1315 results about "Potential risk" patented technology

The invention provides an industrial environment monitoring and accident prediction method fusing multi-modal data, and relates to the technical field of data processing, and the method comprises the steps: carrying out the semantic collection and causal association preprocessing of multi-modal heterogeneous data collected in real time through constructing a dynamic industrial knowledge graph; a customized deep learning model is adopted to extract deep abstract features of each mode, and weak signals and potential risks are accurately represented and uncertainty is quantified; a high-fidelity digital twin model is utilized to drive a deep reinforcement learning algorithm, and dynamic optimization and verification are performed to generate a multi-level and multi-target preventive intervention strategy combination; an intervention strategy is executed through an edge-end-cloud three-layer collaborative intelligent architecture, and online learning and system sustainable evolution are realized by using a closed-loop data feedback mechanism. According to the method, the sensing and early warning capability of the early weak and complex abnormal state of the industrial environment can be remarkably improved, the accident evolution path is accurately predicted, and credible explanation is provided.

The invention discloses a dam safety monitoring system and method based on digital twinning, and particularly relates to the technical field of digital twinning. The method comprises the following steps: acquiring stress, deformation, displacement, inclination and environmental change data of a dam structure in real time, regularly carrying out high-precision calibration on a sensor, recording a time drift error, analyzing nonlinear sensitivity of a digital twin model under an extreme working condition, simulating the influence of the sensor error and the model sensitivity on dam evaluation, predicting a deviation degree, and calculating the evaluation accuracy of the dam. Based on a prediction result, a dynamic drift compensation algorithm is adopted to correct sensor data in real time, the sensitivity of the model is dynamically adjusted, the optimized data and model are integrated, the structural state and potential risk of the dam under normal and extreme working conditions are accurately evaluated, accurate early warning information and decision scheme are generated, and the accuracy of early warning is improved. The accuracy and reliability of dam safety monitoring and risk assessment are remarkably improved, decision delay or resource waste caused by misjudgment or missed judgment is reduced, and the dam safety guarantee capacity is improved.

The invention provides a knowledge graph generation method and system for science and technology project risk control, and the method comprises the steps: obtaining a multi-source heterogeneous data set of a target science and technology project, converting structured index data into a standard vector sequence through a heterogeneous data fusion mechanism, and converting unstructured text data into a semantic vector sequence; converting the time sequence behavior data into a behavior pattern vector sequence, inputting the three into a risk quantitative evaluation model, generating a risk entity feature matrix and a risk association strength matrix, and determining a node distribution topology of the knowledge graph according to entity feature vectors in the risk entity feature matrix; and according to association strength values in the risk association strength matrix, determining an entity relationship topology of the knowledge graph, generating a dynamic knowledge graph of the target science and technology project, and identifying a potential risk propagation path in the dynamic knowledge graph. According to the invention, the risk identification result has the dynamic characteristic of real-time updating, and the traceability of the multi-dimensional risk characteristic is maintained.

Systems and methods for providing a space launch service platform (SLSP) that integrates artificial intelligence and data analytics to support launch operations. The SLSP may connect to multiple data sources, collect data, and standardize the collected data according to regulatory and operational standards. The SLSP may evaluate launch safety and risks using standardized data and generate a situational analysis for decision-making. The SLSP may provide graphical overlays and decision-support tools to highlight optimal launch windows and potential risks.

The invention provides an enterprise employee behavior analysis and safety risk early warning monitoring method and system, and relates to the technical field of enterprise risk management, and the method comprises the steps: collecting employee terminal operation behavior data, building an activity thermal distribution diagram based on office area grid behavior association intensity, and training a behavior evaluation model. And inputting the behavior scoring matrix into a deep neural network to extract target behavior characteristics, calculating an abnormal behavior risk weight coefficient in combination with a department security policy, performing classification and analyzing a risk diffusion probability, and generating a risk situation index to determine an early warning level. Starting a response strategy according to the early warning level, blocking high-risk early warning in real time, tracking associated accounts, establishing a risk association map, identifying potential risk propagation nodes and performing active protection, finally generating an early warning report and feeding back effective protection rules to a behavior baseline model, and realizing continuous optimization of a risk early warning mechanism. And the accuracy and effectiveness of safety risk early warning in the enterprise are improved.

