26 results about "Risk index" patented technology

The application discloses a kind of based on risk scene's electric power market parallel clearing method and system, it is characterized by: S1, multi-dimensional risk scene synchronization generation;S2, scene space-time feature extraction;S3, scene clustering group;S4, distributed parallel clearing calculation;S5. Risk index aggregation analysis.The application improves risk coverage by scene coupling generation, solves the problem that scene and clearing calculation are separated and cannot capture multiple risk coupling effects;Through distributed architecture for clearing calculation, improve the efficiency of calculation;Through risk index, the cross-market chain reaction effect can be quantified.The application improves risk coverage by scene coupling generation, solves the problem that scene and clearing calculation are separated and cannot capture multiple risk coupling effects;Through distributed architecture for clearing calculation, improve the efficiency of calculation;Through risk index, the cross-market chain reaction effect can be quantified.

This application relates to a risk assessment method, apparatus, device, storage medium, and program product for secondary equipment. Based on the fluctuations and trends of alarm frequency sequences associated with the inherent risk factors of the target equipment, the risk index of the inherent risk factors of the target equipment at the current moment is calculated. Based on the risk index of the target factors, the dynamic conditional probability of the target factors is determined using a logistic regression model. The target factors are inherent risk factors with parent nodes in a dynamic Bayesian network. The dynamic conditional probability is input into the dynamic Bayesian network, and the systemic risk probability of the target factors is determined through a Bayesian inference algorithm. Based on the systemic risk probability of the target factors, their operational impact factors, economic impact factors, and safety impact factors, the risk assessment result of the secondary equipment is determined using a comprehensive risk index expression. This improves the accuracy of the obtained risk assessment results for secondary equipment.

The application discloses a big data-based audit risk prediction method and system, relates to a data processing system or method specially suitable for administrative, commercial, financial, management, supervisory or predictive purposes, and in the method, a quantitative data structure and an individual operation benchmark value are generated by analyzing historical operation data of stores, group division is carried out based on the similarity between the stores, and a group operation benchmark value is calculated. According to the scheme, the weight coefficients of a group deviation value and an individual deviation value are dynamically adjusted according to the opening time length of the stores, the two types of deviation values are weighted according to the weights to obtain a comprehensive risk index, and when the comprehensive risk index exceeds a preset threshold value, a risk early warning signal is output. The application is used for improving the audit risk early warning accuracy of stores with different operation scales.

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 provides an AI-based risk control method, system, and computer-readable storage medium for fraudulent orders. Standardized addresses for each order are obtained through preprocessing, enabling refined part-of-speech tagging. Machine learning algorithms are used to construct various risk models to calculate the risk index for each order. The combined risk indices of each risk model for the same order are then used to obtain a comprehensive risk index for all orders. By introducing a target vector database, historical target addresses associated with the standardized addresses are quickly retrieved from the target address vector database, rapidly identifying scenarios where historical target addresses are used for fraudulent orders again. This solves the problem that risk control models can only identify low-level fraudulent orders with obvious rules but cannot identify high-level fraudulent orders without obvious rules, thus improving the fraudulent order identification rate of the risk control method.

This application relates to a method, system, device, and medium for monitoring financial risks of supply chain assets. The method includes: acquiring multi-source heterogeneous data of the target supply chain; fusing entity identifiers and relationships within the multi-source heterogeneous data to construct a supply chain knowledge graph; performing network topology feature analysis on key assets in the supply chain knowledge graph to obtain dynamic asset profile data; inputting the dynamic asset profile data into a hierarchical fusion assessment model for risk quantification to obtain preliminary risk score data; based on the preliminary risk score data and the supply chain knowledge graph, using the preliminary risk score data as the initial risk source, performing risk transmission simulation and iterative calculation on the supply chain knowledge graph to obtain supply chain transmission risk index data; and performing visual early warning and source tracing analysis on the supply chain transmission risk index data to obtain risk monitoring results. This method enables dynamic source tracing and intelligent risk monitoring of financial risks of supply chain assets.

This invention relates to the field of equipment management technology in thermal power plants, and provides a method and apparatus for determining the comprehensive risk index of a flue gas system. The method includes: acquiring a system structure diagram of the flue gas system, which includes a primary air fan, a forced draft fan, an induced draft fan, an air preheater, and an electrostatic precipitator; eliminating electrostatic precipitators that have no substantial impact on power generation reliability based on prior knowledge; automatically identifying the series and parallel logical relationships between equipment based on the eliminated system structure diagram; and calculating the comprehensive risk index by applying series and parallel risk formulas in a hierarchical manner based on the identification results. This invention can automatically eliminate invalid equipment, avoiding its interference with the evaluation results, automatically identify series and parallel logical relationships, avoid subjective biases caused by manual pre-setting, achieve accurate quantification of the risk index, and provide a scientific basis for graded maintenance of equipment.

