, dynamic traffic condition prediction is insufficient, and signal control strategy optimization is insufficient are solved.

The invention relates to the technical field of pressure control, in particular to a dynamic pressure control system and method of a strapping machine. The method comprises the following steps: acquiring information data of an object to be bundled; performing three-dimensional modeling on the to-be-bundled object information data to generate three-dimensional modeling data of the to-be-bundled object; binding mode confirmation is conducted on the binding machine according to the three-dimensional modeling data of the to-be-bound object, binding mode data are obtained, and the binding mode data comprise a single-layer binding mode and a multi-layer binding mode; initial strapping pressure setting is carried out on the information data of the to-be-strapped object based on the single-layer strapping mode, and single-layer strapping initial pressure data is obtained; and carrying out object deformation binding pressure adjustment on the single-layer binding initial pressure data to generate single-layer binding pressure adjustment data. Through three-dimensional modeling, dynamic pressure adjustment and a feedback control mechanism, the intelligence and precision of dynamic pressure control of the strapping machine are improved.

The invention provides a geographic information processing method and device, equipment and a storage medium, and relates to the technical field of GIS data processing, the method comprises the following steps: obtaining vector geographic information data of multiple formats, the vector geographic information data at least comprising a vector layer containing land class attributes; processing the vector of the vector layer through a data editing processing tool to obtain a target vector layer; obtaining an intersection region obtained after the plurality of target vector layers are subjected to intersection operation, and taking the intersection region as a first result layer; according to the layer attribute of the first result layer, performing classified rendering on the first result layer to obtain a second result layer; and performing map finishing on the second result layer to obtain a target map file. The technical effects of the application are that an efficient and intelligent solution is provided, the requirements of modern geographic information processing are met, and powerful support is provided for spatial decision and analysis of each industry.

The embodiment of the invention discloses a power transmission line path planning method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining geographic information data of a power transmission line path planning region, carrying out the preprocessing of the geographic information data through a geographic information system, and constructing a grid cost surface considering influence factors; constructing an optimization model based on the grid cost surface according to the requirement of power transmission line path planning; solving the optimization model by using a heuristic search algorithm to obtain a preliminary path; further optimization processing is carried out based on the preliminary path, unnecessary corners and redundant paths are eliminated, and a target planning path is obtained; according to the method, a mathematical optimization model and a corresponding solution algorithm can be introduced, the optimal line path can be automatically generated on the basis of comprehensively considering various factors, the dependence on artificial experience is reduced, and the objectivity and scientificity of planning are improved, so that the efficiency and accuracy of high-voltage transmission line planning are remarkably improved.

The disclosure relates to systems, methods, and computer-readable media for an artificial intelligence (AI) conversational agent configured for enhanced user interaction in multi-channel contact centers. An example method includes receiving a query regarding a particular property of an asset during a real-time conversation, classifying an intent of the query into a predefined category, triggering, based on the classifying of the intent, a finetuned large language model for the predefined category to answer the query, generating, by the finetuned large language model, a response to the query based on at least one of an information data model and a knowledge graph data store, and outputting the response to the query.

The invention provides a wound surface grading model construction method and a wound surface self-analysis system, and the method comprises the steps: processing an obtained wound surface image, clinical text description data and structured background information data through a specific feature extraction strategy, and obtaining an image feature vector; clinical text description data and structured background information data are subjected to cleaning, word segmentation, entity labeling and other operations, a wound semantic evolution graph is constructed, related vectors are fused to obtain text embedding, wound background context vectors are generated according to the structured background information data, dynamic weighting, splicing and fusion are performed on image and text feature vectors, and the image and the text feature vectors are subjected to image fusion. According to the method, the fusion feature vector is obtained, the corresponding grading model is constructed according to the fusion feature vector, more accurate, comprehensive and explainable grading evaluation can be performed on different types of wounds, and a self-analysis system designed for the constructed model can quickly and accurately judge the wound image condition shot by a user.

The invention relates to the technical field of smart city data visualization, in particular to a visual smart city display method, which comprises the following steps: S1, collecting traffic flow data, energy consumption data, environment monitoring data and city three-dimensional geographic information data in real time; s2, constructing a reference three-dimensional model with a space coordinate system; s3, mapping the data into a dynamic data layer with a timestamp; s4, superposing the dynamic data layer to the reference three-dimensional model according to space-time coordinates to generate a fused dynamic scene; s5, receiving deduction parameters input by a user, and calculating a corresponding evolution path in real time; and S6, converting the evolution path into a dynamic visualization effect, and outputting the dynamic visualization effect to a display terminal. According to the invention, through integrated processing of multi-source dynamic data fusion, space-time path deduction and visual output, continuous expression and dynamic evolution display of the operation state of the smart city are realized.

