119 results about "Risk characteristics" patented technology

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 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 technical field of software engineering and automatic testing, and discloses a self-adaptive multi-dimensional risk testing strategy design method and system and a storage medium. The method comprises the following steps: triggering a risk assessment service through a CI / CD assembly line, preprocessing original data obtained by a plurality of heterogeneous data sources, and generating a risk feature vector; outputting a multi-dimensional risk index by using the risk assessment model, fusing the multi-dimensional risk index and the dynamic weight thereof based on an adaptive weighted fusion model, and calculating a comprehensive risk score; dynamically updating a risk score threshold value used for dividing the risk level, automatically generating a test strategy matched with the current risk level according to a preset mapping rule of the risk level and the test strategy, and calling the test suite to automatically execute the test strategy; and collecting a test execution result and online operation data as training data, and regularly optimizing parameters of the risk assessment model. The test strategy can be dynamically adjusted according to the real-time risk situation.

This invention provides a dynamic assessment method and system for cloud service intelligent operation and maintenance security risks based on artificial intelligence, belonging to the field of artificial intelligence technology. First, it captures all operational and maintenance data of cloud services in real time to form a data set covering various key data types. Then, it inputs the data set into a risk causal tracing model to mine the causal correlation between the data and historical security risk events, generating tracing results. Based on the tracing results, it drives the self-evolutionary adjustment of security risk assessment dimensions, generating a self-evolutionary assessment dimension set adapted to the current risk characteristics. Next, it inputs the data set and tracing results into this evolutionary assessment dimension set to simulate changes in the cloud service's operational state, obtaining risk evolution simulation results. Finally, based on the risk evolution simulation results and the current operational resource status, it generates a response strategy that coexists with the risk evolution trend and sends it to the operation and maintenance execution system. This invention can dynamically adapt to changes in cloud services, ensuring the stable and secure operation of cloud services.

This application relates to the field of network situational awareness technology, and discloses a method and device for network security situational awareness. The method includes: identifying regulatory entities and collecting abnormal events; identifying interactive and operational behaviors as risk characteristics; constructing a first risk identification model and deploying it on an edge terminal; collecting the type and sensitivity attributes of the regulatory entities; dynamically forming regulatory assistance pairs based on the attributes; generating a second risk identification model by weighted averaging of the parameters of the first model based on the regulatory assistance pairs and deploying it in the cloud; real-time interactive data is preferentially screened by the cloud-based second model; if a risk is identified, a structured report is generated and distributed; if not identified, it is reviewed by the first model at the edge terminal. This application achieves distributed intelligent perception with cross-domain collaboration and data not leaving the domain, solving the model bias and centralized delay problems caused by insufficient samples of a single entity, and significantly improving detection accuracy, response efficiency, and collaborative defense capabilities in low-sample scenarios.

The present application relates to the field of information technology services, and discloses a record product risk early warning method and system based on a component library, comprising: extracting a full component list of a record product from product record data to construct a component database corresponding to the full component list by using a preset component matching rule; establishing a component quick search index and a risk feature set of the component database to match potential risk components corresponding to the full component list in the component database; constructing a multi-dimensional risk portrait corresponding to the record product by calculating the compliance correlation degree of each component in the full component list and the potential risk components; and outputting a risk early warning result of the record product according to a risk assessment conclusion corresponding to the record product and the multi-dimensional risk portrait. The present application can greatly improve the initiative foresight and prevention accuracy of record product risk early warning.

This invention relates to the field of nursing technology and provides an internet-based remote monitoring method and system for health care. The method includes: screening for deviation events and identifying risk monitoring nodes, extracting sampling data only for these risk monitoring nodes; performing comparative analysis on computational parameters in chronological order, comparing the magnitude of parameter fluctuations with preset fluctuation thresholds based on historical data statistics, and identifying key parameters with significant fluctuations; and generating predictive intervention nodes through a preset assessment model. This invention avoids indiscriminate processing of all data by locating effective data segments with high-risk characteristics from the raw data, reducing computational load and data transmission pressure, and improving the targeting and efficiency of risk assessment. By identifying fluctuation nodes where key parameters change, it can more sensitively capture the initial signs of potential risks and improve the accuracy of risk identification.

