413 results about "Risk threshold" patented technology

The embodiments of the present disclosure belong to the technical field of data processing. Provided is a multimodal scenario risk determination method based on a generative AI large language model. The method specifically comprises: step 1, acquiring multimodal data to form a target data set, wherein the multimodal data comprises visual data and text data; step 2, using an ALBEF algorithm to extract key features corresponding to the target data set, and fusing the key features into a comprehensive scenario representation; and step 3, on the basis of a preset safety index and a large language model, evaluating a risk degree corresponding to the comprehensive scenario representation, comparing the risk degree with a risk threshold, and determining whether the scenario corresponding to the comprehensive scenario representation is a high-risk scenario. By means of the solution in the present disclosure, a high-risk scenario can be rapidly recognized and identified, so as to provide a basis for taking emergency measures, thereby enhancing the real-time response capability.

The invention relates to a risk management and control method and system based on real-time behavior analysis, and the method comprises the steps: carrying out the structural processing of multi-source behavior data through lightweight protocol decoding and behavior label embedding, and constructing an original behavior data set of a user and an entity; extracting multi-dimensional behavior characteristics by using a sliding window analysis and sparse representation mechanism, and constructing a user behavior graph by combining graph embedding learning; constructing a time-sensitive behavior trend model through streaming modeling and an incremental learning strategy, identifying an abnormal evolution trajectory in real time, and introducing a dynamic risk threshold regulation and control mechanism; adopting a high-throughput flow data processing and fast similarity matching algorithm to construct a fusion discrimination model, giving risk levels to abnormal behaviors and classifying the abnormal behaviors; and finally, performing closed-loop optimization in combination with a historical treatment effect. The system has the advantages of high real-time performance, high calculation efficiency, adaptability to complex network environments and the like, and the network security protection capability can be effectively improved.

The invention discloses a production scheduling strategy adjustment system and method, and belongs to the technical field of production management, and the method comprises the steps: constructing a multi-source heterogeneous database, setting a semantic association method, generating a demand knowledge graph, setting a prediction and correction method, constructing an order prediction model, generating order quantity fluctuation matrixes with different confidence degrees, and providing quantitative parameters for risk assessment. Constructing a risk assessment basic data set, setting a risk assessment method, dynamically adjusting a risk threshold value to realize adaptive early warning, constructing a risk probability model, and generating a production decision result based on a formulated production decision judgment rule; calculating a factory quantity deviation rate, setting causal analysis, detecting deviation by using a threshold value, triggering an alarm, integrating production data to construct a causal graph, and generating a causal effect report; and calculating a deviation rate of deviation detection, setting a self-optimization method, obtaining an optimal hyper-parameter combination, and optimizing an input feature vector so as to improve the production scheduling prediction accuracy.

The invention relates to a dynamic risk control method and device, equipment and a storage medium. The method comprises the following steps: collecting and preprocessing multi-source data to obtain a standardized multi-dimensional data matrix; constructing a dynamic risk control model according to the standardized multi-dimensional data matrix, and calculating a risk control model parameter set; performing risk factor sensitivity analysis by using the risk control model parameter set and the standardized multi-dimensional data matrix to obtain a dynamic weight vector; executing iterative risk assessment based on the dynamic weight vector to obtain a comprehensive risk score; constructing a multi-layer threshold structure based on the comprehensive risk score and executing dynamic adjustment to obtain a personalized risk threshold; and according to the comprehensive risk score and the personalized risk threshold, triggering a risk control measure and executing a feedback mechanism to obtain a risk control execution result. The importance of different risk factors can be adjusted in real time, the market environment change is quickly responded, and the exploration capability of the system for unknown risks is enhanced.

The invention discloses a building construction real-time monitoring and early warning method based on big data, and relates to the technical field of construction management, and the method comprises the following steps: S1, determining a monitoring target and sensing parameters; s2, establishing a multi-dimensional data model; s3, determining a risk threshold and an early warning mechanism; s4, performing data processing on the real-time data stream; s5, constructing an intelligent anomaly detection model; s6, implementing dynamic adjustment and feedback circulation; and S7, integrating an external data source. According to the building construction real-time monitoring and early warning method based on the big data, by introducing a dynamic adjustment and feedback mechanism based on the big data and combining a real-time data flow and a machine learning algorithm, the building construction real-time monitoring and early warning method can significantly improve the response speed and accuracy of early warning; according to the method, the risk threshold and the monitoring index can be updated in real time, and the actual situation of the site can be reflected in time, so that the risk of accidents is reduced, and the safety and efficiency of construction management are improved.

