288 results about "Risk characteristics" patented technology

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 relates to the technical field of data security management, in particular to a self-adaptive data security management and risk early warning system based on intelligent analysis under a cloud platform. Comprising a multi-dimensional data acquisition module; an intelligent analysis module; a self-adaptive strategy generation module; a risk early warning module; and a user behavior portrait construction module. In the design, the security policy can be dynamically adjusted along with the risk situation of the cloud platform, the problem that a static policy cannot adapt to real-time change is solved, and dynamic mapping of risk characteristics-policy parameters is realized; according to the design, the one-sidedness of single-dimension analysis is broken through, multi-modal feature association modeling of user behaviors is achieved, an abnormal behavior triggering threshold value is accurately recognized, and the integrity and accuracy of risk feature analysis are improved; the security policy can be continuously optimized through historical event data, so that protection efficiency attenuation caused by long-term static operation is avoided, and an autonomous lifting link of data driving, algorithm optimization and policy evolution is realized.

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 a rock slope risk assessment method and system based on artificial intelligence, and belongs to the technical field of geological disaster risk assessment.The rock slope risk assessment method comprises the steps that a slope three-dimensional digital model is constructed, and geological-environment-monitoring data are integrated; extracting spatio-temporal characteristics, and predicting a landslide risk probability; quantifying a risk space diffusion path, and identifying a high-risk area; the early warning threshold value is dynamically adjusted, and accurate early warning is achieved; the problems that in the prior art, how to integrate multi-modal data (geological data, environment data and monitoring data) to achieve real-time sensing of the slope state and how to construct a deep learning model with high generalization ability to deal with risk prediction under the complex geological condition are solved. The problem of how to establish a risk transfer model considering spatial heterogeneity to improve high-risk area identification precision and how to realize dynamic adaptive adjustment of an early warning threshold to match personalized risk features of different slopes is solved.

The invention relates to an infection risk assessment method and system for nursing, and the method comprises the steps: collecting multi-source heterogeneous data of a patient, and fusing the multi-source heterogeneous data into a unified original data set; obtaining a standardized risk feature vector based on the original data set; inputting the risk feature vectors into a multi-modal risk assessment model, and outputting risk probabilities of individual infection and group infection; forming a personalized risk index based on the risk probability; according to the personalized risk indexes, the decision support system outputs hierarchical intervention measures matched with the risk levels, and records and feeds back execution conditions and intervention effect data of the hierarchical intervention measures for dynamically updating parameters of the risk assessment model; and integrating the intervention effect data and the early warning signal into feedback information to form a dynamic infection risk assessment system adapted to specific hospital characteristics. The accuracy of infection risk assessment of nursing can be improved.

The invention discloses a project performance evaluation and risk early warning method for multi-source heterogeneous scientific research data, and relates to the technical field of scientific research project management, and the method comprises the steps: collecting the multi-source heterogeneous scientific research data, cleaning, converting, filling missing values, and storing in a unified format; extracting performance and risk features, and fusing to obtain a fused feature set; combining an analytic hierarchy process, an entropy weight method and the like to construct a dynamic weight performance evaluation model; constructing a multi-modal risk early warning model by adopting multiple algorithms and optimizing a threshold value; collecting data in real time to update a feature set, and dynamically adjusting the model; and visually outputting a result, generating an improvement suggestion, and forming closed-loop feedback. According to the method, the multi-source data processing efficiency and evaluation accuracy are improved, the risk early warning timeliness and adaptability are enhanced, a management closed loop is formed, and the problems of difficult data integration, evaluation lagging, insufficient early warning and the like of a traditional method are solved.

The invention discloses an AI-based database operating system risk prevention and control system and method, belongs to the technical field of deep learning, and aims to solve the technical problem of how to comprehensively identify, prevent and control database operation risks and improve data management efficiency. Comprising a data processing module for performing data preprocessing on historical operation logs; the risk identification module is used for carrying out grammar and semantic analysis on the SQL statements in the processed operation logs, constructing risk characteristics of the SQL statements, carrying out risk prediction through a trained risk identification model and outputting risk levels; the risk response module is used for carrying out risk warning level by level and carrying out classification and priority ranking on the risk warning based on the Bayesian network; and the user analysis module is used for monitoring user behaviors in real time based on the user portrait and an anomaly detection algorithm, forming alarm information based on a detection result and pushing the alarm information.

