13107 results about "Risk assessment" patented technology

The invention provides a home abnormal state signal detection method and system based on multi-modal sensing, and relates to the technical field of detection, and the method comprises the steps: collecting human body motion, acoustics and environment parameters through a millimeter wave radar, an acoustic sensor and an environment sensor, inputting the parameters into a deep fusion network, a spatio-temporal attention mechanism and a multi-scale convolutional neural network are used to extract a spatio-temporal feature sequence, and joint probability features are formed in combination with an acoustic feature spectrum analysis result. And inputting the joint probability features into a time sequence knowledge graph, and obtaining scene adaptive features after processing by a graph attention network and a double-flow auto-encoder network. And finally, the multi-task learning network is combined with the risk propagation neural network and the recursive neural network to carry out anomaly prediction and risk level evaluation, and is combined with historical early warning information to output an early warning strategy. According to the invention, multi-modal data can be effectively fused, the accuracy and reliability of home abnormal state detection are improved, and a more accurate risk assessment and early warning strategy is provided.

The invention relates to the technical field of data processing, and discloses a financial transaction anomaly detection and risk assessment method and device based on artificial intelligence. The method comprises the following steps: collecting and preprocessing multi-source financial transaction related data to obtain a multi-dimensional transaction data set; performing feature engineering and abnormal mode recognition on the multi-dimensional transaction data set to obtain an abnormal transaction recognition result; based on the abnormal transaction identification result and the multi-dimensional transaction data, constructing a financial domain knowledge graph to obtain a relation network between transaction entities; performing multi-dimensional risk assessment on the relation network among the transaction entities to obtain a risk score and risk description data; based on the risk score, performing time sequence analysis and trend extrapolation on the risk description data to obtain a dynamic risk early warning signal; and performing scene simulation and multi-factor analysis on the dynamic risk early warning signal to obtain a risk management strategy suggestion. The efficiency and accuracy of financial transaction anomaly detection and risk assessment based on artificial intelligence are improved.

The invention relates to the field of security surveying and mapping, in particular to a network security space surveying and mapping method, system and device based on multi-source data fusion, and the method comprises the steps: obtaining network security data in real time, and constructing a dynamic network topological graph; calculating a time-varying vulnerability score based on the topological graph and a historical attack log, and predicting an attack path and a propagation probability through a Bayesian network; performing cross-domain fusion on equipment, service and user behavior characteristics by adopting a federated learning framework to generate a dynamic asset portrait; generating a risk thermodynamic diagram in combination with spatial autocorrelation analysis and a multi-index fusion algorithm; a defense strategy effect is simulated based on an attack graph reconstruction engine, a Pareto optimal strategy combination is generated through an NSGA-II algorithm, and closed-loop verification and dynamic parameter correction are realized by utilizing honeypot deployment and flow traction. Therefore, the problems of topology update lag, single risk assessment dimension, cross-domain threat association fracture, defense strategy static stiffness, non-closed loop of a verification system and the like in the traditional technology are solved.

The invention belongs to the technical field of bridge diagnosis, and discloses a bridge crack intelligent diagnosis system based on multi-modal data fusion. By fusing multi-source data such as visual images, sound wave detection and vibration signals, comprehensive perception and characterization of crack features are realized; constructing a bridge crack characteristic spectrum diagram by adopting a cross-modal feature extraction and heterogeneous feature coding technology; generating a crack evolution situation map based on space-time correlation analysis and knowledge graph construction; the robustness of the system in a complex environment is improved through environmental adaptability feature enhancement and multi-scale characterization; constructing a bridge safety risk hypergraph in combination with multi-dimensional risk analysis and multi-agent collaborative diagnosis; analyzing and revealing a crack evolution mechanism by applying a causal relationship; and finally, through dynamic fusion and uncertainty quantification, a crack intelligent diagnosis comprehensive report is generated. According to the system, the limitation of traditional single-mode diagnosis is broken through, and dynamic prediction and accurate risk assessment of fracture evolution are realized.