The invention discloses an intelligent financial risk early warning method and system based on a management decision, and relates to the technical field of data intelligence, and the method comprises a multi-source heterogeneous data collection module which obtains enterprise financial data, supply chain data, market public opinion data and industry reference data in real time through an API interface, risk keywords are extracted from news, social media and policy documents through a natural language processing technology according to the market public opinion data; the streaming data processing engine is constructed based on an Apache Flink framework, performs windowing processing on the real-time data stream, calculates the dynamic fluctuation ratio of financial indexes by sliding a time window, and compares the dynamic fluctuation ratio with a preset industry risk threshold value; and a risk decision fusion model, a dynamic threshold adaptive module and a man-machine collaborative early warning terminal. According to the method, millisecond-level financial index fluctuation monitoring is realized through a streaming computing framework, a knowledge graph and a natural language processing technology are fused, a risk entity and a causal chain are extracted from unstructured data, and a multi-dimensional risk portrait is constructed.

The invention relates to a sensitive data exception cross-border detection method and system based on flow analysis, and the method comprises the steps: obtaining a plurality of to-be-detected target flow sessions; analyzing each target flow session through a dynamic risk assessment model, and performing risk assessment based on the risk score of the report information, the risk score based on the historical behavior and a comprehensive risk assessment rule matched according to the current service scene to obtain a risk value; the dynamic risk assessment model is dynamically adjusted according to data flow behavior; and when it is detected that the risk value corresponding to the target stream session exceeds a preset risk threshold, triggering real-time early warning. Through application of the method and the device, the problem that sensitive data is misreported or misreported due to the fact that an existing detection method is difficult to dynamically adapt to the change of the data flow in a complex scene of cross-border data flow is solved, the change of the data flow is dynamically adapted to the complex scene of cross-border data flow, and the sensitive data is prevented from being misreported or misreported.

The invention discloses a driving behavior risk assessment method and system, and relates to the technical field of intelligent traffic and driving safety, and the method comprises the steps: collecting the operation parameter data of a vehicle, carrying out the feature extraction of the operation parameter data, extracting feature parameters to form a feature vector, and calculating the Mahalanobis distance score of the feature vector; according to the Mahalanobis distance score of the feature vector, carrying out risk grade division by adopting an improved hierarchical fuzzy inference system to obtain a risk grade score; and establishing a driver portrait model according to the risk level score, and when the risk level score exceeds a personalized risk threshold, outputting a driving risk behavior improvement suggestion to complete driving behavior risk assessment. According to the invention, a refined grading basis is provided for individual risk monitoring of the driver, dynamic monitoring and real-time intervention of driver behaviors are realized, traffic accidents can be reduced, and driving safety is improved.

The invention relates to the technical field of risk analysis, in particular to a safety engineering comprehensive management system which comprises a geological parameter dynamic monitoring module, a risk threshold early warning module, a stability weight evaluation module and a risk comprehensive management and control module. According to the method, lithology data are extracted through a drilling profile to calculate the layer thickness sudden change rate and the strength attenuation rate, a lithology abnormal area is positioned through a variable coefficient method, lithology sudden change marks are analyzed through spatial clustering, a dynamic trend is captured through sliding window mean value comparison, and gradient amplitude and slope direction dispersion are calculated through a digital elevation model. Fusing the abnormal point density to generate a terrain instability index; establishing a multi-dimensional quantitative evaluation framework; carrying out fuzzy comprehensive evaluation and cross validation on the instability index and an engineering parameter trend; generating a dynamic priority sequence, carrying out stake number interval matching to construct a risk measure table, carrying out analytic hierarchy process dynamic resource allocation support drainage scheme, and carrying out real-time monitoring and prediction closed-loop iteration. And adaptively adjusting the control strategy.

