148 results about "Information dynamics" patented technology

The invention relates to the field of human resource management, and discloses a tobacco enterprise human resource management auxiliary calibration method based on big data, and the method comprises the steps: obtaining multi-source heterogeneous data related to enterprise internal human resources, and constructing a structured human resource data model in combination with a data standardization processing mechanism and an abnormality elimination strategy; post portrait modeling is carried out on the structured human resource data model, a capability dimension nesting analysis method is introduced, key capability factors and weight distribution required by each post are extracted, and a post capability demand graph is constructed; based on the post capability demand map, fusing the staff portraits and the historical job data, and identifying the deviation between posts and the staff through a multi-dimensional feature matching algorithm to form a preliminary calibration suggestion set; and a dynamic service association analysis method is introduced, and key matching parameters in the preliminary calibration suggestion set are dynamically corrected in combination with latest service demand data and real-time task assignment information. The method has the advantage of improving the management efficiency.

The systems and methods disclosed herein enable dynamic selection of a routing model for generation of an output in response to a provided input (e.g., a prompt for a large-language model). Based on the selected routing model, the data generation platform can evaluate the input and / or other suitable system parameters (e.g., system resource usage) to determine a suitable model for processing the provided input. For example, the routing model can determine a technical application associated with the input and dynamically determine to modify the input prior to generation of the output based on system resource measurement values and / or other suitable information, thereby conferring efficiency, security, and accuracy benefits while preserving system resilience.

The invention relates to the technical field of digital engineering, and particularly provides a BIM-based engineering construction dynamic three-dimensional visual management method and system, and the method comprises the steps: collecting construction multi-dimensional data, carrying out the correlation mapping with an initial BIM model component after preprocessing, and forming a structured construction element data set; dynamically reconstructing an initial BIM model based on the data set, generating a three-dimensional dynamic twinborn body, and dynamically displaying information such as superposition progress, quality, resources and environment; a prediction analysis model is called to generate progress prediction, quality early warning and resource optimization suggestions, and a final scheme is determined after visual simulation verification; and updating the data set and the twin according to an execution result, and iteratively optimizing the prediction model. According to the scheme, dynamic integration and visual management of construction total elements are achieved, the timeliness, accuracy and intelligent level of construction management are effectively improved through intelligent prediction and closed-loop optimization, the construction efficiency is improved, and the management cost is reduced.

The invention relates to the technical field of knowledge maps, and discloses a knowledge map completion method based on semantic-structure multi-level fusion. According to the technical scheme, for a training set positive example triple, a structured rationality score and a semantic correlation score are fused to screen high-quality training samples; in order to complement a query search relation path, screening a guiding path through a uniqueness index; integrating the query text, the entity description, the neighbor facts and the guide path to construct an enhanced input prompt; dynamic structure embedding is generated by using a relational graph convolutional network, the dynamic structure is mapped by an adapter module and then injected into a large language model, and completion prediction is completed by combining a fusion input fine tuning model. According to the method, through multi-level fusion of semantics and structural information, intelligent sample screening, prompt enhancement construction and structural information dynamic injection are achieved, the accuracy, reasoning ability and training efficiency of a large language model in a knowledge graph completion task are effectively improved, and the method is especially excellent in performance in complex relation reasoning and inductive completion scenes.

The invention discloses a flood control and emergency rescue plan generation method, which comprises the steps of S1, collecting flood control and emergency rescue original data, performing preprocessing and feature extraction, and labeling and confirming extracted feature data to serve as a training set; s2, performing LoRA fine tuning of a low-rank decomposition matrix on the basis of a pre-trained large language model, and performing large language model training operation through the training set; s3, constructing a flood control and emergency rescue field ontology model, learning distributed representation of the knowledge graph by adopting a graph neural network technology, and dynamically updating the knowledge graph and executing reasoning query according to real-time dangerous case information by establishing a dynamic reasoning mechanism; and S4, constructing a multi-modal information fusion framework which is used for carrying out information fusion processing on the knowledge graph reasoning result and then guiding the big language model to generate an emergency plan as a control signal. According to the method, the technical bottlenecks of low plan generation quality, difficulty in knowledge updating, insufficient multi-source information fusion and the like of a traditional method in a complex dangerous case scene are effectively solved.

