109 results about "Risk indicators" patented technology

The invention discloses a digital economic risk assessment method and device based on computer statistics, and the method comprises the steps: S1, collecting enterprise economic data and market environment real-time information data, and carrying out the data cleaning; s2, dividing enterprise cycles according to the enterprise economic data in S1; s3, extracting enterprise internal stage features and market features according to the enterprise cycle in S2; s4, combining the enterprise internal stage features and the market features to construct an enterprise periodic knowledge graph; s5, defining risk indexes according to the enterprise internal stage features and the market features; s6, constructing a risk assessment model in combination with the enterprise periodic knowledge graph and the risk indexes; s7, analyzing a risk propagation path and evaluating an influence degree through a knowledge graph; s8, carrying out risk early warning according to a risk assessment result, and proposing countermeasures and suggestions in combination with a risk propagation path and an influence range; risk assessment is carried out through enterprise economic data and market environment real-time information in combination with enterprise cycle division, feature extraction and knowledge graph construction.

The invention relates to the field of geological disaster monitoring and evaluation, and discloses a geological disaster rapid investigation and evaluation method based on multi-source remote sensing data, which comprises the following steps of: acquiring optical remote sensing data, SAR (Synthetic Aperture Radar) data and ground sensor data, and establishing unified modeling; consistency verification is carried out on the multi-source data, abnormal data are removed, and the weight is dynamically adjusted; data fusion is realized through a distributed Kalman filtering and variational inference technology; key risk indexes such as the earth surface deformation rate and the rainfall threshold value are extracted, a multi-factor risk evaluation model is constructed, risk grades are divided, and a risk distribution diagram and dynamic early warning information are generated. According to the invention, the problems of single data source, insufficient inconsistency processing and lagging risk assessment in the prior art are solved, the precision and efficiency of geological disaster monitoring are significantly improved, and a scientific basis is provided for disaster early warning and emergency response.

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 belongs to the technical field of watershed water-wind-light integrated operation, and relates to a watershed water-wind-light integrated key operation risk identification method. The method comprises the following steps: firstly, starting from complex coordinated operation requirements such as hydroelectric comprehensive utilization requirements, multi-level scheduling, multiple time scales and water-wind-light complementation, constructing a cascade hydroelectric operation risk system and constructing seven quantitative indexes of the system; secondly, constructing a watershed water-wind-light clean energy base annual 8760h panoramic time sequence operation simulation model, and comparing and calculating cascade hydropower operation risk change levels before and after new energy access; and finally, proposing a risk sorting method coupling TOPSIS and AHP, eliminating different risk index units and magnitude differences, and identifying key risks. By taking a clean energy base at the downstream of the Jinsha River as an application example, various risk distribution characteristics are disclosed, water and electricity waste water, output fluctuation and peak regulation pressure risks are found to be more sensitive to new energy access, and the sensitivity of the risks to seasons and new energy installation is explored and analyzed.

A computer-implemented method and system for authorizing user access to services provided by a first entity is disclosed. The method involves receiving a request to authorize a user, retrieving a plurality of user-related metrics from a database, generating a risk metric based on these metrics, and automatically determining the user's authorization based on the risk metric. The authorization decision involves a second entity assuming risk for the services provided by the first entity. The method further includes communicating the authorization decision to a graphical user interface, providing a clear indication of the user's authorized access.

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.

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 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 enterprise risk self-diagnosis method based on big data, and relates to the technical field of data prediction.The enterprise risk self-diagnosis method comprises the steps that enterprise internal data and competitor external data are collected, and enterprise relative competitive advantages are evaluated regularly; predicting a product sales trend, and analyzing key factors affecting sales; a raw material and product price database is established, historical price changes are recorded, and the influence of the price changes on profits is evaluated; a risk trend is analyzed and predicted by using a time sequence, different responses are triggered according to risk severity, and key risk indexes are monitored in real time. According to the method and the system, the capacity of an enterprise for coping with uncertainty and emergencies is improved, the negative influence of risks on enterprise operation is reduced, and the enterprise can keep business continuity and stability through real-time monitoring and response.