The invention relates to the technical field of computers, particularly discloses an engineering investment project multi-dimensional risk dynamic assessment and early warning system, and aims to solve the problems that existing risk assessment is single in dimension, insufficient in timeliness and lack of dynamic early warning and intelligent decision support. The system comprises a data acquisition and preprocessing module, a multi-dimensional risk feature construction module, a dynamic risk assessment and prediction module, a risk early warning and visualization module and an intelligent decision support and optimization module. Through integration of multivariate data, machine learning and deep learning algorithms, risk dynamic modeling, real-time evaluation and trend prediction are realized, and in combination with intelligent early warning and decision support, risk management is changed from post-remedy to beforehand prevention.

The invention discloses a specific scene safety risk identification method based on visual dynamic analysis, and belongs to the technical field of safety monitoring, and the method specifically comprises the steps: collecting environment parameter time sequence data and cargo state time sequence data of a monitoring region; respectively extracting an environment characteristic component and a cargo response component; calculating a quantized value of correlation strength of the two through an information entropy algorithm, and marking the quantized value as a cargo response entropy value; a cargo response entropy evolution curve is monitored, and when the change rate of the evolution curve exceeds a historical threshold value and the change acceleration is not zero, it is judged that a state transition event occurs; extracting environmental incentive parameters and cargo response parameters of a state transition event, encoding the environmental incentive parameters and the cargo response parameters into risk feature tensors, inputting the risk feature tensors into the accident incentive chain knowledge base, outputting a matched critical intervention parameter set, and formulating an environmental reconstruction instruction set and a cargo intervention instruction set according to the critical intervention parameter set; according to the invention, the intelligent level and risk response capability of dangerous cargo safety early warning are effectively improved.

The invention discloses an engineering safety early warning method and system based on artificial intelligence real-time risk identification, and relates to the technical field of engineering safety, and the method comprises the steps: collecting scattered engineering safety data from each engineering platform in batches, and carrying out the preprocessing of the data, and constructing a dynamic database; according to the engineering safety data collected in batches, an engineering safety knowledge graph is constructed, and different risk levels are preset according to engineering safety standards. According to the method, multi-source engineering safety data are integrated, dynamically changing risk characteristics are analyzed in real time by using an AI risk identification model, the hysteresis of traditional manual inspection and static analysis is overcome, a nonlinear relationship among the risk characteristics is captured by using a random forest model through integrated learning of multiple decision trees, and the risk characteristics are analyzed in real time. And the probability values of high, medium and low risk levels are output in combination with Softmax probability normalization, so that the evaluation precision is remarkably improved, the risk features are positioned, the scientificity of risk traceability is ensured, and data-driven decision support is provided for engineering safety management.

The invention relates to the technical field of electric power, in particular to an electric power circuit alarm method and system with an AI function, and the method comprises the steps: collecting multi-source data, and carrying out the time-space calibration of the information of the multi-source data through a Kalman filtering algorithm; constructing a multi-physics field coupling prediction model, and calculating standardized scores of different risk types; different types of risk feature weights are calculated by using a quantum-graph attention network fusion model, and fault severity scores corresponding to different types of risks are calculated through a migration reinforcement learning fault diagnosis model; constructing a comprehensive risk value calculation formula by combining the standardized scores of the different risk types, the feature weights of the different types of risks and the fault severity scores corresponding to the different types of risks, and calculating a comprehensive risk value; and carrying out grade division on the risk according to a threshold interval in which the comprehensive risk value is located, and pushing different early warning information and processing strategies to different responsible personnel according to different risk grades. According to the scheme, accurate early warning is realized through multi-source data space-time calibration and AI fusion.