The invention belongs to the technical field of engineering data processing, and particularly relates to a digital twinning-based engineering completion delivery data information extraction method, which comprises the following steps of: acquiring completion site entity data, constructing a digital twinning body of building information model and live-action three-dimensional fusion, and fusing semantic tag information based on an improved ICP (Inductively Coupled Plasma) algorithm. Performing space coordinate alignment and attribute association on the digital twinborn body to generate a completion information dynamic twinborn mapping map; a multi-level rule inference engine is used for extracting information items conforming to delivery standards, a dynamic risk propagation model is used for simulating the influence of data deviation on a delivery result through multi-node consensus verification data of a construction party, a construction party and a supervisor, and information extraction weight parameters are corrected in real time; and dynamically generating a delivery data packet and a visual auditing report and synchronously updating the digital twin archive library in combination with engineering change records and acceptance specifications. Therefore, the problems of incapability of capturing engineering parameter fluctuation, poor transmission efficiency and the like in the prior art are solved.

The invention discloses an enterprise financial risk determination method and a related device, and relates to the technical field of artificial intelligence. Six dimensions of mobility, credit, market, operation, industry macroscopic and public opinion reputation are respectively processed through six special and precise agents including a mobility risk analysis agent, a credit risk analysis agent, a market risk analysis agent, an operation risk agent, an industry macroscopic risk analysis agent and a public opinion and reputation risk analysis agent; and full-quadrant scanning of enterprise financial risks is realized. The cross validation coordinator and the dynamic cooperation engine form a double-wheel drive, and the cross validation coordinator can obtain a cooperation agent identifier set, a cooperation task type and cooperation configuration information; the dynamic collaboration engine dispatches multi-agent parallel collaboration in real time based on the collaboration agent identifier set, the collaboration task type and the collaboration configuration information, information islands are eliminated, and misjudgment and missing report are remarkably reduced. Therefore, accurate enterprise financial risks can be obtained.

The invention relates to the technical field of maritime navigation and intelligent shipping, in particular to an electronic chart shipping relation network construction and dynamic updating method and system based on a chart database, and the method comprises the steps: obtaining electronic chart static data and shipping dynamic information, and carrying out standardized preprocessing to unify a space reference and an attribute format; defining node types and edge relation types of channel space elements based on the graph database, and constructing a shipping relation network; shipping dynamic information is accessed in real time, and the weight and the attribute of the side relation are dynamically updated to reflect the real-time state of the navigation channel; and receiving a user path planning query request, executing a path planning algorithm fused with the navigation constraint condition based on the updated network, and generating and outputting an optimal route, thereby effectively solving the problems of weak data association, difficult cross-scale fusion and poor dynamic adaptability of a traditional electronic chart, realizing intelligent management and accurate path planning of a channel relationship, and improving the user experience. And the shipping efficiency and safety are improved.

The invention belongs to the field of intelligent workshop scheduling, and particularly discloses a large model enabled workshop scheduling end-to-end self-decision engine, method and device. According to the method, the large model is positioned as a core decision maker instead of a traditional auxiliary tool, so that the powerful capabilities of the large model in the aspects of natural language understanding, context learning, complex logical reasoning and content generation are fully utilized; according to workshop real-time state information, a dynamically changing workpiece pool, a scheduling target input by a user through a natural language and emergency description, an autonomous and end-to-end scheduling decision can be directly carried out, so that the rapid response capability and intelligent processing level of a scheduling system to dynamic events are improved, and the scheduling efficiency is improved. And the application threshold is reduced through natural language interaction, the dependence on explicit programming and rule customization is reduced, a low-code development paradigm is developed, and the method has a far-sighted decision potential exceeding a traditional heuristic rule.

The embodiment of the invention provides a data processing method and system, electronic equipment, a storage medium and a program product. According to one method, after a first article classification identifier corresponding to a target article is determined according to multi-modal information of the target article, a tax rule suitable for transportation of the target article is obtained from a knowledge graph based on the first article classification identifier; the knowledge graph is dynamically constructed on the basis of import and export article control information, obtained in real time, of each region, and the knowledge graph contains a plurality of nodes and node relations; the nodes have two types, namely article classification identifiers and tax rules; the node relationship reflects the association relationship between the article classification identifier and the tax rule. And further, determining a tax value of the target article based on the applicable tax rule, thereby automatically generating a customs declaration file of the target article according to the tax value. Therefore, automatic generation of the customs declaration file can be realized, so that the customs declaration efficiency is improved, and errors and tedious processes of manual operation are reduced.