The application discloses a kind of assimilation weather radar data's prediction system, specifically related to weather forecasting field, including radar data acquisition module, physical knowledge construction module, physical constraint extraction module, multi-source data assimilation module and industry risk index generation module.A kind of assimilation weather radar data's prediction system is realized to the hierarchical physical discrimination and suppression of ground clutter and superrefraction echo by radar data acquisition module, improves the data reliability of meteorological decision basis in asset dispatching management scene;Through physical constraint extraction module, the multi-objective joint optimization of microphysical process constraint, thermodynamic equilibrium constraint and non-meteorological noise suppression is realized, the credibility of meteorological element deduction result in high-reliability commercial scene is significantly improved, so that the generated feature analysis field has the physical rationality of supporting key asset management decision, improves the scientificity of administrative decision basis and the accuracy of financial risk assessment.

Embodiments of the present application disclose a high-temperature pressure-bearing component risk assessment method and system based on multi-dimensional parameters, wherein the method comprises collecting basic parameters, operation data and inspection and detection data of a target component, and preprocessing the basic parameters and the inspection and detection data; performing risk quantitative analysis on the preprocessed inspection and detection data to generate risk values corresponding to each item of inspection and detection data; performing risk index calculation based on each risk value and performing risk grade division; and outputting a risk assessment report and a repair strategy based on the risk grade. The system comprises a data acquisition module, a quantitative analysis module, a risk calculation module and a strategy generation module to respectively perform the above steps. By coupling key parameters such as wall thickness, defects, hardness and stress with weighting, risk assessment based on multi-dimensional parameters is realized, the stability and reliability of the evaluation results are improved, and through preprocessing, standard dynamic updating is realized to improve the timeliness of the evaluation.

The present application relates to the technical field of contract data processing, and specifically discloses an enterprise intelligent legal affairs auditing method and system based on deep learning, which performs multi-modal preprocessing on multi-source contract data, generates clause-level semantic representation through semantic encoding and context enhancement, constructs a risk identification model based on the semantic representation, extracts risk features in multiple dimensions and calculates clause risk scores, and identifies source risk clauses; the explicit and implicit reference relationships between clauses are extracted to construct a contract clause association network, and the risk association degree is calculated based on the network structure to screen out associated risk clauses; a risk propagation subgraph is constructed through risk propagation analysis to quantify the diffusion path and range of risks in the contract, and then the overall risk index of the contract is evaluated, and the risk results are structured and output, which can effectively improve the risk identification capability.

The application discloses an intelligent risk assessment method and system based on engineering drawing changes, which comprises the following steps: when the engineering drawing changes, a new engineering drawing after the change is obtained, and a structured change content vector is generated according to the change difference identified from an old engineering drawing; according to the dependency relationship between the engineering drawing and the downstream federal data object, the change content vector is mapped to the downstream federal data object, and the attribute offset vector of the downstream federal data object caused by the change content vector is obtained for each downstream federal data object; a risk assessment model is set, a risk index is calculated according to the attribute offset vector, and a risk report is generated, wherein the risk report comprises a risk index, a change content vector and an attribute offset vector.

This invention discloses an intelligent auto insurance assessment method and system based on data analysis, specifically relating to the field of auto insurance assessment technology. It involves time-series alignment and noise reduction of continuous driving behavior data streams and road environment data streams collected from in-vehicle and mobile terminals. This process identifies risk events during driving, such as distraction, changes in following distance, and braking response. State transition detection is performed based on the chronological order of these events to delineate risk accumulation zones. A risk chain graph is constructed by calculating the time-delay correlation strength of adjacent risk events within the risk accumulation zone. The continuous contraction of risk margins in the chain path is identified, resulting in a chain risk index. Finally, the insurance risk level and premium rate coefficient are determined based on the chain risk index, ultimately achieving intelligent and precise auto insurance risk assessment and premium pricing, improving the accuracy and rationality of auto insurance assessment.