The invention relates to the technical field of information security, in particular to a network security risk assessment method and system based on fuzzy mathematics, and the method comprises the following steps: based on dynamic monitoring data of node data in a network environment, extracting multiple risk factor parameters, dividing a parameter value range, comparing risk level intervals, and carrying out the statistics of a differentiation interval distribution proportion. And generating a network risk factor membership distribution table. According to the method, network node data are dynamically monitored, multiple risk factors are accurately analyzed, attribution range division is refined, the distribution relation between parameters and risk levels is defined, the distribution proportion and weight influence are analyzed, an interactive risk distribution model is formed, factor relevance is revealed, a significant characteristic group is screened, and the time dimension change rule is combined, so that the risk level of the network is determined. And key risk feature data is extracted, the dynamic monitoring capability is enhanced, the accuracy, comprehensiveness and dynamic adaptability of risk assessment are improved, the conversion from static analysis to real-time response is realized, and complex network threats are effectively dealt with.

The invention provides a risk early warning method and system for medical insurance settlement data based on a knowledge graph, and relates to the technical field of medical insurance settlement, and the method comprises the steps: formulating a risk rule, obtaining the medical insurance settlement data, extracting a first entity and a relation thereof from the data, constructing the knowledge graph, and storing the knowledge graph in a graph database; and searching the second entity according with the risk rule and the relationship thereof in the graph database by utilizing the reasoning ability of the knowledge graph, extracting the risk characteristics of the second entity, determining the risk level of the second entity according to the risk characteristics, and sending a corresponding early warning signal, thereby improving the recognition rate of the complex associated risk in the process of medical insurance settlement.

The invention relates to the technical field of customer relationship management, and discloses an information data analysis method and system based on CRM. An information data analysis method based on CRM comprises the following steps: S1, collecting user original information data based on CRM, and screening out target users with loss risks according to the user original information data; s2, extracting customer loss risk features from the original information data of the target users, importing the customer loss risk features into the trained risk assessment calculation model, and outputting results to indicate loss risk levels of the target users; and S3, generating a reservation scheme according to the loss risk level of the target user and the corresponding original information data, and outputting the reservation scheme to the corresponding target user. According to the invention, grading intervention is carried out on the target users based on different risk grades, a uniform retention strategy is prevented from being adopted for all users, and the retention rate of clients is improved.

The invention relates to the technical field of laboratory safety management, and discloses an AI model fused laboratory safety management risk intelligent pre-judgment method and system. The method comprises the steps of collecting original monitoring data of a laboratory monitoring area, and extracting risk signals and abnormal signals corresponding to abnormal events from the original monitoring data; obtaining relevance characteristics between the risk signals and adjustment characteristics of the risk signals by abnormal events; risk features representing risk differences are generated according to the features, and target risk probability distribution is obtained based on the risk features; and performing interference suppression processing on the original monitoring data according to the target risk probability distribution to separate a prediction risk waveform of the target risk source. According to the method, through deep mining of the relevance of risk signals and the adjustment effect of abnormal events, the accuracy of risk pre-judgment is improved, potential risks can be effectively captured, and a more intelligent and more reliable risk pre-judgment means is provided for laboratory safety management.

The invention relates to the technical field of engineering safety management, and particularly discloses an engineer safety management method and system based on big data, and the method comprises the steps: collecting all data of a construction site in real time; performing space-time alignment and fusion processing on the collected data to generate multi-modal fusion data; training a multi-modal risk assessment model based on the obtained construction big data; performing construction risk feature extraction on the collected data based on the multi-modal risk assessment model, and outputting construction risk features; and performing safety response analysis on the multi-modal fusion data based on the construction risk features, and generating a safety response result. All data of a construction site are fused, and a multi-modal data set is constructed, so that full-dimensional monitoring of the construction site is realized; a big data training model is matched based on construction types, and space-time correlation analysis of risk features is achieved; a differentiated early warning mechanism for personnel, equipment and environment is generated, multi-agent co-processing is supported, and accident loss is reduced.

