41 results about "Risk threshold" patented technology

This invention relates to the field of digital asset analysis technology, and more particularly to the RWA digital copyright trading system in the cultural industry. It includes constructing a cultural asset value memory network and extracting implicit cultural value transmission relationships to provide a comprehensive and accurate data foundation and value correlation basis for subsequent transaction analysis and risk assessment; calculating the discrete entropy of trading groups to promptly identify abnormal group structures, enabling early detection of potential transaction risks and abnormal market fluctuations; tracing the transmission path and calculating the attenuation coefficient when a sudden change in discrete entropy is detected to pinpoint the source and intensity of risk propagation, providing crucial clues for risk prevention and control; calculating the repricing step probability by combining transmission intensity and price fluctuations to quantitatively assess the likelihood of significant price changes in target assets; and locking suspected bubble trading nodes and isolating the links when the repricing step probability exceeds a risk threshold, effectively blocking risk spread and ensuring the stability and security of the trading system.

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

This invention provides a trusted update and risk-adaptive rollback method and system for over-the-air (OTA) updates of vehicle-side basic models. The method includes: receiving a new version of the basic model update package and performing a trusted verification; upon successful verification, initiating a shadow mode and collecting scenario distribution statistics; calculating multi-dimensional coverage metrics based on the scenario distribution statistics; and proceeding to canary release after successful verification; loading a multi-dimensional risk budget; dynamically deducting the remaining risk budget value based on operational risk events during the canary release process, and achieving adaptive rollback through risk budget gating; performing graded downgrade or automatic rollback operations when a risk threshold is triggered, and generating a standardized rollback reason code; and assigning a globally unique audit trail identifier to the entire OTA update process, generating bound anti-tampering audit records. This invention enables quantifiable assessment of scenario coverage, dynamic control of risk budgets, and adaptive canary release for OTA updates of vehicle-side basic models, improving the security and compliance of the update process.

This invention relates to the field of financial risk early warning technology, specifically a method for dynamic prediction of repayment ability and overdue early warning based on multi-source time-series data. The method includes: acquiring multi-source time-series data of a target object over a continuous time period; generating a clean time-series data stream through time axis normalization and data quality restoration; constructing a feature set of income stability, expenditure volatility, and debt pressure based on this data stream, forming a multi-dimensional risk feature space; inputting this data into a probabilistic graphical model based on state transitions, outputting a probability distribution of repayment ability states; and calling an inversion inference engine to trace the key characteristic variables and change trajectories of abnormal states through state inversion technology, comparing them with preset risk thresholds to determine the risk critical point and level. This method achieves dynamic prediction of repayment ability and overdue early warning, improving the accuracy and targeting of risk identification.

This application relates to the field of network security technology, and provides a method, apparatus, and computer-readable storage medium for defending against spliced ​​malicious code. The method includes: selecting a session request in the request history chain whose splicing pattern is server-side splicing as the first session request to be inspected; accumulating a preset score value triggered by scoring keywords in the first session request to obtain a single-session splicing intent; when the single-session splicing intent score is greater than or equal to a preset score threshold, performing a risk assessment based on risk elements triggered during the execution of the first session request in an isolation sandbox and the read skill modules to obtain a single-session risk assessment value; and when the single-session risk assessment value is greater than or equal to a preset risk threshold, the server intercepts the first session request to be inspected. This application reduces the computational load of risk assessment by filtering the first session request to be inspected through splicing intent scoring, and improves the efficiency and accuracy of identifying and intercepting single-session server-side spliced ​​malicious code.

This invention discloses a multi-dimensional information security risk dynamic assessment system and method for detection platforms, relating to the field of information security technology. It collects multi-dimensional operational parameters such as assets, vulnerabilities, and attacks, converting them into risk factors. An initial comprehensive risk index is calculated using a radial basis function algorithm. Real-time business load is sensed to calculate weight offsets, and risk weights are redistributed and normalized, updating the risk index. A sliding assessment cycle is used to clean the sequence, and quantile regression is applied to determine a basic threshold. This basic threshold is then adjusted using historical statistical data of the load status to generate a dynamic risk threshold. When the risk index exceeds the dynamic threshold, a risk is identified and a response is triggered. This achieves accurate risk fusion and adaptive judgment, reduces response lag, and improves assessment accuracy, aiming to solve the problems of insufficient risk perception and high false alarm rates in complex environments.