The invention provides a construction safety prompting method and system, and the method comprises the steps: carrying out the collection and fusion processing of multi-dimensional environment data of a construction site, and constructing a real-time updated environment digital twinborn model. And according to the model and individual physiological data of the workers, carrying out adaptive evaluation on the safety state of each worker to obtain an individual dynamic safety threshold. In combination with an environment digital twinborn model, an individualized dynamic safety threshold and real-time state data, a graph neural network reasoning technology is adopted to comprehensively evaluate the safety situation, and a potential risk trend is predicted. And according to a security situation assessment result and a risk prediction trend, generating adaptive early warning and intervention schemes for different risk levels by adopting a situation awareness grading processing mode, and providing accurate security prompts. According to the method, the safety state of the worker is monitored, predicted and early warned in real time according to the dynamic change of the construction site, the intelligent level and accuracy of construction safety management are improved, and the accident occurrence risk is reduced.

The invention discloses a contract risk assessment and compliance check method, system and device based on a large language model and a medium, belongs to the cross technical field of artificial intelligence and natural language processing, and aims to solve the technical problem of how to automatically, quickly and accurately identify potential risks and compliance in a contract. According to the technical scheme, the method comprises the steps of data preprocessing, wherein non-plain text format files of various sources are subjected to data preprocessing and converted into plain text format files; constructing a knowledge performing semantic understanding and relation extraction on industrial standards, internal regulations of enterprises and a plain text format file obtained after preprocessing by utilizing a big language, and constructing a structured and queriable knowledge base; training a large language model: optimizing parameters of the large language model in combination with pre-training and supervised training; and risk assessment and compliance check: based on the trained large language model and the compliance rule base, performing identification and analysis of potential risk points and compliance check on the input contract text.

The invention discloses a comprehensive service system and method for intelligent safety protection, and relates to the technical field of safety service systems.After state data of a park are intelligently analyzed, potential risk event types are identified, the risk propagation trend is judged through a space-time association algorithm, the risk level of each area in the park is evaluated, and the risk level of each area in the park is evaluated. And generating a risk level map, performing path planning and scheduling optimization on the inspection equipment according to the current available inspection resources and the risk level map, and cooperatively controlling all the inspection equipment to operate based on path planning and scheduling optimization results. The service system generates a high-credibility regional risk level map, takes a current available resource state and the risk level map as input conditions, dynamically generates a routing inspection path and task allocation, effectively improves reasonable utilization and allocation of routing inspection resources, automatically adjusts a scheduling task under the condition that individual routing inspection equipment fails, has sudden obstacles or is limited in path, and improves the routing inspection efficiency. And stable proceeding of the inspection task is ensured.

The invention relates to the field of enterprise risk early warning analysis, in particular to an intelligent risk early warning method and system based on multi-dimensional data analysis. The method comprises the following steps: acquiring a multi-dimensional enterprise data stream, performing heterogeneous index information analysis and logic hierarchy reconstruction, and constructing an enterprise running state sensing map; performing multi-index local fluctuation amplitude calculation on the enterprise operation state sensing map, and performing dynamic disturbance feature mining to construct a risk disturbance spectrogram; performing deep semantic analysis and node state abrupt change feature analysis on the risk disturbance spectrogram to generate a dynamic transition type of an abrupt change node; and performing full-period time sequence tracing on the risk disturbance spectrogram, performing potential risk node prediction based on the dynamic transition type, and identifying other potential risk propagation ports. Through accurate and efficient enterprise risk perception, the risk intervention decision is made in advance, and the anti-risk capability and the operation stability of the enterprise are improved.

An AI-powered agile project management system for automatic risk assessment and mitigation, including: a risk identification module configured to extract project data from agile project management tools, including but not limited to Jira, Trello, Asana, and Monday.com, and identify potential risks using machine learning and NLP; a risk analysis module configured to assign risk scores based on probability, impact, and severity using predictive analytics, where the module uses historical data, real-time project status, and external dependencies to determine the severity and impact of risks; a real-time monitoring module configured to continuously track project conditions, detect anomalies, and generate immediate notifications via email, mobile apps, or messaging platforms when high-risk events are identified; a risk mitigation module configured to dynamically recommend and execute proactive corrective actions such as task reassignment, resource optimization, schedule adjustments, and implementation of alternative strategies using AI-driven decision-making, where the actions can be automated through predefined workflows or manually adjusted based on AI recommendations; an AI-driven task prioritization module configured to dynamically prioritize tasks based on risk factors, project deadlines, and team workload distribution; an integration module configured to ensure seamless compatibility with existing Agile project management frameworks through API-based synchronization; a learning and continuous improvement module configured to refine risk prediction models over time using machine learning and historical risk mitigation results, with AI-driven models continuously updating risk assessment mechanisms based on evolving project conditions, industry-specific risk factors, and historical performance metrics; a compliance and audit reporting module configured to automatically generate risk reports, audit logs, and compliance checklists; a customizable risk assessment configuration module configured to allow project managers to define custom risk parameters, probability thresholds, and mitigation preferences; an interactive dashboard module configured to visualize data in real time, including risk trends, effectiveness of remedial actions, and analysis of project progress; and a scalability feature configured to be used in industries such as IT, healthcare, finance, construction, and manufacturing.