To significantly improve the prediction accuracy, response speed and management efficiency of large river bank slope disasters, an integrated bank slope monitoring method based on multi-parameter collaborative recognition is proposed. The solution includes step S1 of synchronously collecting data on bank slope displacement, pore water pressure, inclination angle, vibration frequency and environmental temperature and humidity to form an original monitoring dataset and construct a multi-parameter collaborative recognition network; step S2 of using a multi-modal data fusion algorithm to generate a fusion data matrix including spatiotemporal correlation features and perform spatiotemporal data alignment and outlier cleansing; step S3 of combining a geomechanical parameter library and a past disaster case library to output a risk level map and perform dynamic risk assessment model analysis; and step S4 of triggering a multi-level early warning mechanism and generating linked control commands including treatment suggestions to perform multi-level early warning and linked control.

The invention discloses a hydraulic engineering equipment data intelligent management system based on digital twinning, and belongs to the technical field of hydraulic engineering. Comprising an intelligent perception and data fusion module for realizing real-time acquisition and standardized processing of cross-modal data; the knowledge graph construction and causal reasoning module is used for constructing an intelligent knowledge system capable of autonomously learning and semantic reasoning; the digital twin modeling and simulation module is used for realizing dynamic simulation and scene deduction of a full life cycle and providing limit working condition simulation and risk assessment support; the intelligent prediction and health management module is responsible for performing real-time monitoring, fault early warning and residual service life prediction on the equipment state, and generating personalized intelligent maintenance strategies for different working conditions; the visualization and decision support module is used for visually presenting the equipment operation data and the analysis result and providing intelligent decision recommendation; and the cloud edge collaboration and system integration module realizes cross-platform interoperation and continuous integration through distributed computing and micro-service architecture.

The present invention relates to the technical field of mine safety management, and discloses a method and system for dynamic monitoring and early warning of mine environment, including the following steps: multi-source data acquisition, collecting dynamic data of mine environment through remote sensing imaging equipment, ground sensors and meteorological stations; data preprocessing, noise cleaning, time alignment and spatial reference coordinate unification of the data; spatiotemporal data fusion, mapping multi-source data to a unified feature space through feature extraction, cross-modal fusion and attention mechanism; intelligent analysis and disaster prediction, predicting surface deformation, landslide risk and tailings dam stability in mining areas based on multi-task joint learning models; risk assessment and early warning response, dividing risk levels according to comprehensive risk scores, and dynamically generating early warning information, evacuation paths and dangerous area isolation boundaries. The present invention solves the problems of difficulty in multi-source data fusion, low disaster prediction accuracy and delayed early warning response in the prior art.

The invention provides a multi-modal enterprise credit risk assessment method based on a knowledge graph, which integrates data such as enterprise relationships, industry policies and supply chain information by constructing an enterprise financial knowledge graph, processes entity static attributes and associated information by using a multi-modal embedding technology, captures the associated information in combination with a heterogeneous graph neural network, and evaluates the credit risk of an enterprise. And the dynamic space-time attention mechanism mines time and space features of the time series data, identifies a core risk conduction path based on an attention weight, and finally fuses graph-level features, dynamic space-time features and business rules to output a structured evaluation result. According to the method, multi-modal data is effectively integrated, the problem of incidence relation modeling deficiency is solved, deep fusion of enterprise multi-source data and accurate extraction of risk features are realized, and the accuracy and interpretability of enterprise credit risk assessment can be effectively improved.

