38 results about "Risk indicators" patented technology

The invention provides a quantitative transaction strategy dynamic generation method and system based on a large model, and a storage medium. The method comprises the following steps: S1, collecting market data, related financial news and information of financial products; s2, inputting market data, financial news and information into a large language model, analyzing current market environment characteristics, and intelligently selecting a matched transaction strategy type through a strategy generator; s3, performing simulated transaction verification on the generated strategies by using historical market data, and selecting the strategy with the optimal performance from a plurality of candidate strategies through risk assessment, parameter optimization and strategy screening; s4, deploying the strategy passing the verification of the large model to a transaction execution system, monitoring the execution effect, the market environment and the risk index of the strategy, and dynamically adjusting the strategy parameters or regenerating the strategy according to the monitoring result; and S5, a feedback learning step. The method has the advantages that multiple strategy types are integrated, and diversity and innovativeness of strategies are improved.

The invention discloses an unplanned operation early warning method and system fused with multi-source data mining. According to the method, vehicle tracks, operation plans and electronic fence data are collected, cleaning and map matching preprocessing are carried out on the tracks, and then track sequences are mapped into low-dimensional embedded vectors through a track representation learning model; recognizing an abnormal behavior mode through clustering based on the vector, and constructing a dynamic risk index and predicting the future state of the vehicle in combination with real-time data; inputting the abnormal mode, the risk index and the prediction result into a fuzzy logic decision maker for multi-source fusion, outputting a comprehensive risk level and triggering graded early warning; a reinforcement learning mechanism is adopted to dynamically optimize decision maker parameters according to the early warning effect, and finally early warning information is pushed to a management platform and feedback is received to form closed-loop management. According to the invention, accurate and adaptive early warning of unplanned operation behaviors is realized, and the intelligent level of operation safety supervision is significantly improved.

The invention discloses a power distribution network risk assessment method for massive distributed resource access, and the method comprises the following steps: dividing a risk domain according to the related information of a power distribution network, carrying out the stratified sampling of the risk domain, carrying out the Monte Carlo simulation, obtaining a sample matrix of risk indexes, inputting a random forest model, and calculating the importance score of the risk indexes, screening key risk indexes based on the importance threshold; taking the importance score as an objective weight, calculating a subjective weight by using an analytic hierarchy process, calculating a difference degree according to the subjective and objective weights, and fusing the subjective and objective weights to obtain a comprehensive weight; and obtaining a key risk index characteristic value in a to-be-evaluated scene, calculating the membership degree of the key risk index after adjusting the boundary parameter of the membership degree function according to the related information of the to-be-evaluated scene, calculating the comprehensive risk index according to the comprehensive weight and the membership degree of the key risk index, and judging the risk level. The method can fully consider the topological characteristics of the power distribution network, is suitable for the dynamic access of distributed resources, and achieves the precise risk assessment.

The invention discloses an automatic driving scene risk assessment method fusing a diffusion model and a random forest, and belongs to the technical field of automatic driving control. The problems of early recognition and early warning of potential risks of automatic driving are solved. A diffusion model is improved on the basis of a traffic scene state, a condition variable is introduced to obtain a condition generation distribution model, and a traffic scene sample data set is generated; setting the minimum collision time and the minimum time headway as key risk indexes, and generating a risk label for each generated traffic scene sample; constructing a continuous risk quantitative index meeting a functional safety standard as exposure degree and severity, and constructing a training data set; a random forest model is adopted to construct a risk assessment model, the training data set is utilized to train the risk assessment model, the training target is to learn the mapping relation between the exposure degree and severity and the risk label, and the trained risk assessment model is obtained; and evaluating the performance of the risk evaluation model by adopting a multi-dimensional evaluation index system to realize automatic driving scene risk evaluation.