The application relates to a kind of port entry and exit special goods risk identification methods based on knowledge graph construction and entity linking, comprising: according to the risk identification demand of port entry and exit special goods in advance, constructing hierarchical risk factor knowledge graph;Obtain the declaration material of risk to be identified, carry out entity extraction from the declaration material, retrieve historical similar cases according to the extracted entity, build enhanced context containing declaration material and its historical similar cases, and carry out overall vectorization processing;Adopt three-granularity matching strategy: first, through Top-K screening, to match special goods subclass with coarse granularity;Then through threshold filtering, to match risk factor with medium granularity, finally, fine-grained matching risk feature node, to obtain the risk identification result of port entry and exit special goods.Compared with the prior art, the present application has the advantages of more precise risk feature expression, explicit risk knowledge audit, and realizes accurate matching from declaration material to knowledge graph node.

The invention discloses a meat safety risk early warning model based on AI fast detection data and a service method, and relates to the field of food risk control. By extracting four types of risk features of time, space, category and circulation, an LSTM + DBSCAN + XGBoost fusion model is constructed, and the early warning advance period is longer than or equal to 24 hours; a regional thermodynamic diagram is output for the government, a batch prevention and control scheme is pushed for an enterprise, the supervision efficiency is improved by 90%, and the annual average loss of the enterprise is greater than or equal to 80,000 yuan. The method solves the problems of passive response, low data value and single service, realizes active prevention and control of meat safety, and is suitable for government supervision and enterprise quality control scenes.

The invention discloses a high plateau airport operator unsafe behavior monitoring and early warning method and device, and relates to the technical field of plateau operation monitoring. According to the method, algorithm such as YOLOv8 and feature fusion are combined to collect physical and behavior data from multiple sources and extract features, then a bimodal model is constructed by SVM and logistic regression, and a comprehensive fatigue level is generated by matching with an evidence theory, so that the limitation of single-modal data is avoided; meanwhile, a fuzzy Bayesian dynamic fusion model is introduced, and a fatigue score is corrected in combination with specific environmental data (low pressure, low temperature and the like) of the high plateau, so that risk judgment is more adaptive to physiological-environmental collaborative risk characteristics of the high plateau scene; and finally, the graded early warning is triggered based on the comprehensive risk grade of fatigue, environment and space dimensions, so that the accuracy of risk identification is realized, the timeliness of high-plateau operation safety management is improved through differentiated intervention, and the potential safety hazard of high-plateau operation is effectively reduced.

This invention relates to the field of AI-based risk control technology in financial payments, specifically to an AI-based real-time payment risk early warning method and system. The method includes: acquiring transaction behavior feature records containing standardized transaction amount ranges, transaction time slice distributions, and a relational graph of transaction participants; deconstructing the relational graph using a graph structure analyzer to extract potential indirect correlation paths between the transaction initiator and historical risk accounts, generating a risk correlation feature vector, and combining this with multi-dimensional behavioral analysis to obtain a behavioral deviation index; inputting the two types of features into a risk decision fusion model to obtain a comprehensive risk score; generating a dynamic risk threshold based on the risk level; triggering an early warning signal when the score exceeds the threshold, and generating an early warning instruction containing a risk cause code and interception suggestions. This method can uncover multi-level hidden risk correlations, dynamically adapt to the threshold settings for transaction risk levels, improve the completeness of risk feature extraction, and optimize the adaptation effect of risk early warning judgment.

The invention discloses a large language model-driven product quality risk early warning method, which relates to the field of product quality risk early warning, and comprises the following steps of: considering influence of product elements and production processing to extract element risk characteristics and personnel risk characteristics of a target product; in addition, a large language model is adopted to realize automatic conversion of non-standard data of after-sales quality abnormal events of reference similar products of a target product, external risk features are extracted, and then internal and external data features of a production management system are fused to perform quality risk prediction. And the feature weight vector used when the features are fused is obtained based on a ridge regression model, so that compared with a common linear regression algorithm, the quality prediction early warning capability is remarkably improved while high precision is maintained, and the accuracy, the reliability and the real-time performance of predicting the product quality risk by the method are relatively good.

The invention discloses a tax risk identification method and system based on multi-source data comparison, and the method comprises the steps: collecting the multi-source data of a target subject and an associated subject from a tax, invoice and commercial credit investigation system, and constructing a data set; extracting time series data of a preset key risk index in a preset observation time period, calculating a volatility and a deviation degree, and generating a dynamic risk feature vector representing each subject risk condition; constructing a risk conduction network based on the association relationship between the target subject and the association subject; calculating the time sequence correlation of the dynamic risk feature vectors respectively corresponding to the target main body node and the associated main body node in a specific time window; when the correlation coefficient exceeds a preset threshold value, it is judged that risk collaboration exists, the associated subjects corresponding to the associated subject nodes are marked as high-risk clusters, and direct associated tax risks are evaluated. According to the invention, dynamic accurate identification and quantitative prevention and control of tax risks are realized, and accuracy and efficiency are improved.