The invention belongs to the technical field of automobile parts, and discloses an automobile part enterprise supply chain risk early warning method based on artificial intelligence. Comprising the steps that supply chain data are collected and processed, and a graph is constructed; evaluating the suppliers based on the atlas to generate a portrait matrix; on the basis of the portrait matrix and in combination with the production parameters, model training is performed, a prediction engine is constructed, and a part quality risk prediction result is generated; performing anomaly detection on the nodes to form a monitoring network, and generating a risk assessment result; constructing a supply chain network topology model based on the map, and performing risk propagation path analysis to generate a risk conduction map; establishing a risk assessment model, integrating the risk prediction result, the risk assessment result and the risk conduction diagram, and performing integrated assessment on the risk of each link of the supply chain to form a scoring system; based on a scoring system, a dynamic risk early warning threshold is generated, a risk response decision tree is constructed, intelligent risk response suggestions are provided, and the enterprise risk disposal efficiency is improved.

The invention discloses a dynamic sensitive data outbound risk assessment method and system based on multi-source risk information in the technical field of data cross-border. The system is mainly composed of a multi-source risk information acquisition module, a dynamic security identifier generation module, a risk collaborative assessment engine, a dynamic weight adjustment module, a disposal range dynamic calculation module and a flexible emergency disposal module, and an acquisition-identifier-assessment-calculation-disposal full-process closed-loop architecture is formed. A full-process closed-loop processing framework of collection-identification-evaluation-calculation-disposal is innovatively proposed, and by combining a dynamic risk evaluation model, a reinforcement learning intelligent technology and a block chain evidence storage technology, the problems of insufficient dynamic nature, lack of collaboration and lack of closed-loop capability in the prior art are systematically solved; and high-precision evaluation, real-time response and traceable management and control of the cross-border data flow risk are realized.

The invention relates to the technical field of judicial data security, and discloses a judicial system confidential data security circulation method based on a block chain technology. Collecting multi-source judicial data, identifying sensitive information through a large language model, and performing differential privacy desensitization processing; constructing an SM4 encryption and TLS 1.3 end-to-end secure channel, generating a data hash fingerprint, and writing the data hash fingerprint into an alliance chain evidence based on a PBFT consensus mechanism; designing a multi-modal classification engine to perform feature extraction and intelligent classification; establishing a hybrid authority model to integrate an XACML strategy and a Kafka queue, and implementing dynamic authority control in combination with an RBAC / ABAC mechanism; a hierarchical encryption storage architecture is constructed, and homomorphic encryption retrieval and erasure code distributed storage are adopted; constructing a judicial knowledge graph based on a BERT model; and deploying a block chain auditing system, and combining LSTM anomaly detection and a DREAD risk assessment model to form a closed-loop risk control system. According to the method, the problems of security risk and privacy disclosure in judicial data cross-department circulation are solved.

The invention relates to the technical field of information processing, in particular to a water conservancy slope ecological management method and system based on multi-source sensing data. The method comprises the following steps: collecting meteorological monitoring data, soil hydrological monitoring data, vegetation coverage monitoring data, topographic surveying and mapping data and water and soil loss monitoring data as original slope ecological environment data; performing data pre-registration processing on the original slope ecological environment data to obtain standardized slope ecological environment data; performing erosion-runoff coupling process simulation by using the standardized slope ecological environment data to obtain water and soil erosion dynamic parameters; performing water and soil loss trend prediction based on the water and soil erosion kinetic parameters, and generating water and soil loss risk assessment data; and designing an ecological management scheme based on the water and soil loss risk assessment data. By dynamically adjusting and optimizing the ecological management scheme, the ecological state of the slope is monitored in real time, and continuous improvement and self-adaptive optimization of ecological management of the water conservancy slope are achieved.

The invention discloses a construction engineering risk assessment method and system for multi-source anomaly monitoring, and particularly relates to the field of construction engineering risk assessment, comprising the following steps: analyzing equipment-to-component node mapping and return delay characteristics based on multi-source monitoring data received in an idle task period; establishing a cross-device time alignment model and a pseudo-synchronization section time migration trajectory, identifying a potential abnormal path set through a time delay drift trend, and providing a time feature basis for subsequent structure anomaly reasoning and risk assessment; deducing a node triggering sequence and a time causal chain based on the final pseudo-synchronization segment index matrix and channel path mapping; multi-source monitoring data is received in an idle task period, and a cross-device time alignment model is established, so that the problem of time sequence dislocation caused by mechanisms such as cache delay and packet loss re-sending is effectively identified, and historical abnormal data is prevented from being misjudged as a future risk from the source.