The invention discloses a cloud financial system and method based on artificial intelligence, and relates to the technical field of financial data management, and the system comprises a financial management platform which is in communication connection with a data collection and preprocessing module, an abnormal data analysis module, a monitoring and early warning module, a performance optimization module and a report generation and distribution module, the modules are in electric signal connection; the data acquisition preprocessing module is used for acquiring financial data from each financial data source. According to the method, the change trend of financial data is continuously monitored, historical data and industry standards are combined, the abnormal resource demand index is calculated, early warning mechanisms of different levels are triggered according to a set risk threshold value, a resource allocation strategy is dynamically adjusted according to the real-time load condition, it is ensured that efficient operation is still kept under the high-concurrency condition, and the resource allocation efficiency is improved. The response speed of the system is improved, potential financial risks can be effectively prevented and dealt with, and financial health of enterprises is guaranteed.

The invention relates to the technical field of pipeline risk monitoring, and discloses a pipeline risk monitoring method and system based on artificial intelligence, and the method comprises the steps: collecting multi-source sensing data of a pipeline operation environment, and carrying out the preprocessing of the data, and obtaining a multi-scale time sequence feature set; and constructing a pipe network diagram model, and embedding the multi-scale time sequence feature set into the pipe network diagram model. And learning the pipe network diagram model by using a space-time diagram attention network to obtain a target prediction result. And constructing an expected economic loss function, and determining an optimal risk threshold based on the expected economic loss function. And comparing the optimal risk threshold with a corrosion event probability prediction value to obtain a risk level, and determining a maintenance priority sequence of each risk point according to the risk level, a historical maintenance record and the importance of a pipeline section. And generating a maintenance work order based on the maintenance priority sequence. According to the invention, dynamic and prospective risk prediction is realized, and the economy and operability of pipeline risk monitoring management are improved.

The invention relates to the technical field of data processing, and discloses a data processing method and system based on liquid leakage detection. The method comprises the following steps: acquiring a sensor signal and processing to obtain leakage basic data; analyzing signal changes to determine leakage types and influence ranges; matching the risk threshold to determine a risk level; associating historical records to analyze leakage laws; calculating a failure probability to generate a maintenance strategy; and constructing a running state diagram to form a hierarchical management report. According to the invention, leakage management is converted from a traditional passive response mode to a data-driven active prevention mode, optimal configuration of maintenance resources is realized, and the leakage prevention and control efficiency is improved while the maintenance cost is reduced.

The invention belongs to the technical field of cross-border payment settlement analysis and management, and discloses a cross-border payment channel intelligent distribution system based on transaction types. Comprising a type identification module which is used for collecting transaction order data, carrying out feature extraction on the transaction order data to obtain transaction order features, and taking the transaction order features as input of a type identification model to obtain a transaction type label; the risk assessment module is used for collecting risk data, performing feature extraction on the collected risk data to obtain risk assessment features, and taking the risk assessment features as input of a risk analysis model to obtain risk assessment scores; the payment process is monitored in real time based on the real-time data acquisition technology and the stream computing technology, and when the risk degree exceeds a preset risk threshold value, an early warning mechanism is triggered immediately; according to factors such as the real-time market condition and the performance change of the payment channel, the selection strategy of the payment channel is dynamically adjusted, and a precise payment channel distribution scheme is provided.

The invention discloses a digital twinning-based high-altitude falling risk state assessment technology for building workers and a mapping method and system. The method comprises the following steps: extracting aqueduct building engineering site space element information and personnel positions by an IFC model, and respectively inputting the information and the positions into an indoor navigation model and a three-dimensional digital model; historical risk data of an aqueduct construction engineering site are collected to analyze a risk assessment index and a preset risk threshold value; constructing a digital twinborn model according to the risk assessment index, the risk threshold value, the indoor navigation model and the three-dimensional digital model; real-time construction site environment data, worker behavior data and equipment state data are collected to update and simulate the digital twin model in real time; evaluating the risk range of the high-altitude falling risk by combining the updated and simulated results with a risk evaluation algorithm; and performing mapping association on the digital twinborn model through the construction site environment data, the worker behavior data and the risk range, performing visual interface presentation on the mapping association part, and designing a risk early warning trigger threshold.