The invention relates to the technical field of industrial data management, in particular to an energy network dynamic game optimization method under a carbon quota constraint, and the method is suitable for strategy linkage and response relationships generated by different management subjects due to carbon resource allocation and scheduling policy adjustment under a carbon quota system. According to the method, various local policy adjustment events are simulated, the response influence of the events on other management subjects is analyzed, and the possible management conflict risk is recognized; on the basis, a strategy buffer area and a time sequence transition mechanism are introduced, and transition path planning from a local strategy to global coordination is achieved; and the management strategy is dynamically adjusted in combination with feedback information in the execution process. According to the method, the potential instability region can be dynamically identified in the operation process, local reconstruction and iterative optimization are implemented in real time, and the compliance and stability of an energy system in a complex carbon quota management environment are guaranteed.

The invention provides a novel traffic information dynamic time sequence diagram topological structure, which relates to the technical field of dynamic time sequence diagrams, and comprises the following steps: configuring a namespace and time granularity system of a traffic information dynamic time sequence diagram, setting a global timestamp rule and a propulsion strategy, defining an information identification bit structure, and setting a visibility and consistency control strategy. And a unified time window scheduling and snapshot life cycle management mechanism is configured. A dynamic extensible time sequence naming system is formed by establishing a unified naming space, time granularity delta t and a global timestamp rule, windowed organization and snapshot chain management of traffic information are achieved, the structure can automatically advance snapshot in a time driving mode and a data driving mode, time sequence consistency and real-time performance are guaranteed, and the time sequence naming efficiency is improved. The problems that node time management is disordered and data cannot be traced in a traditional traffic map model are solved.

The invention provides an electric vehicle charging demand prediction method and system and electronic equipment, and relates to the technical field of deep learning prediction. Comprising the following steps: acquiring historical observation data and corresponding time characteristic data of an electric vehicle charging station; an MIFM prediction model is constructed, an initial input information coding module receives initial data and carries out parallel embedding processing, and unified input representation containing node specific adaptive embedding is constructed; the spatial-temporal information dynamic fusion module obtains the unified input representation, captures explicit geographic space dependence and implicit function correlation in parallel through a two-channel dynamic graph learning mechanism, dynamically integrates multi-layer spatial-temporal characteristics by using an adaptive Kalman filtering fusion strategy, and generates an enhanced fusion signal; and the space-time dependency modeling and prediction module learns the spatial relationship representation based on the enhanced fusion signal by using a stacked graph attention network, captures time dynamics through a time sequence decoding module, and generates a charging demand prediction result of a future time step.

The invention discloses a natural resource monitoring multi-source heterogeneous spatio-temporal data fusion verification system and method, relates to the technical field of geographic information systems and spatial data processing, and is used for solving the problems of low fusion precision of multi-source heterogeneous data and difficulty in conflict processing. The system performs standardized packaging on the same geographic element of different data sources through an evidence packaging module to generate a standard evidence unit containing element type claim and data source credibility weight; the intelligent judgment module corrects the evidence probability based on the DS evidence theory in combination with the positioning precision, calculates the conflict degree and outputs a high-low confidence coefficient judgment result, so that the fusion reliability is improved; the semantic coupling module performs spatial topology and service semantic verification on the judgment result to generate a type recommendation sequence; the knowledge updating module dynamically adjusts data source credibility and semantic rule weight according to user confirmation information, a self-learning mechanism is established, and efficient conversion from multi-source heterogeneous data to high-credibility monitoring information is achieved.

The invention provides a low-altitude traffic service information fusion and interaction system and method, and relates to the technical field of low-altitude traffic management, and the system comprises a multi-source information collection module which is used for obtaining multi-dimensional data from different information sources; the dynamic weight information fusion module is used for calculating the dynamic weight of each information source based on the data credibility coefficient and the updating frequency factor of each information source, performing weighted fusion and generating low-altitude traffic comprehensive data; the traffic service information generation module is used for matching the low-altitude traffic comprehensive data with a preset service rule base to generate traffic service data; and the multi-terminal information interaction module is used for transmitting the traffic service data to different terminals by adopting different communication protocols. According to the system and the method provided by the invention, by integrating multi-dimensional information, the weight of the information source is dynamically adjusted, and the fusion accuracy is improved; the system adapts to various communication protocols, seamless interaction with different terminals is realized, and the service quality of low-altitude traffic service is remarkably improved.