The invention relates to the technical field of computer networks, and discloses a computing power network real-time performance evaluation method based on an analytic hierarchy process, and the method comprises the steps: collecting a node operation state and task demand data through a sensor, and generating a local performance index in combination with an edge quantum algorithm; simulating a future network state by using digital twinning, and fusing to generate a multi-dimensional performance data set; the AHP weight is dynamically adjusted based on resource deviation and a geological classification model, high-frequency updating is started for high load / fault, and the weight range is expanded for low load; introducing a risk assessment algorithm to quantify a performance-cost-carbon effect conflict level, and triggering resource recovery, optimization prompt or single index suggestion; scheduling strategies are triggered in a grading mode according to evaluation results, and active intervention is started in combination with anomaly detection; the AHP weight is dynamically updated through reinforcement learning, and quantum-classical hybrid algorithm parameters and block chain verification weight are automatically optimized. The real-time response efficiency and the resource utilization rate of the computing power network can be improved.

The invention provides a method for intelligently generating a complete dispatch report for financial unfavorable assets, relates to the field of management systems, and aims at deeply fusing multi-source heterogeneous data such as legal instruments and financial statements and forming a comprehensive context sensing model for target assets through multi-modal feature extraction, semantic alignment and knowledge graph construction technologies; a potential and non-dominant risk factor combination deeply coupled with asset characteristics is automatically mined by applying a genetic algorithm and other evolutionary calculation methods, and a self-adaptive risk assessment network is constructed to dynamically and quantitatively assess and predict the comprehensive risk level; carrying out attribution analysis on a risk assessment result by adopting an interpretable artificial intelligence model, and clearly revealing a key influence path and core data evidence; and according to a report logic framework and a narrative template which can be dynamically adjusted by a user, automatically outputting a financial non-performing asset full-duty survey report including deep analysis, risk early warning, diversified disposal suggestions and compliance review key points.

The invention relates to the field of bidding risk assessment, and discloses an AI technology-based intelligent bidding data analysis method and system and a storage medium, and the method comprises the following steps: obtaining bidding data, constructing a multi-dimensional space-time tensor, and extracting low-dimensional features through dynamic tensor decomposition; establishing a dynamic enterprise relationship graph, modeling an enterprise cooperation relationship by using a graph neural network, and capturing a bidding behavior mode between enterprises; a multi-target joint optimization model is constructed, and market trend prediction, enterprise credit assessment and risk early warning are synchronously realized; performing adversarial training by adopting a generative adversarial network, and dynamically adjusting task weights in combination with Pareto optimization to balance multi-task conflicts; and outputting a market prediction report, an enterprise credit portrait and a risk early warning list, and providing policy and regulation change influence analysis and a risk evidence chain. According to the method, bidding and tendering data are deeply mined through the AI technology, the market insight and the abnormal bidding behavior recognition capability are improved, and the method has wide application value.

The invention relates to a network public opinion intelligent classification and emergency decision-making system based on multi-modal fusion and dynamic evolution, and belongs to the field of network public opinion monitoring and big data analysis and artificial intelligence. The system comprises a multi-source data acquisition and preprocessing module used for crawling multi-modal data, constructing a propagation path map after preprocessing, and identifying key propagation nodes; the multi-dimensional classification engine module is used for carrying out conflict intensity quantification on public opinion events and dynamically updating a rule word bank to keep the adaptability of a conflict intensity quantification model; the event graph construction and anomaly detection module is used for constructing a public opinion propagation path and public opinion event generality logic chain mode, monitoring public opinion propagation speed and giving an alarm; the stakeholder dynamic risk assessment module is used for finely classifying network public opinion participants, providing a basis for differential propagation intervention and simulating public opinion evolution to carry out risk simulation; and the intelligent decision-making and emergency response module executes different levels of emergency measures based on the risk index according to the hierarchical response strategy.