The invention relates to the technical field of computers, and discloses a model content security control method and system, electronic equipment and a storage medium, and the method comprises the steps: combining a received user input with a historical dialogue record of a current session to form a dialogue context; performing risk identification based on the dialogue context, generating at least one risk category and a corresponding initial risk value, and generating a risk integral corresponding to the current input according to a preset integral strategy; accumulating the risk points to accumulated risk points of the current session; comparing the accumulated risk integral with a dynamic risk threshold value obtained by calculation according to a reference threshold value, a logarithmic function attenuation item of the dialogue round and a user historical behavior adjustment item; and when the accumulated risk integral reaches or exceeds a dynamic risk threshold value, triggering a preset risk management and control measure. According to the method, the risk content can be effectively identified and controlled, attacks bypassing a security mechanism through slow induction can be defended, and the security boundary is not easy to detect.

The invention relates to the technical field of electric vehicle charging safety, and discloses a charging station risk grading and classifying method, which comprises the following steps: acquiring safety quantitative evaluation indexes of different vehicle types in each SOC stage, integrating multi-source data, extracting risk correlation characteristics, identifying a periodic risk mode by using time sequence analysis, obtaining the charging safety coefficient of the SOC in each stage, and classifying the charging safety coefficient of the SOC in each stage. According to the method, accurate acquisition and fusion analysis of multi-dimensional data are realized, a risk early warning model is trained through supervised learning LightGBM and 3-sigma multi-level screening and a hybrid neural network algorithm, an unknown abnormal mode is identified in combination with abnormal detection of an isolated forest and an auto-encoder, operation fault reasons are divided according to risk grade scores, and a risk early warning result is obtained. Graded response and automatic control are achieved, a multi-site data updating model is aggregated through federal learning, the generalization ability is improved while privacy is protected, and dynamic optimization of model parameters and scenarized adjustment of risk thresholds are achieved.

The embodiment of the invention provides a subway station large passenger flow risk management and control method and system, and the method comprises the steps: dividing a key region according to a station structure, obtaining the risk level of the key region through employing a passenger flow data-based risk threshold dividing method, and calculating the risk level of a station; taking the passenger flow management and control comprehensive cost and the station coupling passenger flow risk as optimization targets, and enabling the risk level of the station to meet a preset risk level; and carrying out optimization solution on the optimization target, selecting an optimal solution according to different scenes, and taking the optimal solution as a station large passenger flow risk management and control strategy. According to the method, the large passenger flow risk in the station is prevented by generating the dynamic management and control strategy of the passenger flow risk.

Systems and methods for assessing enterprise-level risk assessment of a supplier. The method includes receiving an indication of a supplier, generating an impact score for the supplier; generating a likelihood score for the supplier; generating a combined risk score based on the generated impact score and the generated likelihood score; evaluating the generated combined risk score relative to a dynamic risk threshold; and based on the evaluation, generating an output including the generated combined risk score.

The invention discloses an income and expenditure declaration data processing method and system based on a multi-heterogeneous model. The method comprises the following steps: acquiring international income and expenditure declaration data sent by a target declaration end and a corresponding historical check operation record; predicting a declaration data risk corresponding to the target declaration end according to a preset multi-heterogeneous neural network model and the international income and expenditure declaration data; according to the checking operation records of a plurality of similar declaration ends and the historical checking operation records, determining an operation abnormity risk corresponding to the target declaration end; calculating a product of the declaration data risk and the operation abnormity risk to obtain a checking prior risk value corresponding to the target declaration end; the checking prior risk value is used for triggering warning marking of the target declaration end when the checking prior risk value is greater than a preset risk threshold value. Therefore, the risk of the declaration end can be analyzed and warned in advance, the efficiency and effect of international income and expenditure declaration data checking are improved, and checking errors are reduced.

The invention provides an intelligent risk control system based on big data, and relates to the technical field of risk analysis, the system integrates and cleans various supply chain data through a data acquisition module to ensure the accuracy of analysis, and a coefficient generation module generates a logistics congestion risk coefficient and a production stability coefficient by using a big data analysis technology. Therefore, the on-time delivery capability and the delivery completion degree of the supply chain are dynamically evaluated, and the real-time performance and the accuracy of evaluation are improved. The index generation module generates a failure learning index by analyzing problem response data, comprehensively evaluates the ability to deal with sudden problems, and identifies an improved space; the threshold judgment module establishes a comprehensive risk index by comprehensively analyzing the generated coefficients, automatically predicts a risk early warning level according to a set risk threshold, and timely triggers countermeasures; the system enhances the flexibility and anti-risk capability of the supply chain, and enables enterprises to realize more efficient operation management in dynamic markets.