The invention discloses a map construction method of a three-dimensional Gaussian SLAM (Simultaneous Localization and Mapping) algorithm based on depth information fusion, which comprises the following steps of: firstly, adopting a dynamic point cloud downsampling method based on depth information, so that the defect that structural information is easy to lose due to the fact that point cloud spatial distribution and geometric characteristics caused by the depth information are not considered in traditional random downsampling can be overcome; the sampling rate is dynamically adjusted through depth information, a key geometric structure is reserved while data redundancy is reduced, good geometric priori is provided for generation of Gaussian point clouds, and the generated Gaussian point clouds are more fit with the surface of an object; moreover, according to the method, the optimization efficiency in the map reconstruction process is improved through the perspective principle of depth information adaptive point size and simulation of human eye observation, and the geometric error of luminosity rendering of the reconstructed map is smaller; in addition, the depth information is optimized by utilizing multi-frame depth fusion, so that the geometric position generated by the Gaussian point cloud is more accurate, and the subsequent optimization time is reduced.

The invention discloses a video target tracking system based on historical information dynamic propagation and future frame prediction collaborative optimization, which belongs to the field of visual target tracking and comprises a dynamic attention token module, a multi-constraint future prediction module, a real-time bidirectional calibration module, a tracking decision module and a scene adaptive memory module. The dynamic attention token module is used for receiving continuous video frames and generating a multi-scale token sequence dynamically adjusted along with target states, and the target states comprise a shielding area, a movement speed and an appearance scale; the target tracking robustness problem in a complex scene is solved by dynamically fusing the historical trajectory features of the target and future state prediction, and the method can be applied to the fields of intelligent monitoring, automatic driving visual perception, unmanned aerial vehicle inspection, robot visual navigation and the like. A bidirectional optimization mechanism based on historical token propagation and future frame prediction breaks through the limitations of insufficient utilization of time sequence information and insufficient dynamic adaptability in the prior art.

The invention provides an OCR (Optical Character Recognition) information dynamic verification method and system based on multi-modal fusion and a medium. The method comprises the following steps: firstly, acquiring OCR identification data and scene environment data, extracting a multi-modal feature set, then training a multi-modal quality evaluation model based on historical data, performing field correctness, format compliance and logic relevance scoring on each modal, calculating confidence to position a problem modal, and finally obtaining a multi-modal quality evaluation result. The method comprises the following steps of: obtaining an OCR (Optical Character Recognition) identification result, performing traceability analysis on a problem mode, implementing mode enhancement, recalculating an enhanced quality score and a confidence score, dynamically distributing each mode weight according to enhanced confidence, obtaining a global quality score after weight fusion, and finally judging whether the OCR identification result is qualified or not based on a global score threshold value. According to the method, the accuracy and the robustness of OCR information verification are remarkably improved through collaborative verification and dynamic weight fusion of the multi-modal information, and identification interference in a complex scene can be effectively coped with.

The invention relates to the technical field of network fault diagnosis, in particular to an automatic network fault positioning system and method based on multi-source data fusion and an artificial intelligence algorithm, and the method comprises the steps: collecting network flow, equipment states, logs and topological structure data in real time; cleaning, denoising, standardization processing and feature extraction are carried out on the collected original data; extracting network health state key features, generating node embedding vectors, aggregating neighbor features, and splicing the aggregated neighbor features with equipment states and log semantic vectors; a fault diagnosis model is constructed based on deep learning and a knowledge graph technology, reinforcement learning and current network topology, equipment state and historical fault information are combined, a fault point is dynamically positioned, and a fault type is identified; and displaying the fault diagnosis result and the positioning information, and pushing alarm information to an operation and maintenance system. According to the invention, intelligent, accurate and efficient positioning of network faults is realized, the real-time performance and reliability of fault diagnosis are improved, and the operation and maintenance dependence and cost are reduced.

The invention discloses a dynamic anti-disturbance coordinated scheduling method for a hybrid energy micro-grid, and aims to solve the problems of poor stability, high cost, low consumption rate and the like of the hybrid energy micro-grid caused by disturbance such as renewable energy fluctuation and load abrupt change. The method comprises the following steps: firstly, constructing a system model containing multiple units, and collecting and preprocessing real-time operation data; establishing a multi-target scheduling objective function, and solving an initial scheduling instruction by adopting an improved predictive control algorithm; and finally, generating a coordinated scheduling strategy by combining the disturbance information dynamic correction instruction. Meanwhile, a security constraint and scheduling effect evaluation link is set, and mechanisms such as multi-time scale scheduling and layered response are adopted. The method can significantly improve the anti-disturbance capability and stability of the system, reduces the operation cost by 15%, improves the renewable energy consumption rate by more than 8%, is adaptive to multiple scenes, guarantees the safe operation, and is suitable for various hybrid energy microgrids.