The invention discloses an engineering project supervision real-time visual decision-making platform based on multi-source heterogeneous data fusion, and relates to the technical field of engineering supervision. The visual decision platform is in communication connection with a data acquisition module, a project risk feature module, a risk identification model construction module, a real-time data stream processing module, a risk early warning module and a decision support module. According to the invention, the risk in the engineering project is accurately identified in combination with historical multi-source heterogeneous data and project risk characteristics, and meanwhile, the real-time data stream processing module can analyze data in real time, identify potential risks and problems and generate early warning information, so that the accuracy and timeliness of risk identification are greatly improved, and the risk identification efficiency is improved. According to the invention, powerful decision support is provided for supervisors, and through the preset early warning level and the early warning signal, managers can rapidly understand the risk condition and take corresponding measures, thereby effectively reducing the project risk.

The invention provides a software supply chain risk detection protection method and system, and the method comprises the steps: obtaining a third-party component from a software supply chain, and analyzing the multi-level dynamic behaviors of the third-party component; predicting a risk behavior pattern by using a Bayesian network algorithm, and constructing a behavior pattern library; monitoring a behavior of the third-party component during operation, and recording a behavior data stream; identifying an abnormal behavior deviating from a normal behavior mode by applying a time sequence anomaly detection algorithm of deep learning; evaluating risk levels of the abnormal behaviors by using a graph neural network, and generating an abnormal behavior list in combination with relevance and propagation paths among the behaviors; according to the list, a response strategy in the safety control measure library is dynamically adjusted through an adaptive strategy selection algorithm, and safety control measures are formulated for the third-party component. According to the invention, the identification and prediction capabilities of potential risk behaviors are improved.

The embodiment of the invention provides a BIM-based hydraulic engineering digital management method and system. The method comprises the following steps: performing multi-dimensional monitoring system deployment on a to-be-monitored area to obtain multi-dimensional hydrological data; constructing a first BIM based on the topographic data of the to-be-monitored area, the real-time work area image, the hydraulic engineering construction information of the design stage and the equipment deployment information; fusing the multi-dimensional hydrological data, the water area change data, the construction progress data and the operation and maintenance monitoring data into the first BIM, and constructing a second BIM including a full life cycle; and through the equipment characteristic curve and the water conservancy project physical model, in combination with the multi-dimensional hydrological data and the water area change data, predicting a water conservancy project structure change trend and potential risk factors in the second BIM, fusing the water conservancy project structure change trend and the potential risk factors into the second BIM, and displaying an implementation effect and improvement suggestions in real time in the second BIM. A user is assisted to realize hydraulic engineering digital management, and the engineering management efficiency is improved.

The invention discloses an intelligent contract risk recognition method and system, and relates to the technical field of text recognition, and the method comprises the steps: extracting a semantic vector of a to-be-recognized file; obtaining a first risk identification result based on the semantic vector and the review list; performing semantic matching on the semantic vector and the review knowledge graph to obtain a potential risk; obtaining a first risk category based on the potential risk and the review knowledge spectrogram, obtaining a preset risk judgment rule based on the first risk category, and obtaining a second risk category based on the preset risk judgment rule; obtaining a second risk identification result based on the contract category and the second risk category; constructing a clause rule base, and obtaining a compliance result based on the semantic vector and the clause rule base; obtaining a complete result based on the semantic vector and the standardized contract template library; and obtaining a total risk identification result based on the above identification result, thereby solving the problems of low risk identification efficiency and low accuracy caused by the fact that an existing contract term risk identification method depends on the determination of the license experience of professionals.