The invention relates to the technical field of intelligent risk analysis, in particular to an intelligent risk analysis system of a financial system. The system comprises a data flow analysis module, a relational graph construction module, an abnormal mode detection module, a security boundary monitoring module, a staged strategy planning module, a scene simulation evaluation module, a decision support module and a comprehensive risk management module. A graph database and a graph convolutional network are utilized to deeply analyze a financial entity relationship, reveal a risk association network, improve abnormal behavior recognition through a K-mean value and an isolated forest algorithm, enhance security boundary monitoring through a random forest algorithm, enable risk threshold adjustment to be more dynamic, combine a decision tree with a genetic algorithm, optimize a risk management strategy, and improve risk management efficiency. The system dynamics and proxy model technology in scene simulation evaluation strengthens risk prediction and increases foresight, principal component analysis and risk matrix evaluation are applied in a comprehensive risk management module, a quantitative analysis tool is provided, and more systematic and comprehensive risk management is realized.

The invention relates to the technical field of electric power systems, in particular to an electric power spot market risk prevention and control method and system considering market subject behaviors. Acquiring operation data of generating capacity, electricity demand, market price and power grid state of the electric power spot market; on the basis of the acquired operation data, a coupling degree evaluation index and a stability evaluation index are introduced to carry out prediction and anomaly detection on a market trend, and influence factors of connection of a large-scale generator set and a small-scale power grid are particularly concerned; analyzing a market subject behavior pattern through transaction frequency, price volatility and market participation degree characteristic quantitative indexes, and identifying market manipulation and violation operation risks; market dynamics are monitored in real time, a risk threshold value is set, and an alarm is triggered when a risk index exceeds a set standard; early warning information of different levels is automatically sent out according to the risk degree, and corresponding countermeasures and suggestions are provided; and a supervision mechanism is helped to make a quick response through a visual interface.

The invention discloses a small sample modeling stability evaluation method and system based on Bootstrap resampling, and particularly relates to the technical field of computers and data intellectualization, the method comprises the following steps: completing data preprocessing and stable stage identification under a unified time base, and forming a segment set; an average block length is used as a decision quantity, an SBB is optimized to construct a sample space, and an optimal block length is determined in combination with variance consistency and nominal coverage rate consistency criteria; carrying out re-sampling training around short / medium / long scales, and collecting layered indexes such as prediction, parameters and features; stability calculation and empirical coverage rate calibration are completed based on intra-scale statistics and inter-scale weighted mixing, and a pseudo-stationary diagnosis score is output; and finally, engineering judgment and backspacing optimization are carried out according to the coverage rate, the correlation maintenance and the risk threshold. The system records random seeds, block metadata and model version generation evidence pointers, has the characteristics of traceability and reverifiability, and is suitable for scenes of production lines, network traffic, finance and the like.

The invention discloses a transaction risk adaptive blocking system and method based on large model behavior chain analysis. The system comprises a data acquisition module, a behavior chain construction engine, a large model analysis module, a dynamic time sequence modeling module and a risk assessment and blocking module. According to the system, transaction behaviors of a transaction subject at different time points are connected in series to form a sequence, a time sequence risk feature vector is generated in combination with dynamic time sequence modeling, and real-time accurate assessment of risks is achieved. The system can quickly identify potential risk transactions and give an early warning in advance. Meanwhile, the risk assessment and blocking module dynamically adjusts a risk threshold according to market fluctuation and credit conditions of transaction subjects, adaptively blocks high-risk transactions, and ensures that prevention and control strategies are matched with actual demands. And meanwhile, multi-dimensional data including geographic position and operation behavior information are fused, so that the comprehensiveness and accuracy of risk identification are further improved. An innovative and effective technical means is provided for financial transaction risk prevention and control, and safe and stable operation of financial transactions is powerfully guaranteed.

Sustainable enterprise-wide energy balancing is presented herein, which describes a series of processes to make choices in favor of a sustainably sourced energy mix, grid reliability, and / or to prioritize critical systems in times of a catastrophe. An example method comprises receiving a group of input data representing a standardized phrase, wherein the group of input data is received from first equipment associated with an electric power generation and transmission operator entity; parsing the standardized phrase to identify an initiating event; in response to identifying the initiating event, determining that an initiating threshold value associated with the initiating event has fallen below a defined risk threshold value; and executing, on remote second equipment, a flow of customer defined processes representing a group of runbooks.