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 provides a vehicle safety risk threshold identification method for a heavy-duty truck, relates to the technical field of vehicle safety, and solves the problem of limited identification precision of a vehicle risk threshold in the prior art. According to the technical scheme, dynamic response data of a truck and environment variables under various external environments and different road conditions are obtained through a multi-source data acquisition system, and real-time key risk indexes of the heavy-duty truck are calculated; preprocessing the real-time key risk indexes to obtain cleaned real-time data; determining candidate thresholds corresponding to the real-time data through a quantile statistical method; the candidate threshold is fed back to the vehicle dynamics model for forward verification, the dynamic response of the vehicle under the threshold boundary condition is simulated until the simulation result passes forward simulation verification, and the candidate threshold is written into a dynamic threshold library. According to the technical scheme, accurate early warning can be achieved, and the accident rate is reduced.

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 small and micro enterprise credit risk prediction method and device, a storage medium and a processor. In the scheme, based on acquired enterprise information of a target enterprise, a plurality of credit risk indexes of the target enterprise are converted into a plurality of index vectors; based on the plurality of converted index vectors, constructing a graph structure reflecting a dependency relationship between the indexes; in the graph structure, nodes represent credit risk indexes, and edges represent dependency relationships among the indexes; and then inputting the graph structure into a credit assessment model, performing prediction by the credit assessment model according to the learned graph structure information, and outputting a credit risk assessment level of the target enterprise. The method is different from the prior art which mainly depends on financial index analysis, historical repayment records and qualitative judgment and has limitation in the aspects of processing complex nonlinear relations and deep time sequence information. Compared with the prior art in which the credit risk of the enterprise is difficult to accurately capture, the method has obvious advantages.

The invention provides a financial risk monitoring system based on distributed setting, and the system comprises a data collection layer which is used for collecting data from a plurality of data sources inside and outside a financial institution, and carrying out the preprocessing of the collected data; the data storage layer is used for storing the collected and preprocessed data; the calculation and analysis layer is built based on a distributed calculation framework, integrates various financial risk assessment models and is used for analyzing the preprocessed data and calculating comprehensive risk indexes in real time; the risk early warning and decision-making layer is used for monitoring the calculated risk indexes according to a preset risk index threshold value, triggering a risk early warning mechanism when the risk indexes exceed the threshold value, and providing decision-making support for the management layer of the financial institution; and the user interaction layer is used for providing risk monitoring data query, risk index report checking, risk early warning information acquisition, risk parameter setting and risk model adjustment functions for users. According to the invention, the efficiency and level of financial institution risk management are improved.

The invention discloses a power grid risk identification method based on a pre-trained Transform model and a medium, and the method comprises the steps: selecting specific risk indexes under three risk dimensions, namely, a future maintenance plan, load prediction data and natural disasters, and designing a corresponding fusion risk index; setting risk threshold values of the indexes according to limited operation information in the power grid and normal operation indications of all the devices, calculating a specific risk index value according to the risk indexes by the current operation state of the power grid, and comparing the specific risk index value with the corresponding risk threshold value; and screening out the indexes exceeding the threshold value and the corresponding power grid operation data, and obtaining risk power grid data in a superposition state. Key information such as the topological structure of the power grid, the voltage and the current of each node and the like in the running state is used as input characteristics of a Transform model, risk identification is carried out on potential factors in the power grid, finally various risk identification results are obtained, and various potential risks in the power grid are accurately identified in cooperation with planned daily scheduling maintenance.

The invention relates to the technical field of enterprise architecture management, in particular to a novel energy system-oriented enterprise architecture risk assessment framework, which comprises the following steps: S1, collecting related data of an existing architecture, S2, collecting transformed architecture data, S3, acquiring risk data, S4, setting risk indexes, S5, identifying risks, S6, assessing the risks, S7, sorting risk priorities, and S8, taking risk control measures. The framework emphasizes the integrity and accuracy of data, a solid foundation is provided for risk assessment through detailed collection and arrangement of data of an existing framework and a transformed framework, quantitative assessment and qualitative assessment are combined in the risk assessment process, statistical data and a mathematical model are utilized, expert review and opinion collection are considered, and the risk assessment efficiency is improved. By comparing data before and after transformation, the framework is helpful for enterprises to find key points and difficult points in the transformation process, so that possible risk points can be foreseen in advance.

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 visual data early warning method and system. The method comprises the steps of S1, data source configuration and access, S2, data preprocessing and cleaning, S3, real-time monitoring and acquisition, S4, early warning rule setting, S5, data analysis and prediction, S6, early warning triggering and notification and S7, visual display and interaction. According to the invention, through the functions of real-time monitoring, prediction analysis, multi-dimensional display, rapid response, decision support and the like, accurate early warning of various risk indexes of an enterprise is realized; the method comprises the steps of determining a corresponding service rule code set by acquiring service data of an application end in real time, calling a corresponding early warning rule operation model according to a set early warning trigger mode, calculating an early warning value according to an early warning rule, and sending the early warning value to a receiving end in a graphical display form when the early warning value meets an early warning trigger condition. And efficient service data early warning trigger analysis and message early warning can be realized.