This invention discloses a trusted auditing method for the operation and maintenance of an points mall based on a blockchain distributed ledger, comprising the following steps: Step 1: Monitoring the operation event flow in the points mall, collecting points operation behavior data, and parsing the event types; Step 2: Using an improved SHA-256 hash algorithm to form a points operation behavior evidence record; Step 3: Constructing an operation behavior graph based on the points operation behavior evidence record; Step 4: Performing numerical processing to generate behavior representation vectors; Step 5: Performing feature analysis, extracting explicit risk factors, and forming a risk score; Step 6: Generating interpretable audit records for operation paths exceeding a preset risk threshold, and performing digest compression; Step 7: Writing the structured audit evidence data into the blockchain ledger and performing manual review. This invention integrates on-chain evidence storage and behavior scoring mechanisms to achieve trusted auditing and risk control supervision throughout the entire points operation process.

This invention discloses a multi-angle positioning anti-interference control method and system for a five-axis machining center. The core steps include: constructing a parameterized spatial envelope model of the entire motion chain components of the five-axis machining center; parsing the multi-angle positioning instructions of the current CNC machining program to obtain the target motion parameters of each motion axis, the corresponding target tool position point, and the tool axis posture sequence during the positioning process; performing look-ahead interpolation on the target motion parameters based on a preset look-ahead interpolation cycle number N to generate the real-time motion pose sequence of each motion axis and the real-time pose of the corresponding envelope model within the next N interpolation cycles; performing global interference collision detection on the full-cycle envelope model, calculating the minimum spatial distance, calculating the dynamic interference risk threshold based on the real-time motion parameters, and classifying the interference risk level; matching anti-interference control strategies according to the risk level, correcting the motion parameters, generating interpolation control instructions, and sending them to the CNC system for positioning control execution.

This invention discloses a method and system for assessing open-source software supply chain security risks based on multimodal feature fusion, solving the technical problem that existing open-source software supply chain security risk assessment technologies result in unsatisfactory assessment effects. The method includes acquiring code semantic data, community governance time-series data, and license compliance data, and inputting them into a multimodal security risk assessment model comprising a multimodal feature encoding module, a cross-modal contrastive learning module, a cross-attention and gating fusion module, and a multi-task risk assessment module. The model sequentially completes multimodal feature encoding, cross-modal contrastive learning alignment, global risk vector extraction, and risk index assessment, outputting a comprehensive security risk index. Finally, it uses preset multi-level risk thresholds for risk level matching, outputting the open-source software supply chain security risk level assessment result.

The application discloses a coal mine power consumption prediction method and system fusing event pre-detection, which comprises collecting multi-source data in coal mine production; based on multi-source data of each selected specific moment before the occurrence of historical target events in the coal mine area, a historical target pre-detection factor database under each specific moment is constructed; a plurality of selected machine learning models are trained through each historical target pre-detection factor database, and then a pre-detection model is constructed; based on the multi-source data after dimension reduction and the total power consumption of the coal mine, an electricity-related data set for training an initial prediction model is constructed; multi-source data is collected in real time and input into the pre-detection model, and a comprehensive risk score is determined based on the prediction result; when the comprehensive risk score is greater than a risk threshold, an initial prediction model is adjusted based on parameter adjustment information corresponding to a target event to obtain a fusion power consumption model, and multi-source data is input into the fusion power consumption model after dimension reduction to obtain a coal mine power consumption prediction value. The application has the advantages of wide application range, high accuracy and the like.