The invention provides an engineering dynamic risk assessment early warning system and method, and belongs to the technical field of engineering risk assessment. According to the method, engineering stages are divided, a stage label incidence matrix is constructed, historical cases are collected, an engineering stage-risk feature incidence matrix is constructed, a weight dynamic adjustment mechanism is set, construction data are collected in real time, the current stage is identified, risk features are extracted, risk weights are automatically adjusted, and dynamic risk comprehensive scores are calculated. And then the preset risk threshold is automatically called to divide the risk early warning level, so that scientific and dynamic assessment and early warning of the engineering risk are realized, core risk characteristics of different engineering stages can be accurately identified, the risk weight is dynamically adjusted according to real-time data, the risk assessment is enabled to better fit the actual construction condition, the risk early warning level is timely and accurately divided, and the risk early warning efficiency is improved. Powerful support is provided for engineering safety management, and the probability of occurrence of engineering accidents is effectively reduced.

The invention belongs to the technical field of mine safety mixed reality interactive training, and particularly provides a mine safety training detection method and system based on mixed reality, and the method mainly comprises the steps: obtaining a mine basic data set, and generating enhanced safety constraint boundary data according to the mine basic data set; generating environmental risk features according to the enhanced security constraint boundary data; generating security behavior features according to the enhanced security constraint boundary data and the environmental risk features; generating risk evolution characteristics according to the environmental risk characteristics and the safety behavior characteristics; generating emergency response characteristics according to the safety behavior characteristics and the risk evolution characteristics; and generating a dynamic risk visualization picture according to the risk evolution characteristics and the emergency response characteristics. According to the method, training scene dynamic, operation behavior compliance detection automation and risk evolution process visualization are realized, and safety training effectiveness and emergency processing capability are improved.

The invention relates to the technical field of computer systems, and particularly discloses a data resource migration risk prediction method and system, terminal equipment and a storage medium, and the method comprises the steps: respectively constructing explicit dependency features and implicit dependency features of various resources; comprehensive dependency characteristics of various types of resources are obtained, and then the association strength between the resources is calculated; constructing a dynamic semantic association graph based on the association strength among the resources, and updating the affected sub-graphs in response to resource change and operation log update; on the basis of the topological features of the dynamic semantic association graph and the risk features of the resources, in combination with the association strength between the resources, obtaining neighborhood aggregation features of the resources; and calculating a risk score of data resource migration by using the neighborhood aggregation feature after feature enhancement. According to the method, map topological features and resource features are deeply fused, so that risk assessment is changed from qualitative judgment to quantitative prediction, and quantitative assessment of dependency intensity and risks is realized.

The invention provides an insurance service real-time risk early warning method and system under dynamic risk assessment. The method comprises the steps of obtaining multi-modal risk data including time, space and industry dimensions in an insurance service; separating a core risk element region from the multi-modal risk data through a mask mechanism, and generating an initial risk feature map with a mask mark; inputting the initial risk feature map into a dynamic risk assessment network of an encoder-decoder architecture; wherein both the encoder and the decoder comprise multi-stage cascaded risk processing modules, and each risk processing module is integrated with a gating loop unit and is configured with a binary risk gate for dynamically controlling an update path of risk characteristics; and generating a real-time early warning signal by combining a preset risk level threshold based on the target risk characteristics output by the decoder. According to the invention, the processing path of the risk features can be dynamically adjusted, and the timeliness and accuracy of risk early warning are improved.

The invention relates to the technical field of intelligent auxiliary approval, in particular to a collaborative process intelligent auxiliary approval method and system based on dynamic risk modeling, and the method comprises the steps: obtaining a delay factor of each approval role type node according to the difference between all approval durations of each approval role type node in all structure trees and a reference approval duration; obtaining an overall delay factor of each approval task according to the delay factor and the weight coefficient; mapping all approval tasks in the history in the risk feature space to obtain a plurality of data points; clustering all data points in the risk feature space to obtain a plurality of class clusters; and according to the distance between each point to be analyzed and the center points of all the class clusters and the distance between the center points of the class clusters and the original point, obtaining an abnormal risk factor of each point to be analyzed so as to obtain a priority, and performing approval through the priority. According to the invention, the efficiency and fluency of intelligent auxiliary approval are improved.

