127 results about "Risk index" patented technology

The present invention discloses a system and method for secure artificial intelligence-based financial technology governance and risk management, designed to provide real-time, autonomous, and verifiable compliance assurance within digital financial ecosystems. The invention integrates a secure artificial intelligence processing unit, a governance control processor, a cryptographically anchored storage unit, a federated learning coordination processor, and a quantum-resistant communication interface enclosed within a tamper-proof hardware structure. The system performs encrypted machine learning computations on financial transaction data using homomorphic encryption and trusted execution environments to preserve confidentiality during analysis. It computes a governance risk index based on probabilistic inference and anomaly detection to identify regulatory deviations, applies adaptive compliance reasoning across multi-jurisdictional frameworks, and automatically enforces governance actions through secure decision logic.

The invention relates to the field of data analysis, and discloses a financial statement dynamic analysis system based on multi-agent collaboration, which comprises a data input module, a preprocessing module, a data analysis module and a diagnosis output module, the data analysis module performs multi-dimensional parallel analysis from numerical trend, text semantics and business rules through cooperative work of a time sequence prediction agent, a text evaluation agent and a rule reasoning agent, and provides reference support by relying on a knowledge base; and the diagnosis output module synthesizes conclusions of the agents to generate a comprehensive financial risk index and judges a risk state. According to the method, the deterioration trend intensity value is calculated by monitoring the comprehensive financial risk index in the continuous report period, and the analysis granularity of the intelligent agent is evaluated according to the dynamic regulation and control text. Besides, for the identified significant risks, the system can automatically position key financial indexes and associated risk roots through an iterative positioning method in cooperation with numerical anomaly and text risk events.

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.

The invention discloses a time sequence production simulation multi-dimensional risk analysis method and system based on deep learning, and the method comprises the steps: constructing a time sequence production simulation model, and carrying out the calculation to obtain system operation state data; constructing a multi-dimensional risk index analysis system, and calculating an initial weight of each risk index; introducing a real-time state quantity corresponding to each risk index as a driving factor to correct the initial weight of each risk index to obtain a corrected dynamic weight; carrying out weighted fusion on the risk index values based on the dynamic weight to obtain a comprehensive risk value; forming a feature vector by the product of the drive factor normalized value corresponding to each risk index and the dynamic weight, taking the feature vector as input, taking the corresponding comprehensive risk value as output, training the GRU model, and taking the trained GRU model as a risk analysis model; and processing the operation data of the target system according to the steps to obtain a corresponding feature vector, and inputting the feature vector into the analysis model to obtain a comprehensive risk value of the target system. According to the invention, the sensitivity and accuracy of risk identification are improved.

The invention discloses a risk assessment method and system for access of new energy photovoltaic power generation to a power distribution network, and belongs to the technical field of operation safety of the power distribution network, and the method comprises the steps: collecting illumination resource data and load data of the power distribution network, and generating a basic data set covering typical and extreme scenes through empirical mode decomposition, Copula function and improved Latin hypercube sampling; a coupling evaluation model fusing the graph neural network and the risk propagation theory is constructed, an improved CRITIC-entropy weight method is adopted to dynamically empowerly weight the multi-dimensional risk indexes, and a time-varying comprehensive risk level matrix is output; analyzing the risk characteristics, matching a pre-constructed measure risk knowledge graph, and generating a dynamic self-adaptive coping scheme through conflict verification and priority ranking; verifying the scheme in the digital twin power distribution network simulation model, and updating model parameters and a basic data set by incremental data feedback when the scheme does not reach the standard; according to the invention, the comprehensiveness and timeliness of risk assessment are improved.