The application discloses a kind of multi-dimension information security risk dynamic evaluation system and method for detection platform, it is related to information security technical field, and collection asset, vulnerability, attack and other multi-dimension operating parameters are converted into risk factor, and initial comprehensive risk index is calculated using radial basis function algorithm;Real-time sensing business load to calculate weight offset, execute risk weight redistribution and normalization and update risk index;Through sliding evaluation period cleaning sequence, application quantile regression determines basic threshold, and basic threshold is adjusted floatingly in combination with the historical statistical data of load state, generates dynamic risk threshold;When risk index exceeds dynamic threshold, determine risk and trigger response, realize the accurate fusion and adaptive determination of risk, reduce response hysteresis, improve evaluation accuracy, aim at solving the problem of risk perception under complex environment and high false alarm rate.

This invention relates to the field of nuclear power plant risk assessment technology, and particularly to a method and equipment for screening and evaluating low-risk items in nuclear power plants. A method for screening and evaluating low-risk items in nuclear power plants includes: determining whether the item to be changed can be modeled using a PSA model; if so, using a funnel-shaped hierarchical screening mechanism to screen the item to be changed, obtaining screened items; performing safety analysis on the screened items to obtain specific parameters; using a PSA model based on the specific parameters to calculate the risk magnitude of the screened items, obtaining risk data; determining whether the risk data meets a preset risk threshold requirement to obtain the screening result; and determining the necessity of taking risk management actions based on the category of the screened items and formulating performance monitoring strategies after implementing the screened items. This invention simplifies the screening process for low-risk items, improves the execution efficiency of items to be changed in actual operation, and enhances the safety and economy of the execution process of items to be changed.

This invention discloses a method for generating autonomous driving scenarios based on risk perception and quantitative iteration, relating to the field of autonomous vehicle simulation test scenario generation technology. The method includes: based on user test commands, retrieving information from a domain knowledge base with both semantic and topological dimensions to obtain regulatory boundaries, physical limits, and topological constraints, and injecting this information into a prompt word template; performing scenario element parsing and structured mapping to generate an initial hazardous scenario description that meets traffic regulations and generate an executable simulation scenario; when the comprehensive risk assessment value of the executable simulation scenario is less than a preset risk threshold, performing causal diagnosis and reverse reasoning of low-risk causes to generate optimization commands to guide the generation of executable simulation scenarios in the next iteration, until the comprehensive risk assessment value of the executable simulation scenario is greater than or equal to the preset risk threshold, resulting in a high-risk autonomous driving scenario. This method improves the risk level of the generated autonomous driving scenarios.

The application discloses a risk control system and method for a financing guarantee industry, wherein an industry evaluation index system table module reduces the dimension of qualitative indexes, and a support vector machine model adopting recursive feature elimination is used to reduce the dimension of quantitative data to screen important indexes; a risk assessment model module adopts a multilayer Delphi method module to determine the weight of each index, sets an alarm signal, and sets a risk threshold of each index according to an industry standard; a risk value module is used to obtain each index value through a user input mode, and each business link risk value is calculated according to the risk assessment model module; a penalty factor module performs a credit rating prediction, and determines a credit rating binding penalty factor; and an early warning mechanism module monitors the system through an early warning task, triggers early warning monitoring according to early warning rules after a risk result is obtained, and performs early warning display. The application can effectively improve the risk control capability of a guarantee company.

This invention relates to the field of equipment operation status monitoring and risk assessment technology, and proposes a method for equipment risk prediction based on dynamic weight allocation of multi-source heterogeneous data. The method first collects equipment operating parameter data, environmental monitoring data, and historical operation and maintenance data, and extracts equipment operating condition characteristics; it then divides the equipment operating status into different operating conditions through cluster analysis; under each typical operating condition, it extracts risk characteristic parameters and calculates risk characteristic indicators; further, it calculates the correlation between each risk characteristic indicator and the equipment risk status, and determines the initial dynamic weights of the multi-source data based on the correlation; it adjusts the dynamic weights by introducing a weight change smoothing constraint mechanism to obtain smooth and stable final dynamic weights; finally, it fuses the final dynamic weights with the risk characteristic indicators to obtain a comprehensive equipment risk index, and achieves equipment risk prediction and graded early warning based on a preset risk threshold. This invention can achieve dynamic weighting and stable fusion of multi-source equipment data by operating condition, improving the accuracy and stability of equipment risk prediction.