The invention relates to the technical field of hybrid electric vehicle energy management, in particular to a target SOC track generation method and device based on road information dynamic optimization, and the method comprises the steps: carrying out segment processing on path data in a current navigation path of a vehicle, constructing a path information matrix through employing a plurality of obtained segment paths, and obtaining a path information matrix; calculating a dynamic basic SOC value of the power battery according to a target input characteristic matrix determined by the path information matrix, performing target correction processing to obtain an initial target SOC value of the power battery, performing adaptive correction processing, smoothing processing, compensation processing and dynamic limiting processing in sequence to generate a processed target SOC value, and outputting the processed target SOC value. And generating a target SOC track of the power battery by using the processed target SOC value. Therefore, the problem that in the related technology, dynamic adjustment near a fixed target is limited, so that target SOC control lacks global adaptability, and energy consumption optimization requirements under complex road conditions cannot be fully matched is solved.

The application provides a kind of multi-modal enterprise credit risk assessment method based on knowledge graph, by constructing enterprise financial knowledge graph, integrating enterprise relationship, industry policy, supply chain information and other data, using multi-modal embedding technology to process entity static attributes and associated information, combined with heterogeneous graph neural network to capture associated information, dynamic spatio-temporal attention mechanism to mine time and space features of time series data, and based on attention weight to identify core risk transmission path, finally fuse graph-level features, dynamic spatio-temporal features and business rules, output structured assessment results. This method effectively integrates multi-modal data, solves the problem of missing association relationship modeling, realizes the deep integration of enterprise multi-source data and the accurate extraction of risk features, and can effectively improve the accuracy and explainability of enterprise credit risk assessment.

The present application relates to the technical field of vehicle damping control, and discloses an anti-car-sickness intelligent damping control system. A multi-dimensional sensing array of the system collects vehicle driving state, passenger physiological indexes and environmental disturbance data in real time, and generates multi-dimensional driving data set in time and space alignment through time stamp alignment and space coordinate synchronization. A data separation engine carries out feature decoupling reconstruction on the data set, separates the main feature matrix and the abnormal disturbance identification set, and a feature screening unit screens the core feature sequence in combination with the vehicle dynamics parameter library. A damping strategy generator generates a multi-level collaborative damping control strategy accordingly. An abnormal feedback regulator analyzes the abnormal disturbance identification set and triggers strategy dynamic tuning. A control instruction distributor converts the tuned strategy into a driving signal and issues it to the suspension actuator cluster. The system can comprehensively perceive multi-dimensional information, dynamically adjust the damping strategy, effectively relieve car sickness and improve passenger comfort.

The application discloses a kind of dynamic ring vision splicing method and system based on image overlap area feature perception, method includes based on the depth information and the feature information dynamic planning splicing path;According to the splicing path of planning, image splicing is executed, including the multi-band image fusion based on depth perception.The present application extracts local and global features of the overlapping area and calculates pixel-level depth information, constructs a geometric transformation model optimized by feature-depth combination, so that panoramic stitching can accurately identify the outline of close-range objects, and dynamically plan the optimal stitching line to bypass prominent objects;By searching the distance splicing template based on the real distance and automatically unifying the focal length of each camera, the field of view range is kept consistent;Through the multi-scale depth estimation network guided by features and joint reprojection error optimization, high-precision registration can still be maintained in scenes with few feature points or low overlap areas.

The present application relates to a kind of layered reinforcement learning driven car-road cooperation automatic driving longitudinal following control method and system, belong to the field of automatic driving vehicle control.For the problem that the generalization ability of existing following control method is insufficient, strategy update is easy to lose stability, easy to fall into local optimum, adopt layered architecture, upper layer adapts intersection, high-speed straight road and ramp multiple typical traffic scenes, based on real-time car-road cooperation communication information dynamic planning follow target, after normalizing processing to multi-source heterogeneous traffic characteristics, generate environment state vector;Lower layer uses proximal policy optimization algorithm, outputs continuous accelerator or brake command through actor-critic double network, and updates network parameters by combining strategy entropy with pruning loss with Jacobian correction.The method improves the cross-scene generalization ability and perception robustness, effectively guarantees the stability and monotonicity of strategy update, avoids instruction step mutation, breaks the exploration deadlock, and balances driving safety, traffic efficiency and driving comfort.