The invention relates to the technical field of agent testing, and particularly provides an agent dynamic behavior safety testing method and system based on a risk conduction quantification model, and the method comprises the steps: collecting agent behavior data which comprises a plurality of pieces of risk operation information and a risk association relationship; constructing a behavior risk association map by taking the risk operation as a node and the association relationship as an edge, labeling a risk attribute and an aging factor for the node, and labeling risk conduction intensity for the edge; constructing a risk rule base based on the risk labeling result and a preset risk judgment logic; inputting the behavior data, the behavior risk association map and the rule base into a trained quantitative model, and calculating a risk conduction probability, an influence range and a critical node; monitoring a behavior track in real time, suspending a task when a multi-dimensional risk threshold is triggered, backtracking an inference chain and generating an intervention record; and fusing the multi-dimensional data to generate a test report. According to the method, the problems of correlation risk missed judgment and response lag in the traditional test are solved, and the pertinence and reliability of the safety test are improved.

The invention relates to the technical field of smart park safety monitoring, and particularly provides a BIM-based trajectory analysis method and system, a terminal and a storage medium, and the method comprises the steps: S1, collecting real-time target information, obtaining a real-time target emotional state, obtaining a real-time target position and a real-time target movement trajectory based on monitoring equipment; dynamically dividing the building area into a plurality of sub-areas based on the BIM model, and obtaining boundary information of each sub-area; calculating the group intensity in each sub-region; weight values are configured for the target emotional state, the target moving track and the group intensity of the targets in each area; constructing a multi-input LSTM risk assessment model, and outputting a predicted target risk score; and analyzing and judging the target risk score based on a preset risk threshold, and when the target risk score is greater than the risk threshold, judging that the target has a risk. The identification and management capability of potential risks is effectively improved, and safety precaution measures are enhanced.

The invention discloses an operation state evaluation method and system for the deep peak regulation process of a fluidized bed unit, and relates to the technical field of fluidized beds, and the method comprises the steps: collecting the combustion condition data of the deep peak regulation process of the fluidized bed unit; generating a particle flow velocity vector diagram, and identifying and marking different flow modes and paths in the particle flow velocity vector diagram; generating a non-uniform index of the whole flow field through particle flow velocity and particle concentration distribution of each flow mode area in the flow field; generating a bed temperature distribution diagram, recognizing a high temperature-flow mode region by combining with the particle flow velocity vector diagram, and calculating a coking risk index of the high temperature-flow mode region; and calculating a coking risk index of the whole flow field, comparing the coking risk index of the whole flow field with a risk threshold value, and when the coking risk index of the whole flow field is greater than or equal to the risk threshold value, giving out an early warning, identifying potential risks, and avoiding serious problems such as coking.

The invention discloses an enterprise tax risk prediction method and device, computer equipment and a storage medium. The method comprises the steps of obtaining to-be-predicted enterprise tax data; performing feature engineering processing on the to-be-predicted enterprise tax data to obtain a processing result; inputting the processing result into a prediction model for risk value prediction to obtain a prediction result; determining a risk level according to the prediction result and a set risk threshold; outputting the risk level; wherein the prediction model is obtained by training a multi-task learning network through a full sample set composed of a risk sample data set and a non-risk sample data set. By implementing the method provided by the invention, the identification capability of the tax risk can be improved, and the prediction accuracy and comprehensiveness can be improved.

The invention relates to the technical field of financial risk assessment, in particular to a financial risk assessment method and system fusing multi-dimensional indexes and combined weights, and the method comprises the steps: constructing a financial cooperation risk index system containing multi-dimensional heterogeneous data; executing a combined weight calculation process based on Bayesian inference logic; performing weighted aggregation on the standardized index data and the posterior combination weight, and calculating a comprehensive financial risk index of the target assessment object; and based on the comprehensive financial risk index, in combination with a preset risk threshold model, generating a systematic risk assessment result and a structural risk distribution map for a specific financial cooperation scene. According to the method, complex risk factors are converted into visual quantitative indexes, and macroscopic decisions in transnational financial cooperation are directly supported.