The invention discloses an intelligent early warning method and device for anti-electricity fraud and a medium, and relates to the technical field of data processing methods based on financial purposes. The method comprises the following steps: acquiring multi-source data from a plurality of heterogeneous data sources, and performing data treatment on the multi-source data to generate a standardized risk data set; the multi-source data comprises a real-time data stream, a quasi-real-time data stream and an offline data stream, and the data management comprises data splicing, data cleaning and data labeling processing; based on the standardized risk data set, executing index calculation through a calculation engine to generate a plurality of risk indexes, and inputting the risk indexes into a multi-modal risk decision engine to execute expert rule decision and machine learning model decision in parallel to generate a risk early warning signal; generating a risk check list based on the risk early warning signal, and issuing the risk check list to a specified mechanism to execute a corresponding disposal action; the processing actions comprise transaction blocking, account management and control and verification issuing.

A system can generate a risk assessment associated with an identity element used in interactions associated with a user entity. For example, the system can receive historical data related an identity element associated with a user entity, the identity element used in a set of interactions associated with the user entity. The system can generate a binomial distribution of the historical data associated with the identity element. The system can determine, based at least in part on the binomial distribution of the historical data, a risk indicator associated with the identity element. The system can control, based at least in part on the risk indicator associated with the identity element, an interaction involving a target entity and the user entity using the identity element.

The invention discloses a power grid enterprise emergency plan system assessment method and system, a medium and equipment, and belongs to the technical field of emergency plan system assessment, and the power grid enterprise emergency plan system assessment method comprises the steps: constructing an emergency plan assessment index system; key risk indexes are identified through a fuzzy Delphi method; carrying out dynamic weight distribution on the key risk indexes based on a Bayesian best worst method; a fuzzy comprehensive evaluation method is combined with the maximum membership degree principle, and comprehensive evaluation is carried out on the emergency plan system; constructing a comprehensive risk early warning model, and calculating an early warning value of each index and an overall early warning level; and outputting an evaluation result and an early warning level. According to the method, comprehensiveness, systematicness, feasibility and practicability of emergency plan system evaluation indexes can be ensured, and emergency capability construction of power grid enterprises can be objectively and effectively evaluated.

A method is provided. The method comprises: receiving user information comprising one or more identifiers associated with a user, wherein the one or more identifiers indicates a phone number associated with a user device; based on the user information, querying an internal data management system and an external data management system to determine previous interactions of the user; determining one or more calculated risk metrics associated with the user based on comparing the first previous interactions and the second previous interactions with one or more thresholds; generating a dynamic identity data request for the user based on the one or more calculated risk metrics; in response to providing the dynamic identity request to the user device, receiving, from the user device, dynamic identity data for the user; based on the dynamic identity data, authorizing the user access to one or more services.

A system can generate a risk assessment associated with a target entity. The system can access a request for a risk indicator associated with a target entity. The system can also access a model in a test environment and run an ATP tool on the model. The ATP tool can execute a set of deployment steps using the model and determine a completion status of each of the set of deployment steps. The system can deploy the model to a production environment when the set of deployment steps is successful. The system can determine the risk indicator using the model. The system can transmit, to a remote computing device, a responsive message comprising at least the risk indicator to control access of the target entity to one or more interactive computing environments.

The invention discloses a rescue scene-oriented unmanned aerial vehicle flight path planning method and device, and relates to the technical field of low-altitude unmanned aerial vehicles. The method comprises the following steps: constructing an airspace risk index system, analyzing and processing each index in the airspace risk index system, and determining a key risk index; performing airspace risk quantitative evaluation based on the key risk indexes to obtain airspace risk factors; based on the airspace risk factors, determining an airspace model; constructing an unmanned aerial vehicle route planning model based on the airspace model; and performing flight path planning based on the unmanned aerial vehicle flight path planning model to obtain a flight path planning result. According to the invention, the trajectory planning efficiency and flight safety of the unmanned aerial vehicle are improved.