The application discloses a pre-examination abnormal registration group detection method based on a community discovery algorithm. A graph structure for representing the association relationship between examinees is constructed, then the examinees are divided into communities or non-community individuals based on the graph structure, and then the graph structure is subjected to message passing to generate the embedded representation corresponding to the examinees, and whether there is a risk is predicted according to the embedded representation corresponding to the examinees, so as to determine the confidence degree corresponding to the examinees and the preset label, and then generate a risk score for the examinees. Then, the high-risk examinee characteristics are counted based on the risk score, so that the high-risk label marking and dyeing of the high-risk examinee nodes are performed, and the label propagation of the high-risk label in the graph structure is performed. Through the method, the risk characteristics can be counted, the risk label can be marked and propagated based on the graph structure, so that the examinee group that may have risks can be determined in a big data manner before the examination, and the efficiency and accuracy of the risk prediction of the examinees before the examination are improved.

The invention relates to the technical field of gas safety, and discloses an indoor gas leakage risk management and control system and method based on coupling analysis. According to the system, instantaneous gas flow time sequence data, pipeline completion and design data and environment monitoring data are fused, a gas pipeline is divided into continuous equal-length gas pipe sections, and related data are extracted; analyzing and extracting internal damage risk characteristics and external damage risk characteristics of the gas pipe section based on the acquired data, performing coupling analysis to obtain a coupling damage risk value of the gas pipe section, and evaluating a fire risk level of the gas pipe section based on the gas pipeline data; comprehensively coupling the damage risk and the fire risk level, and generating a risk management and control scheme of all the gas pipe sections; according to the invention, quantitative evaluation and precise management and control of the risk of the indoor gas pipeline are realized, traditional passive response is converted into active prevention, and the intelligent level of gas safety management is significantly improved.

The application discloses a risk control method for business process review, and belongs to the technical field of process review, and specifically comprises the following steps: acquiring all node information of a target business process, determining risk dimensions of the business process review according to compliance requirements of a field to which the business process belongs; the risk dimensions comprise node operation permission matching degree, process data transmission compliance, and inter-node business logic coherence; for each risk dimension, risk judgment rules corresponding to each risk dimension are established based on characteristic parameters; operation data generated by each node of the target business process during operation is collected in real time; the collected operation data is substituted into the risk judgment rules corresponding to each risk dimension respectively, risk dimensions and node positions corresponding to operation data that do not conform to the risk judgment rules are marked, a preset risk treatment strategy library under the risk dimensions is called, and a risk treatment suggestion corresponding to a current risk characteristic is matched from the risk treatment strategy library.

The invention discloses a bidding and tendering risk value calculation method and system based on deep learning, and relates to the technical field of data mining and risk assessment crossing. A weighted analysis and time sequence change identification method is adopted to carry out risk influence degree importance evaluation and dynamic change trend identification of the space-time dimension on the bidding and tendering risk time sequence data set to form a time sequence risk change result, and the time sequence risk change result is fused with the multi-dimensional risk feature vector to construct a fusion risk feature matrix; performing risk relevancy calculation on the fusion risk feature matrix by using an adaptive optimization algorithm to generate a comprehensive risk index set; and according to the comprehensive risk index set, carrying out risk level mapping through principal component risk compression and clustering mode identification, and generating a bidding risk assessment report. According to the invention, by generating the bidding and tendering risk assessment report, dimension reduction optimization of complex risk features and automatic identification of a high-risk mode are realized, and the accuracy and interpretability of risk assessment are enhanced.

The invention discloses a distributed photovoltaic cluster operation risk assessment method based on multi-dimensional observability, and relates to the technical field of risk assessment, and the method comprises the following steps: obtaining an environment interval score according to the obtained environment data and baseline height of a photovoltaic array, and obtaining an environment factor score in combination with fault data; converting the environmental factor score into an environmental risk weight, and weighting the environmental data to obtain a first risk influence value; correcting the first risk influence value according to the fault severity of the environmental factors at different baseline heights to obtain a second risk influence value, wherein the fault severity is obtained according to the fault frequency and fault loss of the fault data; constructing a position risk association degree and an equipment environment association degree according to the second risk influence value and the equipment health state data, and obtaining a cluster risk feature in combination with the equipment operation state data; and evaluating the cluster risk characteristics to obtain the distributed photovoltaic cluster operation risk. The method is used for solving the problems that existing photovoltaic cluster risk assessment is one-sided in angle and low in precision.