The invention provides a risk control early warning method and system based on bank flow analysis, and relates to the technical field of computers, bank flow data of a target user in a bank system is collected, the collected bank flow data is fused with multi-source cross-domain data, and time sequence data of transaction behaviors is generated according to a time sequence; performing data feature extraction on the time sequence data to obtain transaction features; constructing a framework of a risk prediction model, and obtaining the risk prediction model through training and verification; real-time flow data of a target user in a bank system is monitored in real time, a trained risk prediction model is utilized to evaluate transaction behaviors of clients in real time, an anomaly detection result of an anomaly detection model is combined with output of the risk prediction model, a comprehensive risk score is formed to identify potential risks, and an early warning mechanism is triggered. Through deep analysis of bank flow data, real-time risk monitoring and early warning are realized, and potential financial risks can be effectively identified.

The invention discloses a building construction safety risk assessment method and system, and relates to the technical field of artificial intelligence, and the method comprises the steps: constructing a neural network model, and recognizing an unsafe behavior mode of a worker; receiving a data stream from a construction site in real time, dynamically adjusting risk assessment parameters according to a current construction state and environment change, and predicting potential safety risks; according to the prediction result, generating corresponding prevention measure suggestions; key indexes and abnormal conditions are displayed through a monitoring interface, and a detailed analysis report is generated; according to the invention, through dynamic assessment and timely response to the safety risk of the construction site, the risk assessment parameters can be rapidly adjusted according to the environmental change, the potential risk can be predicted, and the prevention measures and suggestions can be provided, so that the occurrence probability of safety accidents is effectively reduced, and the construction efficiency is improved.

The invention relates to the technical field of path planning scheme design based on deep reinforcement learning, in particular to a path planning method and system based on deep reinforcement learning and electronic equipment. According to the method, a reward function is designed by comprehensively considering path length, smoothness, feasibility and multi-objective optimization weight through a deep reinforcement learning model, the model is trained by using experience playback and exploring and utilizing a balance strategy, and the generated path is used for real-time planning after being subjected to smoothness and feasibility correction. The system comprises an environment perception module, an action definition module, a reward function design module, a model construction and training module, a path optimization module, a plan updating module and the like. The electronic device covers a processor, a memory, and input and output modules to implement real-time path planning. According to the method, the quality and efficiency of path planning can be improved, the method adapts to a complex dynamic environment, potential risks caused by frequent turning or sudden change are effectively avoided, the practicability and safety of the path are improved, and the method has a wide application prospect.

The invention discloses an integrated collaborative management method for construction site risk operation data, and belongs to the technical field of data management. The method comprises the following steps: constructing and calibrating a space-time risk reference database; deducing a potential risk conduction link set, and generating an associated intervention knowledge base; during operation, through multi-sensor cooperative verification, an abnormal signal is confirmed as a risk event; matching the risk event with a conduction link to calculate a risk upgrade level and dynamically adjust an early warning threshold; and when the early warning is triggered, generating and issuing a dynamic collaborative response instruction, and feeding back a processing result to correct the reference database to form a management closed loop. According to the method, the technical means of constructing the space-time risk reference, deducing the conduction link, cooperatively verifying the risk and dynamically regulating and controlling the threshold are adopted, so that the predictability of project risk management and control, the efficiency of resource cooperative scheduling and the scientificity of overall management decision are improved.

The invention discloses a coal mine pressure risk assessment method based on complex system dynamics, belongs to the technical field of mine pressure risk assessment, is used for mine pressure risk assessment, and constructs a complex system dynamics model, combines various sensor data, and carries out real-time monitoring and risk assessment of mine pressure changes. The method comprises the steps of geological data acquisition, sensor data real-time monitoring, data preprocessing, mine pressure prediction model construction, model training and optimization, real-time risk assessment and the like, data of a stress sensor, a displacement sensor and an acoustic emission sensor are comprehensively utilized, and data preprocessing is performed by adopting a Kalman filtering algorithm; and optimizing the mine pressure prediction model by using a graph convolutional network and an attention mechanism, and performing real-time risk assessment through an adaptive graph neural network. According to the method, the adaptive graph neural network is adopted for real-time risk assessment, potential risks in mine pressure changes can be found in time, risk scores are generated, and scientific decision support is provided for coal mine managers.