The invention relates to the technical field of data analysis, and particularly discloses an enterprise authentication data analysis method and system, and the method comprises the following steps: S1, collecting historical data, and preliminarily dividing a green index and a risk index; s2, marking the enterprises with the same products or services as the enterprises as companion enterprises, setting a proportion range threshold according to the market share proportions of the enterprises, screening out companion enterprises with the market share proportions within the threshold range, and marking the companion enterprises as reference enterprises; in a set judgment period, collecting authentication data of all reference enterprises, extracting industry reference indexes of the reference enterprises, and calculating industry reference values of all green indexes; s3, obtaining an industry reference value, calculating a difference value between the current industry reference value and a historical reference value, and if the difference value exceeds a preset error range threshold value, re-marking all green indexes as risk indexes; otherwise, calculating a fluctuation value, and setting a sampling inspection proportion according to the fluctuation value; and S4, checking the sampling index and the risk index.

The invention relates to application of a digital twinning technology in the aspect of risk monitoring of chemical production equipment, and discloses a real-time risk monitoring and early warning system for isocyanate production equipment, the classification number is G06Q or G06F, and the keyword is digital twinning. The system comprises a sensor data acquisition module, a data preprocessing module, a digital twinborn simulation module, a risk assessment module, an early warning decision module and an intervention control module. According to the method, the equipment state is predicted through the digital twinborn model, the risk index is calculated, the early warning is generated, and pre-intervention is realized; the method is characterized in that the digital twinning technology is integrated, excessive dependence on sensor precision is reduced, the problems of uncertainty and hysteresis of early warning of an existing system are solved, the technical effect of improving early warning accuracy, timeliness and safety is achieved, and the method is suitable for chemical production risk monitoring.

A system can efficiently determine whether an identity is manipulated. The system can receive entity data and interaction data associated with a target entity. The system can determine, based on the entity data and the interaction data, one or more risk signals associated with the target entity using one or more artificial intelligence models. The system can generate a linked graph structure based on a first graph structure and a second graph structure each generated using the entity data and the interaction data. The system can apply the one or more risk signals to the linked graph structure to determine a risk indicator associated with the target entity. The system can provide a responsive message based on the risk indicator. The responsive message can be used to control access of the target entity to an interactive computing environment.

The invention relates to the technical field of electric power cabinet operation and maintenance, and provides an electric power cabinet inspection personnel scheduling platform. Comprising a power cabinet operation state risk analysis module, a power cabinet operation state judgment module, a power cabinet inspection task determination module and the like. According to the method, risk parameters of the power cabinet are monitored and analyzed to generate risk indexes of all dimensions; generating a power cabinet operation state risk coefficient and various types of risk signals according to the risk index of each dimension; screening out power cabinets which are not well operated based on the power cabinet operation state risk coefficients; based on each type of risk signal, determining an inspection task corresponding to the power cabinet in poor operation; based on the inspection path reference predicted arrival time of the optimal inspection personnel and the first and second predicted arrival time correction factors, screening the corresponding optimal inspection path; on-demand inspection and accurate scheduling of the optimal inspection personnel and the optimal inspection path are realized, the inspection efficiency is improved, and the power supply stability is guaranteed.

A method and a system for analyzing a transportation network in a logistic supply chain, the method including acquiring unstructured information from plural information sources and information on the transportation network. Analyzing the unstructured information utilizing a large language model for generating structured information. Analyzing historical information in the structured information and identifies correlations between events and disruptions in the transportation network for determining risks metrics. Generating a dynamic graph comprising nodes and edges, and determining critical transportation routes and critical links of the transportation network by performing a spectral analysis of the dynamic graph utilizing the determined risk metrics. Minimizing a risk of the transportation network by adjusting transportation routes based on the critical transportation routes and the critical links and based on the risk metrics until a termination criterion is met. Generating and outputting an analysis signal including information on the adjusted transportation routes of the transportation network.