The application discloses a risk distribution four-color chart management and control method, system and device based on a smart power plant and a storage medium, relates to the technical field of smart power plant operation safety, and comprises the following steps: unifiedly identifying equipment, operation and environment risk objects in the smart power plant, collecting and structuring multiple-source risk original indexes under the same time reference, and constructing a risk input data set; normalizing the equipment risk, operation risk and environment risk original indexes of each risk object, outputting a comprehensive risk value, determining a fixed risk threshold, and mapping the comprehensive risk value to red, orange, yellow and green four-color risk levels; and generating a risk distribution state of a space node level according to a power plant space topology structure, and performing a management and control operation according to the risk color level corresponding to the space risk node. The application drives the hierarchical management and control operation by the color level, and improves the objectivity of smart power plant risk identification, the consistency of management and control, and the implementability of execution.

The application discloses a user data analysis method based on big data, comprising: acquiring behavior data and environment data related to user account related business, aligning and unifying all data, and calculating data credibility score; generating user business event sequence based on a preset business event model, associating the user business event sequence with business entities, and forming a user business entity structure; extracting risk control features from the user business event sequence and the user business entity structure, and calculating a user business risk score in combination with the data credibility score; constructing a mapping rule from the risk score to the operation disposal measure, optimizing the risk threshold parameter with the minimum overall operation disposal cost as the target under the condition of meeting the preset operation constraint condition, and outputting the disposal measure for the user.

The application discloses a large model generation content risk identification and intervention method based on deep learning, comprising the following steps: collecting multi-modal data output by a content generation system and pre-processing to generate pre-processed multi-modal input data; multi-modal perception model feature coding and fusion to generate global risk feature representation data; inputting the global risk feature into a cheetah optimization algorithm to optimize model structure parameters, risk threshold and intervention strategy parameters; driving risk identification and hierarchical intervention by optimized parameters to output risk level, category and hierarchical intervention measures; collecting intervention effects and user feedback to drive continuous optimization and adaptive evolution of parameters. The application realizes efficient risk identification and intelligent intervention of large model generated content, and significantly improves the accuracy and automation level of content security management.

A method and system of performing behavior based access control in an industrial automation system. A first device requests access to a second device within the industrial automation system. The second device requests a certificate from the first device. The first device transmits a certificate, which contains a device risk level. A risk assessment engine determines the device risk score by evaluating device security information. After validating the certificate, by a certificate authority processor, the device risk level is compared to a predetermined risk threshold. In response to the device risk level exceeding the risk threshold, a multi-factor authentication request is transmitted to the first device. Upon completion of the multi-factor authentication, the first device is granted access and connects to the second device. In response to the device risk level falling below the predetermined threshold, the first device is granted access and a connection opens.

This application provides an automatic method for identifying false information, belonging to the field of information identification technology. The method includes: acquiring a multimodal fusion feature set corresponding to multiple data sources; constructing a city-wide governance knowledge graph based on multiple entities corresponding to different entity types in city-wide governance government data and the relationships between these entities; performing an identification operation on each multimodal fusion feature in the multimodal fusion feature set: matching the geographical location information in the multimodal fusion feature with the coordinates of events contained in each node of the city-wide governance knowledge graph to obtain target nodes that match the multimodal fusion feature; weighting the matching degree between the multimodal fusion feature and the events corresponding to each target node based on the feature weights corresponding to each target node to obtain a comprehensive risk score; and determining that the information corresponding to the multimodal fusion feature is false information when the comprehensive risk score is greater than a preset risk threshold. This application can improve the efficiency of false information identification.