The invention relates to the technical field of power systems, in particular to a power grid operation risk level visualization intelligent management tool system. The system obtains power grid operation data through an Internet of Things sensor network, wherein the power grid operation data comprises current, voltage, equipment operation state and other multi-dimensional information; performing real-time analysis on the acquired data by adopting multi-level clustering analysis, a predictive maintenance algorithm and a deep learning model to realize abnormal behavior identification and risk prediction; a dynamic risk map is constructed based on a GIS technology, and accurate positioning and real-time display of risk levels are realized in combination with regional feature recognition and personalized threshold setting. According to the invention, multi-source data acquisition, intelligent analysis and visual presentation are creatively and organically integrated to form a closed-loop risk management system, so that the system has adaptive learning ability. Practical application shows that the system can early warn potential risks 30 minutes in advance, the operation and maintenance response time is shortened to 3 minutes, and the power grid operation risk monitoring and management efficiency is remarkably improved.

The invention discloses an unmanned inspection and intelligent fault judgment method. A security threat model is constructed through deep learning, potential risks are identified, a strategy is made, security analysis modeling based on a comparison technology is utilized, and system state abnormity is detected. In data transmission, confidentiality and integrity are guaranteed by integrating a plurality of security means, a multi-level protection framework is designed, and all-round defense is carried out from a network boundary, a host terminal and an application system. By means of anomaly detection monitoring index data, anomaly and risk are subjected to correlation analysis, and a basis is provided for decision making. And regularly carrying out security assessment and penetration testing, repairing vulnerabilities, and carrying out optimization and reinforcement. The method covers multiple aspects such as threat modeling, safety analysis, data protection, protection framework construction, anomaly detection and evaluation reinforcement, the safety and reliability of the unmanned inspection system are effectively improved, network attacks and illegal access are resisted, and stable operation of the system is guaranteed.

The invention relates to the technical field of bridge engineering, in particular to a system and method for predicting the service life of a steel structure bridge after fatigue crack repair. The method comprises the steps that load data and stress data in the operation process of a bridge structure after fatigue crack repairing are collected; dynamic response characteristic parameters are extracted in combination with stress distribution characteristics of the fatigue crack repair area; performing multi-dimensional data fusion and preprocessing on the dynamic response characteristic parameters, and constructing a data set for training a deep learning algorithm; combining a deep learning algorithm and a fracture mechanics theory to construct a crack propagation rate prediction model and a residual life prediction model; according to the operation state of the bridge and the performance degradation law of the repairing area, the remaining life of the repaired structure is predicted, and multi-stage early warning is conducted. According to the method provided by the invention, bridge structure performance evaluation, life prediction and potential risk early warning can be realized, and the safety and reliability of bridge operation are ensured.

A prefabricated building component stability monitoring and early warning method and system, relating to the technical field of engineering project management. The method comprises: on the basis of a target building engineering drawing, constructing a construction node sequence; generating a plurality of engineering construction simulation models; acquiring a sample data set of a plurality of construction nodes, performing stability-associated factor analysis, and constructing a node factor mapping sequence; by using the node factor mapping sequence as monitoring data, performing construction monitoring, and determining construction monitoring data of a first construction node; on the basis of the construction monitoring data, performing mapping and acquiring a first engineering construction simulation model, performing error analysis by means of a twin comparison channel, and determining a first construction error; and on the basis of the first construction error, generating a construction early warning signal, and, on the basis of the construction early warning signal, acquiring a first construction compensation parameter and performing subsequent construction compensation for target building engineering, thereby achieving the technical effects of improving the safety and reliability of a prefabricated building structure and reducing potential risks and losses.

The invention discloses a goat milk product cold-chain logistics system risk evaluation method. The method comprises the steps of S1, collecting a plurality of key parameters in a goat milk product cold-chain logistics process in real time; s2, historical cold-chain logistics data are collected, risk event labeling is carried out, and a historical data set is generated; s3, preprocessing the real-time data set and the historical data set, and generating a feature vector for risk assessment by applying a feature engineering technology; s4, based on the feature vectors, a support vector machine algorithm is adopted to construct a multi-level risk assessment model through a multi-dimensional feature fusion technology; s5, performing dynamic risk assessment according to the real-time data set and the multi-level risk assessment model; and S6, according to a dynamic risk assessment result, by setting an intelligent threshold value, automatically identifying a potential risk and triggering an early warning mechanism, forming an early warning signal, and according to an early warning level, automatically starting an intervention measure. The method has the advantages of being accurate, real-time and dynamic in response, and the safety and reliability in the cold-chain logistics process are effectively improved.