The invention discloses a project management system development method based on low code configuration, relates to the technical field of computer software, and remarkably improves the prediction adaptability and practicability of a project management system in a new project type through a low code configuration and meta-learning fused architecture. A low-code interface allows non-technical users to intuitively configure project parameters, such as a construction period or a risk index, so that a system customization threshold is reduced, and enterprises can quickly respond to innovative project requirements without depending on professional development teams; the meta-learning module uses general knowledge of a pre-training model to complete model adjustment only with a small amount of new data, and overcomes the defect of weak generalization ability of a traditional model in a data sparse scene; the few-sample adaptive mechanism is optimized through internal and external loops, so that the model is ensured to be fast converged while keeping stability, and the training time and resource consumption are reduced; the verification process in the deployment stage enhances the reliability of the system, and the dynamic consistency verification can detect the output deviation of the model in time.

The invention belongs to the technical field of enterprise management consultation, and particularly relates to an enterprise management consultation system based on big data, which comprises a data acquisition and integration module, a risk index system construction module, a real-time risk monitoring and early warning module, an intervention scheme generation and optimization module, an effect tracking and feedback module and a user interaction module. All the modules work cooperatively. According to the invention, a data acquisition and integration module, a risk index system construction module, a real-time risk monitoring and early warning module, an intervention scheme generation and optimization module, an effect tracking and feedback module and a user interaction module are arranged; the enterprise management consultation system based on the big data solves the problems that in traditional enterprise management consultation, the data collection range is narrow, the processing efficiency is low, risk identification is not systematic, early warning lags behind, an intervention scheme lacks scientificity, and the effect is difficult to track and evaluate, and the enterprise management consultation system based on the big data can achieve enterprise management risk full-process management and control.

A system (120) and method (400) for artificial intelligence impact assessment with threat modelling for responsible artificial intelligence requirements is disclosed. The system (120) comprises a business requirement module (315) for capturing objectives, a responsible artificial intelligence threat modelling module (320) for identifying risks, and a compliance module (325) for generating questionnaires and initiating data protection impact assessments. An automatic regulatory requirement module (330) maps risk to global Al regulations, while a technical safeguard recommendation module (335) provides dynamic mitigation strategies and session summaries. A human-in-the-loop module (340) enables expert validation, and a workflow orchestration module (350) structures collaborative reviews. A permission workflow module (345) assigns risk scores and authorizes deployment. A dashboard module (355) offers real-time monitoring of assessments, compliance status, approvals, and risk metrics, ensuring accelerated, accurate, and safe Al impact assessment aligned with responsible Al principles.

The invention discloses a human insurance company operation management risk control method and system, and mainly relates to the technical field of financial risk management and control. Comprising the following steps: extracting original data, cleaning and standardizing the original data, and constructing a multi-dimensional basic data matrix; based on the multi-dimensional basic data matrix, calculating scores of various preset risk indexes, and obtaining a comprehensive risk rating of the company according to a preset weight; identifying potential fraud and abnormal behaviors based on the multi-dimensional basic data matrix, and utilizing a graph database technology to match a predefined abnormal business relation mode through graph query to output a key risk clue; and according to the comprehensive risk rating and the key risk clues, pushing the risk disposal task to a corresponding responsibility unit and carrying out tracking investigation and rectification feedback. The method has the beneficial effects that full-process automatic closed-loop management and control of human insurance company operation management risks from data integration, intelligent identification and comprehensive evaluation to early warning treatment is realized.

In some aspects, a computing system can train a machine-learning model to analyze a graph database for risk assessment. The computing system can use the machine-learning model to identify a risk indicator for a target component of one or more interactive computing environments. The graph database can include a set of nodes where each node represents a respective infrastructure service of one or more infrastructure services and a set of edges connecting individual nodes of the set of nodes. The computing system can generate the risk indicator for the target component based on an output of the machine-learning model The computing system additionally can output a graphical user interface including at least the risk indicator for use in controlling access to the one or more infrastructure services.