The invention discloses a supply chain ESG risk dynamic assessment method and system based on multi-source data fusion, and the method comprises the following steps: 1, collecting multi-source heterogeneous data at each node of a supply chain, processing the data, and generating a node data set containing ESG risk features; 2, based on the node data set generated in the step 1, in combination with attribute parameters of supply chain nodes and external dynamic information, adaptively generating a dynamic evaluation benchmark; and step 3, on the basis of the node data set generated in the step 1 and the dynamic assessment benchmark generated in the step 2, constructing a risk conduction model to carry out real-time risk assessment, and outputting an assessment result containing a risk conduction path. And multi-source implicit information is fused by combining a space and semantic association technology, so that the objectivity and comprehensiveness of an evaluation basis are ensured.

The embodiment of the application provides a risk processing model training method, a risk object processing method and related devices, which can perform selection operation on risk assessment feature information on a risk feature selection page, select at least one target risk assessment feature information, that is, the embodiment of the application presents the risk assessment feature information in a visual form, can quickly select the target risk assessment feature information, determines reference risk processing rule information based on the target risk assessment feature information, and predicts risk processing rule information, thereby accelerating the training of the risk processing model, and improving the development efficiency.

The application provides a dynamic risk early warning method and system, the method comprises the following steps: collecting multi-mode heterogeneous data and preprocessing the multi-mode heterogeneous data; determining a risk event vector corresponding to the same event based on the preprocessed multi-mode heterogeneous data; embedding and constraining the first type of language data in the risk event vector using a knowledge graph to generate target embedding features aligned with the risk features of the second type of language data; determining the attention weights of the multi-dimensional risk features in the risk event vector and the embedding features; determining the comprehensive risk score of the risk event by combining the multi-dimensional risk features and the corresponding attention weights; inputting the comprehensive risk score into a pre-constructed sequence prediction model to obtain the comprehensive risk score trend of the corresponding data in the future T time windows; determining the risk escalation probability by combining the comprehensive risk score trend; and performing corresponding early warning based on the comprehensive risk score trend and the risk escalation probability.

This invention discloses a method for monitoring the storage of hazardous chemicals in a production area, relating to the field of hazardous chemical storage technology. The method includes the following steps: S1, collecting the actual ambient temperature sequence of each grid in the production area; S2, determining the basic volatility coefficient, and generating risk characteristics for each grid at each time point based on the temperature sensitivity coefficient and environmental correction coefficient, as a risk characteristic sequence; S3, obtaining the predicted temperature value of the grid at the next time point. This invention is based on the first-order kinetic law of hazardous chemical volatilization, combined with corrections to actual grid parameters, and generates basic volatilization, temperature sensitivity, and environmental correction coefficients for parameters such as sealing and ventilation of each grid. Based on these parameters, a time-series risk characteristic sequence is generated, which is then fused with the temperature sequence for deep learning prediction, allowing for early detection of temperature change trends and reserving a time window for risk management, significantly improving storage safety.

The present application relates to the field of intelligent transportation systems and road safety technology, in particular to a risk real-time evaluation method and system for merging area and diverging area in human-machine mixed driving environment, comprising the following steps: S1, real-time collection of multi-traffic data of merging area and diverging area, S2, real-time calculation and output of risk feature set based on multi-traffic data; S3, based on the risk feature set, through a dynamic weighted risk fusion model, real-time calculation and output of a comprehensive risk value representing the current regional safety situation; the dynamic weighted risk fusion model dynamically adjusts the fusion weight of each risk feature according to the real-time traffic state parameter; S4, according to the risk level of the comprehensive risk value, generating and issuing a customized intervention instruction matched with the driving demand of the merging area or diverging area; meeting the real-time requirement of highway risk response, realizing accurate real-time risk evaluation in human-machine mixed driving scene.

This invention discloses an e-commerce order behavior risk assessment system based on IoT coding, comprising: a data acquisition module for collecting and preprocessing data to construct an order data sequence; a unified coding module for performing entity identification and unified coding processing to generate an object coding sequence; an association mapping module for performing association propagation analysis and relationship mapping processing to construct an order association graph; a risk propagation module for performing risk impact analysis and risk diffusion identification processing on the order association graph to generate a risk association sequence; an anomaly analysis module for performing abnormal behavior analysis on the risk association sequence to construct a risk feature sequence; and a risk assessment module for generating an order risk assessment result based on the risk feature sequence. This invention utilizes IoT unified coding and other technologies, possessing advantages such as strong association tracking capabilities, high accuracy in risk identification, high efficiency in anomaly detection, and high stability in risk assessment.