The present application relates to the field of engineering cost risk assessment, and discloses a kind of engineering cost risk assessment early warning method and management intelligent system;The method obtains engineering list data, contract constraint data, resource consumption data, supplier quotation data, market price data, historical transaction data, change visa data and engineering progress data, establishes the multi-level cost object correlation model of project layer, unit engineering layer, list item layer and resource item layer;Standardized processing is carried out on different source quotation records and is merged into comparable quotation unit, and reference price interval and price fluctuation parameter are generated;Inconsistency check is carried out in combination with quotation source, quotation time, caliber completeness and contract consistency, and evidence consistency coefficient is obtained;The quantity-price transmission relationship from resource item to project total price is established, the forward-looking risk index is calculated, the early warning level and risk source are determined;Then re-calculation and re-evaluation are carried out on disposal scheme, and closed-loop early warning management is realized.

The present application relates to the technical field of financial asset management, in particular to an AI-driven bank asset allocation intelligent optimization decision method and system. Obtain the public opinion risk index of the target enterprise; call the dispute information of the target enterprise, analyze the self dispute risk index of the target enterprise based on the pre-trained dispute information analyzer; call multiple dispute risk indexes of multiple cooperative enterprises of the target enterprise, and calculate the business relationship risk index of the target enterprise; obtain the investment rationality index and the industry prospect index of the target enterprise, and calculate the enterprise prospect index of the target enterprise based on the investment rationality index and the industry prospect index; the weighted sum of the multiple risk indexes of the target enterprise is obtained, and the comprehensive investment risk index of the target enterprise is obtained, which is used for asset allocation recommendation. The present application significantly improves the comprehensiveness, accuracy and forward-looking of the bank's enterprise investment risk assessment.

The application discloses a risk feature extraction method for a main microgrid, and comprises the following steps: firstly, constructing a new energy output probability model and a system element operation model, and generating multiple system operation scenarios by using a sequential Monte Carlo method; then, determining an active power and reactive power dispatching optimization model, wherein the active power optimization takes the minimum system operation cost as an objective function, and the reactive power optimization takes the minimum system operation network loss as an objective function; subsequently, determining decision variables and constraint conditions of the dispatching evaluation model, and solving system state physical quantities by power flow calculation; then, constructing a risk criterion, determining a risk state data set of each scenario, inputting a random forest algorithm model to quantitatively analyze the risk importance of system features. Finally, determining a risk quantification evaluation method, and constructing a multi-level risk index system according to system risk features. The application can identify key influencing factors under a risk scenario.

This invention belongs to the field of artificial intelligence technology, specifically disclosing an automated auxiliary method and system for the litigation liability insurance underwriting process based on AI document parsing. Through intelligent extraction of legal document content and case element information, it achieves end-to-end parsing from legal documents to structured data, improving the efficiency of document parsing. By constructing case element vectors and employing a mathematical risk matching mechanism, it transforms fuzzy underwriting risks into calculable indicators, avoiding human oversight. A comprehensive risk index quantifies and compares the risk level of cases, enabling pre-filling of insurance applications or risk warnings based on risk assessment results, thus automating the litigation liability insurance underwriting process. It provides effective case support for underwriters, significantly reducing cognitive burden. Furthermore, it facilitates seamless integration with existing business platforms and underwriting terminals, solving the problem of information silos and improving the collaborative processing efficiency of litigation liability insurance underwriting business.

This invention relates to the field of product management technology, and more particularly to a blockchain-based product quality traceability method, comprising: obtaining a unique digital identifier and a quality risk sensitivity coefficient for the target product; collecting risk assessment data during transportation, determining a risk assessment cycle characterization value to determine a risk accumulation value and several risk cycle intervals; determining whether to mark the target product as a logistics risk warning based on the risk accumulation value, and determining a quality assessment risk index based on the mapping relationship between the risk accumulation value and historical quality risks; determining whether to proactively generate a product quality traceability request for the risk cycle interval based on the quality assessment risk index, thereby determining the key risk cycle interval; and determining whether to optimize the quality risk sensitivity coefficient based on the accuracy verification of the key risk cycle interval and the actual quality risk index after transportation. This invention improves the traceability efficiency and accuracy of quality damage during transportation.

This invention discloses a forecasting system for assimilating weather radar data, specifically relating to the field of weather forecasting. It includes a radar data acquisition module, a physical knowledge construction module, a physical constraint extraction module, a multi-source data assimilation module, and an industry risk index generation module. The weather radar data acquisition module enables hierarchical physical identification and suppression of ground clutter and super-refractive echoes, improving the data reliability of meteorological decision-making in asset scheduling and management scenarios. The physical constraint extraction module achieves multi-objective joint optimization of microphysical process constraints, thermodynamic equilibrium constraints, and non-meteorological noise suppression, significantly improving the credibility of meteorological element extrapolation results in high-reliability commercial scenarios. This ensures the generated feature analysis field possesses physical rationality to support key asset management decisions, enhancing the scientific basis of administrative decisions and the accuracy of financial risk assessment.

