58 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 a business risk management method and device, a computer program product and electronic equipment. The method relates to the field of artificial intelligence and big data, and comprises the following steps: obtaining business data, and encoding the business data to obtain a business vector; the business vector is input into a target model to obtain a risk prediction level, the target model comprises an input layer, a hidden layer, an attention mechanism layer and an output layer, the input layer is used for extracting a state vector from the business vector, the hidden layer is used for extracting hidden features from the state vector, the attention mechanism layer is used for calculating an attention weight, and the output layer is used for outputting the attention weight; the target feature is determined through the hidden feature and the attention weight, the attention weight is determined by the similarity between the business risk index and the hidden feature and the position code of the hidden feature, and the output layer outputs a risk prediction level based on the target feature; and determining a target risk management strategy through the risk prediction level. Through the method and the device, the problem of low business risk management efficiency in related technologies is solved.

The invention discloses an export enterprise risk prediction method and system based on Transform, relates to the field of data processing, is applied to an enterprise risk control system, and comprises the following steps: constructing a multi-dimensional index system, and dynamically distributing index weights by adopting a subjective and objective fused multi-target combination weighting model; designing a Transform risk prediction model, capturing dynamic association of risk indexes through a space-time attention mechanism, and training the risk prediction model in combination with historical risk evaluation index data; establishing a weight comparison correction mechanism, comparing a combined weighting result with a model prediction value, and optimizing weight distribution through error feedback; and establishing a risk early warning linkage mechanism of a regional level by combining a corrected weighting result, and triggering graded early warning when an enterprise risk value exceeds a threshold value, thereby realizing accurate prediction and collaborative prevention and control of export enterprise risk indexes.

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 provides a commercial risk assessment management method and device and electronic equipment, and belongs to the technical field of financial science and technology. The method comprises the following steps: collecting data sources from different channels, wherein the data sources comprise historical transaction records, market information, financial statements and social media feedback; defining a preset feature index according to the business demand, and constructing a risk assessment model by applying a machine learning algorithm; performing risk assessment on the data source by using the risk assessment model, calculating the numerical value of each risk index and generating a comprehensive risk score; and setting a risk level threshold, and triggering an early warning mechanism when the comprehensive risk score exceeds the risk level threshold. Through fusion of multi-source heterogeneous data and a machine learning algorithm, full-process intelligent evaluation and dynamic early warning of commercial risks are realized, and accuracy, response speed and decision support capability of risk identification are improved.

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 a cross-regional electric power spot market electricity purchase risk intelligent early warning method, device and equipment, and relates to the technical field of electric power market and power grid operation safety, and the method comprises the following steps: extracting risk index data influencing inter-provincial spot electricity purchase, and forming a risk index data set; constructing a dynamic weighted risk conduction network based on the risk index data set; inputting any risk index in the risk index data set into the risk conduction network for simulation deduction, and predicting risk intensity probability distribution of each node in the future; and based on the risk intensity probability distribution, outputting a strategic early warning instruction through the optimization decision model. According to the method, the defects that in the prior art, risk cross-region conduction is difficult to quantify, early warning is lagged and performability is lacked are overcome, and accurate prediction, advanced perception and active defense of inter-province market risks are achieved.

The invention discloses a credit assessment method and device for market risk indicator analysis and a medium, and the method comprises the steps: collecting Internet public data and national government affair platform public data, and carrying out the preprocessing of the collected data, and generating a structured data set; processing financial time series data through a long-short-term memory network to generate a price fluctuation risk index, analyzing an enterprise association relationship through a graph neural network to generate a credit infection risk index, and analyzing text data through a natural language processing model to generate a market emotion risk index; an XGBoost integrator is adopted to fuse the price fluctuation risk index, the credit infection risk index and the market emotion risk index, and a comprehensive market risk index is generated; and performing fuzzy matching on the subject name to be checked and the subject name of the market data in the structured data set through a hybrid editing distance algorithm, and outputting a credit assessment report based on a matching result and the comprehensive market risk index.

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 discloses a long-distance water diversion project operation risk assessment method, and the method comprises the steps: recognizing risk factors based on big data through employing a text mining method, carrying out the processing of unstructured data in historical operation scheduling data through employing an ICTCLAS system and an Ultratrain-release tool, and constructing a long-distance water diversion project operation risk index system; integrating a COPRAS operator by adopting a DEMATEL-ISM method to define a nonlinear association mechanism among risk factors under a random dimension, constructing a risk association network hierarchical structure, and determining a key risk; the fuzzy theory and the dynamic Bayesian network method are coupled to construct a risk association evaluation model with the time-varying reasoning capability, and the time-varying rule of the risk dynamic association characteristics and the risk situation is quantitatively analyzed. The objectivity and comprehensiveness of risk identification are improved, a risk association mechanism is clarified, and a risk situation dynamic rule is revealed.

This invention presents a multi-factor method and system for assessing the operational risks of AC / DC hybrid power grids. It constructs a time-series probability distribution model of renewable energy output. Based on this model, it establishes indicators for grid topology vulnerability, AC / DC interaction, dynamic operational risk, economic risk, and commutation failure risk in the AC / DC receiving-end grid. Considering grid node failure events, AC line failure events, voltage limit exceeding events, grid economic downturn events, and commutation failure events in the AC / DC receiving-end grid, the risk assessment matrix constructed based on these five indicators is weighted and standardized. The standardized risk assessment matrix is ​​then dimensionality-reduced, and its contribution rate vector is calculated. Finally, the comprehensive operational risk value is calculated based on this contribution rate vector. This invention enables a systematic assessment of multi-dimensional indicators, accurately quantifying the comprehensive risk level of the power grid.

The present invention belongs to the field of data processing technology, and specifically relates to a supplier management method and system based on risk assessment. The method comprises: obtaining a supplier's own risk indicators based on the supplier's quality risk, delivery risk, financial risk, and compliance risk; constructing a risk propagation directed graph based on the upstream and downstream relationships between suppliers, thereby obtaining the risk propagation index between any two suppliers; and further introducing an ant colony algorithm to construct a procurement path. During each iteration, the ant colony algorithm comprehensively considers the potential supplier's own risk index, the risk propagation index from existing nodes in the current path to the potential supplier, and the distribution of risk and cost-oriented pheromones in historical paths, thereby dynamically calculating the selection probability of each potential supplier and guiding the ants to gradually construct an optimal path that meets procurement needs. The present invention improves the objectivity and accuracy of risk identification and enhances the enterprise's early warning capabilities for systemic risks.

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 disclosed data generation platform enables generation of an output in response to an output generation request based on tuning a routing model that enables model selection in a dynamic, system-sensitive manner. For example, the disclosed data generation platform receives an output generation request for a user device and generates a risk indicator associated with the output generation request. The platform can determine a current system state and generate a set of performance indicators and associated weighting values based on the risk indicator and the system state. The data generation platform can select a first routing model based on the weighting values. The data generation platform can provide the output generation request to the first routing model to generate an indication of a model with which to generate a model output responsive to the input. The data generation platform can enable access to the generated model output.