The invention provides a risk assessment method and a storage medium, relates to the technical field of aviation, and is used for assessing the risk of aircraft navigation. The method comprises the following steps: acquiring aviation data of an aircraft; feature extraction is carried out on the aviation data to obtain aviation risk features, the aviation risk features comprise an over-limit risk feature, an aircraft state control feature and an environment risk feature, and the over-limit risk feature is used for reflecting the degree that aircraft operation parameters of the aircraft approach or exceed a preset navigation safety threshold value; the aircraft state control feature is used for reflecting the aircraft navigation control stability degree of a pilot, and the environment risk feature is used for reflecting the risk degree of the external environment of the aircraft. Based on the aviation risk characteristics, an aviation risk assessment result is determined, and the aviation risk assessment result is used for indicating the risk degree of the safety accident of the aircraft.

The application provides a food safety monitoring method and system based on big data, comprising constructing alliance chain nodes in a food supply chain, uploading full life cycle data to a distributed ledger of a block chain for notarization to obtain traceability notarization data; establishing a risk factor correlation graph based on a time-space dimension, extracting a risk feature vector; based on a dynamic game model, monitoring the risk feature vector in real time, and judging whether an abnormal risk signal appears; if yes, triggering an early warning logic, the early warning logic combining a multi-party game mixed strategy Nash equilibrium solution to calculate an optimal early warning threshold under a current monitoring environment, and generating a response strategy; based on the optimal early warning threshold and the response strategy, calculating a safety risk index, and sending different levels of early warnings to relevant parties according to the safety risk index. The application can identify implicit risks that cannot be found by traditional threshold alarms, significantly improving the accuracy and forward-looking nature of risk early warning.

The application relates to the technical field of road monitoring, and discloses a road risk multi-source perception and hierarchical early warning method under severe weather, which comprises the following steps: collecting meteorological data and road state data of each road area in real time and carrying out pretreatment; establishing a multi-source data fusion model based on the pretreated meteorological data and road state data, carrying out data interpolation and supplement by using the multi-source data fusion model, and extracting key risk characteristic factors; determining the comprehensive risk score of each road area according to the key risk characteristic factors, and determining the early warning level of each road area according to the comprehensive risk score; generating a protection strategy according to the early warning level and the key risk characteristic factors, and controlling the working state of the corresponding physical protection facilities based on the protection strategy. The application reduces the influence of severe weather on road traffic safety and improves the safety and reliability of road operation.

The embodiment of the invention provides an information interception method and device, and relates to the technical field of network security, the method comprises the following steps: responding to received service information, the service information comprising a first risk assessment feature; the first risk assessment feature is compared with each second risk assessment feature recorded in a risk feature library, the risk level of the business information is determined according to a comparison result, and each second risk assessment feature is obtained according to sending end reputation data fed back by a cloud information security service; and when the risk level represents that the service information is risk information, intercepting the service information. By applying the scheme provided by the embodiment of the invention, communication resources consumed by the electronic equipment in an information interception scene can be reduced.

The invention relates to the technical field of intelligent traffic systems and road safety, in particular to a real-time risk assessment method and system for a confluence area and a diversion area in a man-machine mixed driving environment, and the method comprises the following steps: S1, collecting the multivariate traffic data of the confluence area and the diversion area in real time, and S2, calculating and outputting a risk feature set in real time based on the multivariate traffic data; s3, based on the risk feature set, through a dynamically weighted risk fusion model, calculating and outputting a comprehensive risk value representing the security situation of the current area in real time; the dynamically weighted risk fusion model dynamically adjusts the fusion weight of each risk feature according to the real-time traffic state parameters; s4, according to the risk level of the comprehensive risk value, generating and issuing a customized intervention instruction matched with the driving demand of the confluence area or the diversion area; the real-time requirement of the expressway on risk response is met, and accurate real-time risk assessment in a man-machine mixed driving scene is realized.

This specification provides a risk control rule mining method, device, storage medium, and program product. The risk control rule mining method includes: acquiring a sample set containing multiple samples, each sample including the value of at least one risk feature variable and associated risk labels; binning each risk feature variable according to binning parameters and the value of the risk feature variable in the sample set, and performing statistical analysis based on the distribution of risk labels associated with samples in each bin to obtain binning feature information of the current binning method; constructing a first prompt word based on preset binning decision knowledge, semantic information of risk feature variables, and binning feature information; inputting the first prompt word into a language model to enable the language model to perform binning rationality analysis, outputting binning parameters for the next binning process when the current binning method is determined to be unreasonable, or outputting risk control rules for the risk feature variables when the current binning method is determined to be reasonable.