The application provides an overseas LNG project investment and economic evaluation template construction method and system, the method comprises the following steps: obtaining input data of an overseas LNG project, determining a multi-scheme engineering quantity template; dividing unit engineering and single-item engineering according to different technical schemes, and determining a work breakdown structure framework; obtaining overseas fiscal and tax policy data, determining tax rate and fee rate parameters, adapting to overseas market conditions, and obtaining fee combination data; determining investment cost breakdown structure and fee combination based on construction cost adjustment coefficient and the work breakdown structure framework; constructing a cash flow model based on the investment cost breakdown structure; determining risk parameters based on obtained gas price fluctuation parameters and exchange rate fluctuation parameters, adjusting the cash flow model to obtain an adjusted cash flow model; performing probability simulation analysis on the adjusted cash flow model, determining sensitivity analysis results based on obtained probability distribution of internal rate of return, and dividing and forming a risk prompt based on a preset risk threshold.

The application discloses a judgment method and device for preventing cheating of enterprise private car public employee clock-in based on big data, equipment and medium, relates to the technical field of big data, and comprises the following steps: collecting first multi-dimensional data of employees in a private car public clock-in scene; processing the first multi-dimensional data to obtain a first clock-in data set; analyzing the first clock-in data set to obtain a first risk point set; obtaining a risk quantitative score according to the first risk point set; comparing the risk quantitative score with a preset cheating risk threshold to obtain a first comparison result; if the first comparison result determines that the clock-in behavior is a cheating behavior, determining a corresponding warning level according to the risk quantitative score, and triggering a warning processing operation matched with the warning level. The method can accurately identify various clock-in cheating behaviors, and execute differentiated warning processing according to the risk level, thereby effectively improving the intelligent level and the automation degree of clock-in management in the private car public scene.

The present application relates to the technical field of power system distribution network monitoring, and more particularly to a distributed distribution network facility state monitoring and early warning method and device, comprising S1 full-scene distributed monitoring node deployment and data acquisition, forming a network without blind area and collecting monitoring data of each node, forming a standardized data package after preprocessing, S2 fault mode judgment, setting a scenario-based risk threshold, determining the comprehensive risk level, S3 fault location result calculation, outputting the fault mode, risk level and fault location result of each point, S4 early warning linkage and adaptive adjustment, grading pushing early warning information, interfacing with the emergency command system and distribution network operation and maintenance system, S5 result verification and parameter iterative correction, based on the error rate to iteratively correct the sensor sampling frequency, fault threshold and fault location model parameters, the present application realizes accurate monitoring and efficient early warning of distribution network facilities in all scenes, improves the monitoring reliability in extreme scenes, and ensures the safe operation of distribution network.

This invention discloses a construction site safety risk identification method based on multimodal spatiotemporal alignment and causal hypergraph, comprising the following steps: feature extraction from real-time collected multimodal construction site data; spatiotemporal alignment and normalization of the multimodal features to obtain a normalized multimodal state vector, and constructing a causal hypergraph based on this vector; risk evolution inference under multi-causal synergy based on the causal hypergraph to obtain a comprehensive risk score; and accurate identification of the construction site safety risk status by comparing the comprehensive risk score with a preset risk threshold. This invention solves the problem of existing construction site safety risk identification methods based on deep learning models, which rely on statistical correlation between data for risk determination, only outputting risk identification results but failing to trace and explain the root causes of the risks, leading to a significant reduction in the credibility and interpretability of the risk identification results.

The present application relates to the technical field of urban underground infrastructure safety monitoring, and particularly relates to a safety early warning method and system for underground pipe gallery multi-risk sources, which comprises the following steps: obtaining physical state monitoring data through each sensor of the underground pipe gallery and calculating the time series activity factor of each data at the current time, then judging the fast or slow change type of the data according to the factor and extracting the corresponding characteristic parameters; then calculating the original abnormality degree and the conduction confidence between the same layer sensor nodes according to the parameters, and correcting the significant abnormality degree according to the confidence threshold; finally, generating the risk source confidence by aggregating the significant abnormality degrees of the same type data, and determining whether the pipe gallery has risks and triggering the early warning according to the comparison result of the risk source confidence and the risk threshold. The present application precisely adapts to the multi-type risk evolution characteristics of the pipe gallery, effectively solves the lag and false alarm problems of the traditional early warning, and improves the accuracy and reliability of the pipe gallery multi-risk source early warning.