The application discloses a financial overdue risk intelligent early warning system and algorithm fusing time sequence data and belongs to the technical field of financial risk management and control. The system comprises seven cooperative modules, forms a whole-process closed-loop management and control, can collect six types of multi-source time sequence data and complete hierarchical fusion processing, and the algorithm comprises three original core formulas, realizes time sequence fusion weight calculation, risk indicator quantization and dynamic weight iteration. The application solves the defects of the prior art, such as insufficient time sequence fusion, model solidification and early warning lag, can automatically extract time sequence risk characteristics, realizes 7-60 day early warning, the early warning accuracy rate is more than 90%, and the misjudgment rate and the omission rate are both less than 5%. The application is suitable for all financial business scenes, is fine in risk grading, is safe and compliant in data, can effectively reduce the non-performing asset rate and operation and maintenance cost of financial institutions, and provides an efficient, accurate and intelligent overdue risk management and control solution for various financial businesses.

An institutional risk assessment method and apparatus are disclosed. The institution uses known inherent risk rules to assess customer data, obtaining first-risk customer data that matches the rules, and calculates known inherent risk index values. Next, based on risky customers and / or risk events not directly related to the institution contained in acquired external intelligence, a second-risk customer data set is selected from the customer data that did not match the rules, and an unknown inherent risk index is constructed, calculating its probability score. An inherent risk value is determined based on the known inherent risk index value and the unknown inherent risk probability score. Residual risk indicators are extracted from the acquired risk cases, and corresponding index values ​​are calculated; the remaining risk value is determined based on these index values. A risk assessment result for the institution is determined based on the inherent risk value and the remaining risk value. The risk cases include risk events directly related to the institution that have occurred or been discovered. The customer data contains private data, which requires privacy protection.

The application discloses a new energy photovoltaic power generation access power distribution network risk assessment method and system, belongs to the power distribution network operation safety technical field, including: collecting illumination resource data and power distribution network load data, through empirical mode decomposition, Copula function and improved Latin hypercube sampling to generate basic data set covering typical and extreme scenarios; a coupling evaluation model combining graph neural network and risk propagation theory is constructed, an improved CRITIC-entropy weight method is used to dynamically weight multi-dimensional risk indexes, and a time-varying comprehensive risk grade matrix is output; the risk characteristics are analyzed and the pre-constructed measure risk knowledge graph is matched, a dynamic self-adaptive response scheme is generated through conflict checking and priority sorting; the scheme is verified in a digital twin power distribution network simulation model, and when the scheme does not meet the standard, the model parameters and the basic data set are updated through incremental data feedback; the application improves the comprehensiveness and timeliness of risk assessment.

The application provides a technology achievement transformation feasibility evaluation system and method based on multi-source data, relates to the technical field of technology achievement transformation, and comprises a server for processing data, a system construction method for constructing a risk evaluation system and a prediction method for optimizing the risk evaluation system. The modules used in the application are designed for the data types to be processed, and internet data is accessed in the data analysis process to construct a risk index system and a risk evaluation model in a big data assisted verification manner. According to the design of calculating other data according to the change data, the difficulty of data acquisition can be greatly reduced, the workload of the management team is reduced, the time of risk evaluation is greatly saved, the risk evaluation result is more time-efficient than the risk evaluation result obtained in the general manner, and the success of technology achievement transformation is positively promoted.

The application provides a kind of enterprise credit risk dynamic early warning system and method based on behavior change, belong to data processing technical field, this early warning system includes: rule configuration module, for configuring risk early warning rule, data acquisition and processing module, for real-time acquisition of enterprise loan business based on behavior change Data, and the standardized data obtained by standardizing the collected data;Risk monitoring module, based on early warning rule and standardized data dynamically calculate risk index value;Early warning trigger module compares risk index value with preset early warning threshold and trigger condition in real time, generates early warning event, information push and tracking module instant directional push to early warning event, and dynamically track the risk disposal process of early warning event.The embodiment of the application can realize the real-time acquisition, processing, monitoring and accurate push of data, and provide strong dynamic prevention and control support for risk compensation business.