Systems and methods for digital media-based user authentication are disclosed herein. In some aspects, the system may receive an authentication request. The system may transmit a user verification request based on the authentication request. The system may receive media recordings in response to the user verification request. The system may detect one or more features in the media recordings. The system may generate a plurality of risk metrics for the user based on generating risk metrics for the one or more features. The system may determine an authentication probability based on the plurality of risk metrics.

A computer-implemented method and system for authorizing user access to services provided by a first entity is disclosed. The method involves receiving a request to authorize a user, retrieving a plurality of user-related metrics from a database, generating a risk metric based on these metrics, and automatically determining the user's authorization based on the risk metric. The authorization decision involves a second entity assuming a portion of the risk for the services provided by the first entity and the first entity assuming a second portion of the risk for the services. The method further includes communicating the authorization decision to a graphical user interface, providing a clear indication of the user's authorized access.

The invention relates to the technical field of financial risk management, and discloses a financial risk management system based on a block chain, which comprises a risk identification module, a risk early warning module, a risk index evaluation module, a data management module and a data access control module. The risk identification module is used for identifying and evaluating risk factors capable of influencing a financial institution in each node of the block chain; and the risk index evaluation module is used for evaluating the risk in the financial transaction according to the fund mobility risk index, the operation risk index and the reputation of the two parties of the transaction. Financial transactions are managed and controlled through the risk identification module and the risk index evaluation module at the same time, risks in the financial transactions are evaluated according to fund mobility, operation risks and reputation of both parties of the transactions, early warning is given out when the risk probability of the transactions exceeds 80% of the history, and the risk of the transactions is reduced. Therefore, the risk of financial trade can be effectively reduced.

The invention provides an intelligent management and control system for enterprise finance and taxation risks. Invoice data, enterprise finance data and industry tax data of a target enterprise are acquired through a data acquisition module; through a data fusion module, performing distributed cleaning on abnormal data in the invoice data, the enterprise financial data and the industry tax data based on the established data mapping rule base, and performing real-time fusion to obtain a dynamic data pool; using a risk diagnosis module to extract compliance rules from enterprise finance and taxation legal provisions as conditions, using the trained random forest regression model to predict the dynamic data pool to obtain a reasonable tax threshold value, and determining a risk diagnosis result according to the reasonable tax threshold value; the risk diagnosis result is analyzed through the early warning module, the weight of each risk index is obtained and used for early warning the enterprise finance and taxation risk and generating the risk report of the target enterprise, the island problem of enterprise finance and taxation data is solved, and the finance and taxation management efficiency is improved.

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 a bill discount intelligent decision-making method based on a price comparison model, and relates to the field of finance, and the method comprises the steps: 1, deploying an intelligent optimized lightweight crawler, capturing a bill text message from multiple sources, analyzing and extracting a key semantic tag through a BERT model subjected to depth fine tuning, and mapping the key semantic tag into structured data; 2, constructing a multi-dimensional risk index system, carrying out PCA dimension reduction, dividing a risk interval by using a DPC and a decision tree algorithm, and determining a bill risk condition; 3, according to different risk intervals, a low-risk linear regression enhancement model, a medium-risk random forest integration model and a high-risk LSTM-attention model are adopted, and recommendation interest rates and influence factor weights are output; and 4, adjusting the Pareto search optimal threshold by applying a double-circulation dynamic threshold self-calibration mechanism and combining with the macroscopic index, and carrying out risk income dynamic balance.

A computer implemented method for maintaining information security infrastructure of an entity, includes accessing a cascading classification schema comprising a plurality of levels of classifications indicative of critical security controls, identifying a plurality of Key Risk Indicators (KRIs) and assigning a plurality of respective KRI weight values, a plurality of respective first KRI threshold values, and a plurality of respective second KRI threshold values to the plurality of respective KRIs, assigning the plurality of KRIs to the lowest level of classification, defining a plurality of data points corresponding to the plurality of KRIs, determining a plurality of KRI score values corresponding to the plurality of respective KRIs by comparing a plurality of data values associated with the plurality of respective data points, with the plurality of first KRI threshold values and the plurality of second KRI threshold values, and determining a cascading schema of aggregate scores.

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.