The invention relates to the technical field of micro-grid energy dispatching, in particular to a micro-grid energy dispatching carbon emission reduction system based on reinforcement learning dual-objective optimization. The data acquisition module is used for acquiring information such as equipment energy consumption data, environmental parameters and carbon emission coefficients in the micro-grid in real time; the data processing module is used for cleaning acquired data, extracting features and performing standardization processing; the intelligent scheduling module is used for extracting information features and a deep reinforcement learning algorithm, optimizing by using a model to obtain an optimization strategy, and forming a scheduling instruction set; the execution feedback module is used for dynamically scheduling the operation state of equipment in the micro-grid according to the scheduling instruction set; the man-machine interaction module provides a user interface and is used for displaying the carbon quota use condition and the system operation state and allowing a user to input an adjustment demand; compared with the prior art, the energy system of the park is used as a unified network, mutual influence of multiple systems is considered, accurate scheduling is achieved, environmental protection and high efficiency are achieved, and the operation cost is reduced.

The invention discloses a project spatio-temporal information management and control method based on a BIM technology, and the method specifically comprises the following steps: 1, building a BIM collaborative public data environment, and enabling an engineering project decomposition structure coding standard to be analyzed and built in the BIM collaborative public data environment; step 2, establishing a plan information model, inputting budget quota data and engineering quantity list data into the BIM collaborative public data environment established in the step 1, establishing a mapping relationship among quota list items, engineering quantity list items and an engineering project decomposition structure, and determining unique codes of the data; step 3, establishing an execution information model, inputting spatio-temporal information data of the real world and model information data of the virtual world into a BIM collaborative public data environment, establishing a mapping relationship with a plan information model by relying on an engineering project decomposition structure code, and multiplexing the unique code determined in the step 2; 4, establishing a collaborative linkage relationship between the information model and information data, and realizing plan and execution difference comparative analysis based on model definition by means of data fusion and visualization service of a graphic engine; and step five, based on a three-dimensional visualization analysis result in the step four, project cost, progress, quality and safety control is carried out. The method is suitable for traffic infrastructure project management and control.

The invention discloses a ship desulfurization system reliability evaluation method and system based on multi-source information fusion, and the method comprises the steps: collecting multi-source information data of a hybrid desulfurization system, and carrying out the self-adaptive preprocessing; generating a fusion feature vector; constructing a dynamic Bayesian network based on multi-source fusion features, performing real-time reasoning by adopting data-driven transition probability learning and particle filtering, describing transient behaviors of system state evolution and mode switching, and performing dynamic multi-state reliability modeling; a fault mode is automatically extracted, and data-driven systematic risks are identified and quantitatively analyzed; a multi-resolution digital twinborn architecture is constructed, dynamic simulation prediction is carried out, a self-adaptive updating mechanism is adopted to keep the model synchronous with a physical system, and a virtual verification environment for reliability evaluation is provided; according to the method, an intelligent decision optimization system is constructed, self-adaptive generation and dynamic adjustment of a maintenance strategy are realized, closed-loop feedback is carried out, and the accuracy of reliability evaluation of the hybrid desulfurization system is improved.

The invention discloses an information security management method and system for enterprise customers, and relates to the technical field of information security, and the method comprises the steps: collecting equipment identification information data and operation behavior data, carrying out the security scoring of the equipment identification information data and the operation behavior data through a security baseline model, generating a security level, and dividing a security control region. An access permission and a data encryption strategy are configured; establishing a dynamic session channel for data transmission of the security control area, and performing key updating according to a preset time period; and monitoring data transmission characteristics of the dynamic session channel, triggering multi-factor identity authentication, and recording an audit log. According to the method, an accurate quantitative evaluation mechanism of equipment and user behaviors is established through the security baseline model, and comprehensive perception of security risks is realized; the differentiated management and control region division based on the security level enables the security resource configuration to be finer and more intelligent; and the multi-factor identity authentication and the audit log record cooperate with each other, so that the reliability of identity authentication is improved.