The invention relates to the technical field of unmanned aerial vehicle control, in particular to an unmanned aerial vehicle autonomous obstacle avoidance decision-making method and system based on multi-source sensor fusion, and the method comprises the steps: achieving the time-space synchronization of a laser radar, a visual camera and a millimeter-wave radar through timestamp alignment and coordinate mapping, and constructing a dynamic obstacle grid map; fusing multi-source data based on a dynamic Bayesian network, and dynamically adjusting the confidence coefficient weight of the sensor in combination with the environment illumination intensity and the barrier surface material; and adopting a reinforcement learning model to generate an incremental obstacle avoidance strategy, and triggering a grading response instruction according to the risk assessment grade. The problem of fusion errors caused by spatial-temporal asynchronization of multi-source sensor data is solved, and the real-time obstacle avoidance success rate of dynamic obstacles is increased.

A chemical industrial park hazardous chemical substance vehicle real-time dynamic scheduling system based on multi-dimensional data fusion and intelligent decision-making comprises a vehicle monitoring module used for collecting dynamic information of transport vehicles in real time and monitoring the running state of each vehicle; the risk assessment module is used for calculating the current risk level of the transportation task based on the data collected in real time in combination with a risk assessment model; the path optimization module is used for calculating and adjusting an optimal driving path through a dynamic A * path planning algorithm; and the emergency response module is used for automatically starting when the high-risk condition is detected, so that the safety in the dangerous chemical transportation process is ensured. The invention aims to realize real-time and dynamic scheduling of the hazardous chemical vehicles in the chemical industrial park through multi-dimensional data fusion and intelligent algorithm design. By integrating multi-dimensional data of vehicles, goods, external environment, time, space and the like, the system can be ensured to automatically adjust a scheduling scheme in an emergency, and an efficient emergency response mechanism is provided.

The invention relates to a power transmission line forest fire risk assessment early warning system and method, and belongs to the technical field of power transmission line risk assessment. The system comprises an environment DNA power transmission line forest fire risk assessment and early warning system, a quantum sensing power transmission line fire remote monitoring system, a power transmission line forest fire early warning emergency training system, a digital twin power transmission line fire risk dynamic management system, and a multi-node power transmission line fire early warning system of an edge AI and a low orbit satellite. The system comprises a thermal imaging and deep learning integrated power transmission line fire risk distribution prediction system, an AI-driven power load dynamic adjustment and fire prevention system, an intelligent fire-fighting equipment linked power transmission line fire spreading path prediction system and a distributed quantum computing power transmission line fire emergency simulation and optimization response system. According to the method, the inflammable vegetation density and biocenosis change can be dynamically analyzed through a plant and microorganism gene sequencing technology.

The present application provides a method and system for intelligent monitoring and early warning of meteorological disasters in power grids based on GIS, the method comprising: obtaining historical multi-source data including power grid equipment data, meteorological data and equipment operation data; standardizing the historical multi-source data to obtain a four-dimensional data model including longitude, latitude, altitude and time dimensions; constructing a disaster risk assessment model based on the four-dimensional data model using a deep learning method; inputting real-time multi-source data into the disaster risk assessment model to obtain disaster risk assessment results; making early warning decisions based on the disaster risk assessment results to generate early warning information; and generating emergency response plans based on the early warning information. The system comprises a data processing module, a model building module, a risk assessment module, an early warning decision module and an emergency response module. The technical solution of the present application realizes the effective fusion of multi-source heterogeneous data, improves the accuracy and real-time performance of early warnings, and improves the efficiency of emergency response.