Methods, systems, and non-transitory computer readable storage media are disclosed for facilitating modification of components of artificial intelligence computing applications via aggregated risk scores of individual artificial intelligence computing application components. The disclosed system generates data objects representing the artificial intelligence computing application components. The disclosed system determines mappings between the data objects based on relationships of the artificial intelligence computing application components. Furthermore, the disclosed system generates risk scores for the data objects representing the components of the artificial intelligence computing application by administering risk assessments that correspond to a system requirements framework. The disclosed system also generates an interactive aggregated risk indicator indicating contributions of the components to the artificial intelligence computing application in relation to the system requirements framework by combining the risk scores of the data objects according to the mappings between the data objects.

The invention discloses a network service quality risk index extraction method and device, equipment and a storage medium. According to the scheme, for a target service link hierarchical structure, service quality indexes of all service hierarchies are obtained, risk analysis is carried out, and risk index mapping data of all the service quality indexes are determined. And constructing a directed acyclic graph containing the service quality indexes and the risk indexes according to the risk index mapping data of each service quality index. And determining the risk influence value of each service quality index based on the directed acyclic graph and a preset risk influence value calculation model. And carrying out risk index analysis according to the risk influence value corresponding to each service quality index, and determining a service quality risk index of the target service. Through full-chain business hierarchical modeling and graph structure dynamic evaluation, a complete process from index acquisition to risk index extraction is realized, key risk indexes highly associated with business scenes are accurately acquired, and risk evaluation pertinence and actual effectiveness of index extraction are greatly improved.

The application discloses a method for quality prediction of tinplate cans based on iron ion migration amount, which comprises the following steps: sealing and soaking the tinplate cans filled with acetic acid, and performing temperature acceleration test; sampling on the third day of the temperature acceleration test, and detecting the iron ion migration amount; sampling on the tenth day of the temperature acceleration test, and detecting the iron ion migration amount; and predicting the quality of the tinplate cans based on the iron ion migration amounts on the third day and the tenth day. The application takes the migration amount on the third day as a short-term risk indicator, and takes the migration amount on the tenth day as a key quality determination standard of long-term stability indicator. Based on the detection results of the two time points, the iron ion migration trend of the tinplate cans within a six-month shelf period can be reliably predicted, the sensory quality and stability of beer products can be ensured, clear and quantitative delivery access conditions of suppliers can be set according to the detection results, the risk of beer quality deterioration caused by quality problems of packaging materials can be avoided from the source, early prediction and precise control of the quality of packaging materials are realized, and the overall quality management level is improved.

The invention belongs to the technical field of energy collection, and provides a deep and far sea energy collection and conveying method and system based on a three-core low-temperature energy pipeline. Liquefied natural gas, liquid carbon dioxide and liquid hydrogen are conveyed in an integrated mode through the three-core low-temperature energy pipeline, and the real-time monitoring environment and phase state parameters are combined; according to the closed-loop control method, the saturation pressure difference serves as a key risk index for state trend prediction, a PID controller is used for generating the flow adjustment amount, and multi-medium collaborative optimization is carried out. The problems that in the prior art, multiple low-temperature media cannot be safely and efficiently conveyed in a single pipeline, and the phase state stability of the media is difficult to maintain under the severe sea condition of deep and far seas are effectively solved, so that the conveying efficiency is remarkably improved, and the operation and maintenance cost is reduced.

The invention discloses a power grid project investment execution risk dynamic early warning method, system, device and medium, and relates to the technical field of power grid project investment risk early warning, and the method comprises the steps: obtaining multi-source project management data of a full life cycle of a power grid project, and carrying out the data preprocessing; performing real-time dynamic benchmarking and risk measurement and calculation based on the preprocessed multi-source project management data to obtain the risk level of the risk index; performing multi-level aggregation based on the risk levels of the risk indexes to obtain an overall risk assessment result of the project; and performing visual risk early warning according to the overall risk assessment result of the project and the key influence factors. The problems of risk identification lagging and poor early warning timeliness caused by the fact that a conventional power grid project investment risk early warning technology cannot dynamically track the risk formation process and attribution and the evaluation dimension is single are solved, and the risk identification accuracy and the early warning timeliness are improved.