The invention discloses an AI-driven robot working environment safety sensing method, and relates to the technical field of artificial intelligence, and the method comprises the steps: collecting the visual, laser radar, force sense and environment parameter data of a working environment in real time through a multi-mode sensor; performing feature extraction and semantic understanding on the acquired data by using a deep learning algorithm to generate multi-dimensional perception information of the environment; based on a preset security policy model, in combination with the multi-dimensional perception information, predicting and classifying potential risks; according to a risk classification result, a behavior strategy of the robot is dynamically adjusted, and active safety decision is realized; and through the continuous learning module, the deep learning model and the security policy model are optimized online by using newly collected data so as to adapt to environmental changes.

The invention discloses a risk assessment and prediction method based on a network security situation awareness system, and relates to the technical field of network security. According to the risk assessment and prediction method based on the network security situation awareness system, a current network potential risk data set is obtained, and a current network potential risk assessment value is obtained through comprehensive analysis based on the obtained current network potential risk data set; acquiring a current network traffic data set, and based on the acquired current network traffic data set, performing comprehensive analysis to obtain a current network traffic evaluation value; comprehensively analyzing the current network potential risk assessment value and the current network flow assessment value to obtain a network security risk coefficient and a network security risk level; by comprehensively analyzing the current risk and flow data, a more comprehensive, real-time and dynamic network security management scheme is provided, potential threats can be effectively identified, and the early warning capability is improved. The problem that the existing network security situation awareness system is inconvenient to comprehensively reflect the network security situation is solved.

The invention discloses an operation data risk intelligent identification and evaluation method based on a large language model. The method comprises the following steps: S1, collecting operation data of a data source; s2, preprocessing the unstructured data in the operation data; s3, performing semantic analysis on the unstructured data by using an improved BERT large language model, and identifying potential risk signals; s4, preprocessing the structured data, extracting feature information, and fusing a risk signal identification result with the feature information; s5, risk assessment is carried out based on an improved XGBoost algorithm; s6, performing quantitative evaluation on the identified risk factors; s7, when the operation data is updated, performing real-time risk identification and evaluation on the new operation data; and S8, when the risk assessment result triggers a set early warning threshold value, generating risk early warning. According to the method, the improved BERT model and the XGBoost algorithm are combined, industrial data risk identification and evaluation are realized, and the method has the advantages of high accuracy, high real-time performance and high automation degree.

The invention relates to the technical field of computer vision, and discloses a building construction safety state evaluation system and method based on computer vision. The judgment module is configured to judge whether the construction danger early warning threshold value needs to be corrected or not according to the historical construction deviation coefficient: when it is judged that the construction danger early warning threshold value needs to be corrected, a correction coefficient corresponding to the construction danger early warning threshold value is set, and the corrected construction danger early warning threshold value is obtained; the processing module is configured to compensate the corrected construction danger early warning threshold value according to the environmental factor influence coefficient to obtain a compensated construction danger early warning threshold value; and the early warning module is used for judging the construction safety state level of the construction area according to the comparison result and judging whether danger early warning or danger warning is carried out or not according to the construction safety state level. According to the method, potential risks in the construction process can be evaluated more accurately, early warning or warning signals can be sent out in time, and smooth proceeding of construction activities is ensured.

The invention relates to the field of enterprise safety assessment, in particular to an enterprise data safety capability assessment method and system. The method comprises the following steps of: obtaining an enterprise historical security log based on an enterprise information system, performing real-time data flow analysis and dynamic positioning of a storage position on the enterprise historical security log, and constructing an enterprise data flow mapping model; performing abnormal intrusion attack simulation on the enterprise data flow mapping model, performing enterprise security risk assessment, and marking potential risk nodes; performing historical normalized behavior analysis on the dynamic enterprise data flow model, and performing access behavior deviation detection on potential risk nodes to obtain abnormal intrusion behavior data of the enterprise system; and performing multi-time-window intrusion trajectory backstepping on abnormal intrusion behavior data of the enterprise system, and performing deep mining of abnormal attack features to obtain intrusion attack features. According to the invention, efficient and accurate enterprise data security assessment is realized, so that the enterprise is helped to make a targeted security protection decision.

A threat management facility receives data from a variety of sources such as compute instances within an enterprise network, cloud service providers supporting the enterprise network, and third-party data providers such as geolocation services. In order to facilitate prompt notification of potential risks, the threat management facility may incrementally update data for use in threat assessments as the data becomes available from these different sources, and create suitable alerts or notifications whenever the currently accumulated data provides an indication of threat meeting a predetermined threshold.