The invention provides an engineering safety risk management method and system. The method comprises the steps that target vectors are determined based on field engineering collection data, and the target vectors at least comprise field engineering feature elements and field risk feature elements; and based on the target vector and the safety risk model, determining early warning information related to the engineering safety risk and sending the early warning information to the user. Through the method, causal relationships between various risk factors and accidents can be comprehensively considered, and automatic risk factor comprehensive analysis is realized, so that the generation process of the risk early warning result is faster and more accurate, and related personnel can conveniently and effectively process the risk factors subsequently.

The invention discloses a user big data processing method based on supply chain finance, and the method comprises the steps: collecting user interaction data, extracting short-term time sequence and long-term behavior features to construct an interaction data network, selecting risk-related features, initializing the weight, and carrying out the training adjustment; calculating a user preference attenuation coefficient according to a transaction time interval, segmenting behavior characteristics, identifying behavior and preference changes of each stage, evaluating risk influences and adjusting weights; constructing an interaction data operation chain based on the data network, and adjusting behavior change rates of the user in different stages; and finally, constructing a risk conduction rule and marshalling according to historical risk characteristics, and determining a key behavior change rate. Through multi-dimensional feature analysis and behavior change rate calculation, user dynamics are accurately described, potential risk association is mined, an evaluation model is constructed in combination with historical data, risk signals can be captured in time, risk identification accuracy and timeliness are improved, and platform risk management is assisted.

The invention relates to the technical field of risk prevention and control, in particular to an electric power engineering construction site safety management method and system, and the method comprises the following steps: analyzing the spatial relation between constructors and high-voltage cable laying points based on the positioning and operation paths of the constructors, screening and adjusting path nodes, and judging the distribution of abnormal resident personnel; and dynamically evaluating the working condition risk level by combining the regional personnel density and the meteorological change, and adjusting the risk level of the operation region. According to the invention, through fusion of space positioning, path behaviors, risk monitoring and meteorological data, dynamic capture of operation behaviors and environment changes, combination of path space optimization and resident behavior screening, active perception of a space interaction relationship between personnel and a risk area, linkage of personnel distribution and extreme meteorology, continuous identification and linkage judgment of risk characteristics are realized, and the risk characteristics can be identified more accurately. And the partition risk levels are updated in real time according to operation behaviors and environmental conditions, and the field risk data linkage breadth and depth are enhanced.

The invention relates to the technical field of fire detection, and provides an intelligent sensing multifunctional integrated sensor for fire. The sensor realizes synchronous real-time monitoring of a plurality of risk characteristics of a target area through a plurality of sensing monitoring modules triggered by a weight-based composite event, and when any sensor monitoring module monitors that the target area has the risk characteristics and the corresponding characteristic value reaches a preset characteristic threshold value, the risk characteristics of the target area can be monitored on the basis of different weight mechanisms. The dynamic risk weight of the target area is calculated through the feature dynamic weights of different sensing monitoring modules, and risk dynamic grading early warning is carried out on the target area; in the process of monitoring the target area by the sensing monitoring module, the feature dynamic weight of the sensing monitoring module dynamically changes along with the change of the feature value of the monitored risk feature, so that the fault-tolerant capability of the target area during risk early warning is improved, and the problems of false alarm and missing alarm caused by the single monitoring function of the existing sensor are effectively solved.

The invention discloses a risk assessment method and device based on a trusted data space and related equipment. The method comprises the steps of obtaining a digital contract, participant information, transaction data, environment data and data space operation data of the trusted data space; carrying out anchor point hash calculation to determine whether tampering is carried out; obtaining a plurality of risk indexes and risk characteristic values; and based on the global weight and the risk feature value of each risk index, obtaining the size of each risk level preset by the digital contract. According to the method, whether the data information is tampered or not can be quickly determined through anchor point hash calculation, changes of multi-source heterogeneous data such as digital contracts, participant information, transaction data, environment data and data space operation data can be determined in real time by utilizing the preset large language model, and risk judgment is made in real time. In addition, when the risk is judged based on the risk characteristic value of each risk index, the importance degree of each risk index in the global data contract is considered, so that the judgment is better.