The invention discloses an early warning method and system based on project management risk control and computer equipment. The method comprises the following steps: acquiring multi-source real-time data integrated by an API (Application Program Interface), and calculating project-level and center-level key risk indexes; giving different weights according to the importance of the key risk indexes, analyzing and adjusting the weights by using historical data, and performing standardized scoring based on an actual value or a predicted value to obtain a scoring result; calculating a comprehensive risk score according to the scoring result, and converting the comprehensive risk score into an early warning level; according to the early warning level, triggering early warning of a corresponding level in combination with a plurality of scenes, and analyzing unstructured data to find potential risks; a visual chart is adopted to display key risk index distribution, and drilling analysis is carried out to position a problem root and a mode to find and recognize similar risk groups. By implementing the method provided by the invention, the accuracy and timeliness of risk identification are improved, and scientization and standardization of risk management decisions are promoted.

This invention discloses a method, apparatus, equipment, and storage medium for risk early warning of power contractors, belonging to the technical field of power contractor risk early warning. The invention includes four steps: Step 1: generating a risk profile of the power contractor; Step 2: quantitatively assessing the power contractor's risk resistance capability; Step 3: calculating the relative risk value of the power contractor enterprise; Step 4: based on pre-set thresholds as early warning conditions, issuing risk warnings to power contractors reaching high-risk and medium-risk levels, and outputting the relative risk value and indicator information for the warning; no warning is issued for low-risk contractors. This invention automates the entire process from data collection and classification of power contractor risk activities, generation of profile indicators, quantitative calculation and assessment, relative risk value calculation, and risk early warning. The entire process is based entirely on data-driven decision-making to achieve risk quantification and early warning, fully considering the relative numerical assessment of risk indicators and risk resistance capabilities, thus improving the accuracy of risk early warning.

The invention belongs to the technical field of data analysis, and discloses a supply chain risk assessment system and method based on big data, and the system comprises a data collection and processing module which collects multi-dimensional supply chain data from nodes of a supply chain, carries out the format standardization and time alignment of the collected multi-dimensional supply chain data, generates a unified supply chain state data set, and carries out the data collection and processing; extracting multi-dimensional supply chain features of the nodes; the risk index construction module is used for fusing the multi-dimensional supply chain features of the nodes to form a unified risk representation vector, calculating node risk related indexes, and generating comprehensive risk features of the nodes through weighted integration and feature importance evaluation of various features; the risk prediction simulation module is used for constructing a supply chain network according to the supply dependency relationship among the nodes and dynamically simulating risk flow by combining the comprehensive risk characteristics of the nodes; the precision and efficiency of supply chain risk early warning are improved, and the potential impact of systematic risks on enterprise operation is reduced.

The application discloses an unstructured prompt word optimization scoring method for an intelligent credit audit assistant. The method comprises collecting unstructured credit audit text of an enterprise, wherein the credit audit text comprises credit audit materials, due diligence reports and post-loan inspection document data; the collected credit audit text is blocked and indexed to construct an intelligent audit vector knowledge base; a large language model is used to analyze the credit audit text, generate an audit opinion on the credit of a client, and give an enterprise risk score; the enterprise risk score is associated with an actual default risk index to construct a loss function for measuring the difference between the two, the large model prompt is regarded as a training parameter, and the prompt is iteratively optimized through back propagation; and the enterprise risk score is visually displayed through a visual means. The application can effectively solve the subjective problem in risk measurement, improve the performance of the model in tasks without standard answers, and provide a more reliable basis for intelligent credit audit.

There is herein described a method of running an application on a cloud platform, the method comprising transmitting a syscall from the application to the cloud platform, performing a comparison of a risk indicator associated with the syscall to a risk tolerance threshold associated with the cloud platform, where the risk indicator is a measure of a level of risk presented by the syscall and the risk tolerance threshold is a measure of a maximum level of risk acceptable to the cloud platform, allowing or preventing the syscall from being processed by the cloud platform depending on an outcome of the comparison.