The invention relates to an intelligent management system, in particular to a building construction quality safety risk management system which comprises a data acquisition module, a convolution analysis module, a risk assessment module, an early warning decision module and a feedback optimization module. The data acquisition module generates a sensing signal. And the convolution analysis module receives and adopts a multi-scale feature pyramid and a cross-modal attention mechanism to process the sensing signal to form a four-dimensional fusion signal. And the risk assessment module carries out comparative analysis on the four-dimensional fusion signal under a digital twinborn framework and forms a five-dimensional risk early warning signal. The early warning decision module receives the risk early warning signal and forms a multi-level risk early warning level signal. And the feedback optimization module receives the risk early warning level signal and optimizes the building construction quality safety risk management system through an incremental learning technology. The building construction quality safety risk management system can solve the problems that the dynamic characteristic adaptability is low in the construction process, and misinformation or missing information is likely to be generated.

The invention discloses a data driving-based power battery safety risk assessment method and system, particularly relates to the technical field of battery safety monitoring, and is used for solving the problems of false alarm and missing detection caused by confusion of electrochemical relaxation effect and real fault signal characteristics in the existing standing stage fault detection method. Through multi-source data acquisition based on voltage, temperature and state of charge in a standing stage, a current slope extreme point is identified through charge and discharge mode weighted correction, a time-delay mapping relation with a voltage curve geometrical morphology abrupt change point is established, abnormal inflection points are screened, and fractal dimension attenuation characteristics are quantified; according to the method, abnormal distribution density and material damage indexes are fused to generate a dynamic threshold value, composite criterion analysis is carried out by combining frequency domain energy offset characteristics extracted by time-frequency transformation and heat conduction time-lag phase difference, relaxation interference signals are effectively stripped, hidden danger characteristics are locked, full-dimension safety state evaluation from microdefect evolution to macroscopic parameter abnormity is realized, and the safety state evaluation accuracy is improved. And the reliability of power battery fault detection under complex working conditions is improved.

The invention discloses a risk control credit monitoring method based on cloud computing, and belongs to the technical field of cloud computing, and the risk control credit monitoring method based on cloud computing comprises the following steps: S1, collecting user transaction data and behavior track data in real time; s2, cleaning and standardizing the data; s3, constructing a multi-dimensional risk assessment model based on machine learning; s4, dynamically generating a credit score according to the risk characteristics; s5, triggering an early warning mechanism for abnormal transactions in real time; and S6, generating a visual risk control report and updating a monitoring strategy. According to the method, multi-source heterogeneous data are integrated through federated learning, hierarchical privacy protection is realized in combination with homomorphic encryption and differential privacy, risk assessment real-time performance is improved by using a hybrid cloud resource scheduling and dynamic model updating technology, and a compliance audit closed loop is constructed based on a block chain and interpretability analysis.

The invention discloses an automatic code generation and optimization system based on multiple modes, which relates to the technical field of automatic programming, and comprises a task analysis module for analyzing task description and constraint conditions in combination with a CLIPS rule engine and a knowledge graph and original data, identifying task targets and requirements, and outputting a task risk assessment report and a task intention set; the optimization decision module is used for selecting an optimal modal data subset by using a particle swarm optimization algorithm and a path planning algorithm, performing optimization adjustment according to task requirements, and outputting a code generation strategy; and the test evaluation module is used for evaluating and improving the unit test and the integration test by utilizing the variation test, carrying out quality and performance evaluation on the code through continuous integration and continuous delivery, and outputting a code evaluation report and an optimization suggestion. According to the method, the optimal modal data subset is dynamically screened, and the data transmission path is optimized, so that the code performance is ensured, the computing resource consumption is reduced, and the global optimization of the code generation strategy is realized.