The invention discloses a tunnel construction safety risk management and control method and system. The method comprises the following steps: collecting multi-source sensor data of a construction site, constructing a continuous time sequence monitoring data set, introducing a Hampel model to detect abrupt change and abnormity and identify sudden risk features, and introducing a CUSUM model to detect a slow change trend and extract evolutionary risk features. Time synchronization and vector combination are carried out on the two types of risk features, a joint risk feature matrix is constructed, dominant risk types are further judged, dynamic weighted fusion is implemented, a fusion risk index is generated, risk levels are divided according to the fusion risk index value, and the risk level is determined by combining the dominant risk types and the risk trend. And an informatization risk response result including a response level, a response strategy and trend prediction is generated, and accurate identification and intelligent management and control of tunnel construction risks are realized. The invention is suitable for dynamic risk early warning and management of a tunnel engineering construction site, and belongs to the technical field of tunnel engineering safety management.

The invention relates to the field of exploration and development data processing, and discloses an oil and gas reservoir exploration and development risk prediction method and device, which can construct a normalized adjacency matrix, a node feature matrix and a high-dimensional time sequence matrix according to parameter values of a plurality of production related parameters in an oil and gas well in a target time period. And inputting the normalized adjacency matrix and the node feature matrix into a trained graph neural network for risk prediction to obtain a risk feature matrix and a first risk score corresponding to each production related parameter. And inputting the risk feature matrix and the high-dimensional time sequence matrix into a trained multivariable time sequence processing model for risk prediction to obtain a second risk score corresponding to each production related parameter. And determining the risk grade of the oil and gas well based on the first risk score and the second risk score corresponding to each production related parameter. The accuracy of oil and gas well risk grade prediction can be effectively improved.

The invention relates to the technical field of risk identification, in particular to an intelligent automobile operation high-risk scene identification method and device based on deep learning and a storage medium, and the method comprises the steps: collecting multi-modal data of an intelligent automobile operation scene; determining the space-time evolution of the risk features based on the fused feature sequence, and updating the global risk feature map based on the space-time evolution; and determining a risk mark of the operation scene based on the updated global risk characteristic spectrum, and distinguishing a static risk region and a dynamic risk region in the risk mark based on the unupdated global risk characteristic spectrum and the fusion characteristic sequence to obtain a high-risk candidate region corresponding to the operation scene. According to the method, the multi-modal data is collected in real time, and the data is fused and coded in combination with deep learning, so that a high-risk scene in the operation of the intelligent automobile can be accurately identified, and potential risks can be dynamically evaluated and predicted, thereby providing more accurate decision support for the intelligent automobile and avoiding possible accidents.

The invention discloses a dam hidden danger risk grade assessment method. The method comprises the following steps: S1, acquiring detection data by adopting a comprehensive geophysical detection technology; s2, performing mileage checking and standard batch processing on the acquired detection data; s3, acquiring spatial feature information of hidden dangers; s4, acquiring physical parameter information of different mileage hidden dangers of the dam profile and rock-soil parameter information of the dam structure, and establishing a relation criterion between physical parameters and rock-soil parameters; s5, establishing a hidden danger risk characteristic evaluation index data system; s6, establishing a multi-source information fusion neural network model; and S7, inputting hidden danger risk characteristic evaluation index data into the model, and calculating risk grade division results of different mileage positions of the dam. According to the method, the conversion from the dam hidden danger to the hidden danger risk is realized, the hidden danger interpretation and risk assessment model is established, the dam risk grade mileage map and the dam profile map of the corresponding risk mileage are drawn based on the model result, and the grading of the dam risk is realized.