The application provides an image generation method based on federated learning and an optimal time allocation strategy, comprising the following steps: determining the sparsity according to the client communication budget and the data volume, and randomly initializing the diffusion model parameters and the personalized sparse mask; determining the neighbor clients of each client according to different optimal time allocation strategies, and determining the mask intersection parameter aggregation mode of the current client and the neighbor clients according to the current round and the aggregation probability, to obtain the aggregated initialization model; performing gradient update on the initialized model parameters after aggregation according to the data batch and the parameter mask state, to obtain the updated model parameters; and calling a gradient information dynamic search algorithm to update the sparse mask in each client based on the updated model parameters. The application can protect the training data privacy of users of each party, can jointly use the data of each party to improve the generalization ability of the model, and can efficiently allocate limited communication resources under the condition of bandwidth limitation.

The invention discloses a privacy computing method and system based on state driving and a storage medium thereof, and relates to the technical field of information security and privacy computing. The method comprises the steps of obtaining execution state information of a current calculation task; dynamically generating a temporary key set based on the state information and configuring access authority; state information and interaction behaviors are continuously monitored in the task execution process; if the abnormal event is detected, key access response operation is executed, including revoking the current key access authority, updating a key set and authorization information thereof, and continuing task execution; and the updated state information and authorization information are fed back to the state evaluation module to realize closed-loop adaptive optimization of the control strategy. According to the method, the state credibility score and path deviation degree double-index feedback model is constructed, so that the exception recognition capability and the key management precision of the system in a complex environment are effectively improved, the flexibility of state perception and access control is enhanced, and the method is suitable for security sensitive scenes such as multi-party computing and federated learning.

The invention belongs to the technical field of cross-platform information processing, and relates to a cross-system data management method based on dynamic protocol analysis and metadata driven rendering, which comprises the following steps: a cross-system general data operation request comprises a data object type identifier and an operation parameter; querying a service registration center by using the service name identifier to obtain a target service instance; assembling through a unified data management protocol and sending a remote calling request to the target service instance; executing business data operation by using the operation parameters to generate original business data; packaging the original business data into a standard data transmission object, and returning the standard data transmission object; combining the standard data transmission object with the metadata configuration information to generate a comprehensive response packet; and driving the client to dynamically generate an interface control based on the metadata configuration information, and filling data in the standard data transmission object. The problems that cross-system data interaction is poor in flexibility, and codes need to be frequently modified for adaptation in front-end rendering are solved.

The application provides a grouting pressure and flow integrated optimization method based on diffusion inversion and gas grading, and relates to the technical field of grouting pressure and flow optimization. It comprises the following steps: constructing a multi-field coupling prior model and, based on the final multi-field coupling prior model, collecting on-line field data and performing data preprocessing to obtain a time series data set; taking the final multi-field coupling prior model as a forward modeling framework and taking the time series data set as a constraint, constructing a joint objective function and determining a parameter posterior distribution; based on the parameter posterior distribution and gas state information, dynamically evaluating risks and performing multi-objective optimization, and outputting a joint control curve and a corresponding execution plan. The application solves the problem that, in the prior art, real-time closed-loop control cannot be realized under uncertain conditions to achieve parameter distinguishability, dynamic gas grading linkage and opportunity constraint multi-objective optimization.

The invention relates to the technical field of network data security management, and discloses an internet data security multi-dimensional evaluation and analysis system, which comprises a data acquisition and preprocessing module used for acquiring security data from a plurality of heterogeneous internet data sources and performing standardization and entity relationship extraction on the security data; the core asset and risk map construction module is used for constructing a multi-dimensional attribute graph model based on the extracted entities and relationships, nodes in the multi-dimensional attribute graph model represent the entities, edges in the multi-dimensional attribute graph model represent the interaction relationships between the entities, and the nodes and the edges carry attribute information; and the dynamic risk assessment engine is used for distributing dynamic weights for edges in the multi-dimensional attribute graph model. According to the invention, by constructing the core asset and risk map fusing multi-source data, the system breaks the information island of a traditional assessment tool, and associates isolated vulnerabilities, abnormal behaviors and attacked assets into an organic whole.