This invention relates to the field of database information management technology, and discloses a method, system, device, and medium for monitoring the operation and maintenance risks of databases. The method includes: acquiring collected multi-dimensional database data and preprocessing the multi-dimensional data; using principal component analysis to extract key features from the multi-dimensional data, performing key feature analysis to obtain principal components, and calculating a comprehensive evaluation coefficient; calculating the database indicator change rate coefficient and business importance coefficient, determining the activation intensity function, calculating the risk threshold adjustment range based on the activation intensity function and action space, and adjusting the original risk threshold according to the risk threshold adjustment range to obtain the current risk threshold; and obtaining the database operation and maintenance risk judgment result based on the relationship between the comprehensive evaluation coefficient and the current risk threshold. This invention effectively overcomes the drawbacks of traditional fixed risk thresholds by dynamically adjusting the risk threshold, providing a direct and reliable basis for operation and maintenance decisions.

The application discloses a municipal engineering quality evaluation method and system based on big data, and relates to the technical field of municipal engineering quality management. The method comprises the following steps: creating a unique identification code for a municipal engineering project and binding engineering data to form a data chain, comparing test data with standard data to generate a first difference signal, comparing actual cost with budget cost to generate a second difference signal, performing logical consistency verification to generate a first risk signal, calculating a comprehensive risk value according to the first difference signal and the first risk signal, generating a first early warning signal and triggering sampling inspection when the comprehensive risk value exceeds a third threshold value. The application creates tamper-proof data by creating a unique identification code and a data chain, identifies fraud behaviors affecting engineering quality through logical consistency verification, and adopts a dynamic risk threshold matrix and a self-adaptive sampling inspection algorithm to realize accurate allocation of supervision resources according to risk values, and comprehensively improves the initiative and accuracy of quality supervision.

The application relates to the technical field of intelligent manufacturing and industrial blockchain application, in particular to an intelligent production line general control platform system based on a blockchain and industrial big data, which comprises a data acquisition module, a causality analysis module, an intelligent contract execution module and a traceability management module; the data acquisition module acquires data through an industrial internet of things interface; the causality analysis module generates a dynamic defect risk index; the intelligent contract execution module compares the dynamic defect risk index with a preset risk threshold; if the dynamic defect risk index is greater than or equal to the preset risk threshold, process compensation instructions of a downstream node are generated, corresponding deviation compensation action information is updated and is stored in a chain; otherwise, a state maintenance mark is generated to control the downstream node to maintain a current parameter state, and the state maintenance mark is stored in a chain; the traceability management module generates a product full-life-cycle causality traceability tree; the application solves the problems of difficult cross-process causality tracing of a complex production line and unclear responsibility definition.

This invention relates to the field of Industrial Internet of Things (IIoT) and provides a method and system for supervising the pledge of base liquor based on multi-source trusted data fusion. The method includes: associating and binding the basic attribute information of the pledged base liquor containers with multiple types of heterogeneous sensing devices to generate digital identity identifiers; collecting multi-dimensional raw data from multiple heterogeneous sensing devices to obtain a multi-source heterogeneous dataset; performing consistency cross-validation and dynamic weight calculation on the multi-source heterogeneous dataset to obtain a multi-source consensus spatiotemporal stamp; digitally signing the multi-source consensus spatiotemporal stamp and associated data digest and submitting it to a consortium blockchain network to obtain an on-chain trusted state record; performing risk quantification processing through a multi-dimensional dynamic risk association model to obtain a comprehensive risk score; and triggering a level-based early warning and handling process based on the comparison result between the comprehensive risk score and a preset risk threshold. This invention achieves multi-source trusted data fusion and dynamic intelligent risk assessment of pledged base liquor assets.