The present invention discloses a safety risk analysis system and method based on multimodal data processing; it relates to the field of industrial automation and information technology; the purpose of the present invention is to specifically target high-risk scenarios of hazardous chemical enterprises, and provide a safety risk analysis system and method based on multimodal data processing, which is based on deep learning technology, comprehensively considers the changes in spatiotemporal characteristics, can monitor and evaluate the safety risks of hazardous chemical enterprises in real time, and automatically integrates multimodal data such as monitored videos, sensor data, and operation logs. The spatial features are extracted through convolutional neural networks, and the time series data is processed using recurrent neural networks and long short-term memory networks, and further feature extraction and fusion are performed. The key risk factors are also identified through the attention mechanism, thereby achieving a comprehensive and accurate safety risk assessment, thereby providing real-time warnings for safety managers to ensure that potential safety hazards are discovered in a timely manner.

A system and method for multi-scale temporal attention processing in multimodal technology deep learning systems. This system processes time-series, textual, sentiment, and structured tabular data across three hierarchically-organized temporal streams—quarterly, weekly, and intraday levels—with bidirectional cross-temporal information flow. Scale-specific attention mechanisms are optimized for respective temporal granularities, while an adaptive controller dynamically weights each temporal level based on real-time market volatility indicators. A multi-scale fusion processor integrates attention-weighted representations to generate temporally unified representations preserving both short-term market dynamics and long-term trends. This approach enables superior forecasting and risk assessment by leveraging temporal correlations across multiple time scales while automatically adapting to changing market conditions. The system facilitates interpretable AI analysis through attention visualization and enables synthetic scenario generation for model testing.

The invention relates to the technical field of intelligent assessment and decision making, in particular to a robot dynamic risk assessment and decision making system and method based on multi-modal perception, and the system comprises a multi-modal sensor module which is used for collecting environment vision, acoustics, mechanics and position data in real time; the edge calculation unit is used for carrying out space-time alignment and feature fusion on the sensor data; the dynamic risk assessment model is used for integrating the environment uncertainty quantification module and the robot state prediction module based on a reinforcement learning framework; the decision execution interface is used for outputting a risk level and obstacle avoidance, speed reduction and shutdown instructions; by integrating visual, acoustic, mechanical and position multi-source sensor data and the like, the system can comprehensively capture various risk factors in a complex dynamic environment, so that the defect that a traditional single sensor system is insufficient in sensing dimension is overcome, and the system is particularly suitable for terrains and weather conditions with variable regions.

The invention relates to the technical field of data processing, and discloses a multi-dimensional financial pressure testing and financial early warning method and system. The method comprises the following steps: forming a financial situation awareness data set through multi-dimensional data acquisition integration and standardization processing; adaptive weight distribution and data dimension reduction are carried out on the five dimension indexes, and a risk measurement standard spectrum is output; a third-level pressure scene is established according to the standard spectrum, a risk conduction path is analyzed, and a financial pressure fluctuation spectrum is generated; a deep learning early warning model is constructed based on the fluctuation pedigree, an early warning threshold value is determined, and a risk pulsation graph is formed; constructing a multi-level early warning system based on the pulsating diagram, and establishing a financial early warning intelligent chain; and matching historical cases based on the early warning intelligent chain, designing a prevention and control strategy, and constructing a risk solution scheme library. According to the invention, by constructing a multi-level risk assessment system, accurate identification, dynamic monitoring and intelligent disposal of financial risks are realized, and the accuracy and timeliness of risk early warning are improved.

The invention relates to the technical field of safety certification, in particular to an electronic commerce platform safety certification system and method. The method comprises the following steps: acquiring platform login identity information, performing field migration and recombination, constructing an identity information tracing chain, performing multi-domain splitting on the tracing chain, generating a domain segmentation identity chain, performing identity risk assessment based on the multi-domain splitting, generating risk parameters, and performing parallel fusion authentication according to the risk parameters and the domain segmentation identity chain. The method comprises the steps of generating an initial security authentication result, logging in through a disguise platform, collecting login environment change data, verifying environment authenticity and performing security judgment to obtain login environment security data, performing platform login re-authentication by utilizing the security data and the initial authentication result, updating a login state and establishing a repeated authentication mechanism. According to the invention, a set of more comprehensive security protection system is formed through a multi-level security authentication strategy.