The invention is suitable for the technical field of data processing, and particularly relates to a financial data processing and classification method, system and equipment based on verifiable computing, and the method comprises the steps: obtaining on-chain transaction data verified by a block chain consensus node storage and risk-related index data verified by an under-chain oracle machine; the problems that the authenticity of traditional financial data is difficult to verify and the data is split are solved; calling a risk assessment model through an intelligent contract deployed on a block chain, and placing an assessment process in a decentralized block chain network; performing verifiable calculation through an intelligent contract to determine a total performance score and a risk index, and realizing whole-course tracing and verification of a calculation process by utilizing the characteristics of verifiable calculation of a block chain; the total performance score and the risk index are stored in the block chain as non-tampering records, and the risk rating groups are automatically divided according to the fixed rating rule on the chain through the smart contract, so that the authenticity, objectivity, verifiability and fairness of financial data processing are guaranteed.

The invention relates to the technical field of risk management, and discloses a financial AI large model-oriented multi-dimensional test intelligent risk management and control method, which comprises the steps of integrating channel test, data test, AI test and AI large model test by constructing a financial AI large model test special database; traditional financial sensitive fields and AI test specific sensitive fields are accurately identified based on data consanguinity analysis, and dynamic desensitization is realized in combination with a lightweight desensitization engine; calling a preset risk scene library, defining a risk index system of each scene, dynamically calculating a risk level through a weighted scoring algorithm, and carrying out modeling by using a graph database to identify hidden linkage risks; triggering a grading response strategy according to the risk grade, and dynamically optimizing resource allocation; and after the test is finished, a multi-dimensional risk thermodynamic diagram and a root cause analysis report are generated, and processing experience is fed back to the scene library optimization strategy. According to the invention, the efficiency and safety of risk management and control can be improved.

The invention provides an industrial project cross-department collaborative approval and supervision system based on trusted computing, and relates to the technical field of cross-department processing, and the system comprises an extraction module which is used for triggering an intelligent contract deployed on an alliance chain based on an on-chain evidence storage record, generating an approval event, and sending the approval event to a server; according to the data integrity, the process time sequence and the operation behavior characteristics in the approval process event, each behavior characteristic is quantified into a risk index corresponding to each dimension through a preset risk model, and a set of the risk indexes of each dimension forms a multi-dimensional vector; through on-chain credible evidence storage, multi-dimensional risk measurement, dynamic credit evaluation and closed-loop regulation and control, precision, credibility and dynamic optimization of industrial project cross-department collaborative approval and supervision are realized, approval efficiency and supervision efficiency are improved, and scientificity and safety of collaborative management and control are guaranteed.

The invention discloses a power distribution network risk assessment method for massive distributed resource access, and the method comprises the following steps: dividing a risk domain according to the related information of a power distribution network, carrying out the stratified sampling of the risk domain, carrying out the Monte Carlo simulation, obtaining a sample matrix of risk indexes, inputting a random forest model, and calculating the importance score of the risk indexes, screening key risk indexes based on the importance threshold; taking the importance score as an objective weight, calculating a subjective weight by using an analytic hierarchy process, calculating a difference degree according to the subjective and objective weights, and fusing the subjective and objective weights to obtain a comprehensive weight; and obtaining a key risk index characteristic value in a to-be-evaluated scene, calculating the membership degree of the key risk index after adjusting the boundary parameter of the membership degree function according to the related information of the to-be-evaluated scene, calculating the comprehensive risk index according to the comprehensive weight and the membership degree of the key risk index, and judging the risk level. The method can fully consider the topological characteristics of the power distribution network, is suitable for the dynamic access of distributed resources, and achieves the precise risk assessment.

The present invention relates to the technical field of international trade risk management, and particularly relates to an intelligent international trade risk prevention and control method and system based on multi-modal data analysis. The method comprises: collecting transaction records, logistics information, policies, laws and regulations, and dynamic market data; extracting abnormal behavior features and evaluating a transaction risk grade; in view of logistics and policy data, evaluating the degree of influence of a cross-border transportation risk, so as to obtain a comprehensive risk index; and by means of the dynamic market data, estimating an overall risk level for early warning. The method can improve the accuracy and real-time response capability of international trade risk prediction, thereby meeting intelligent risk management requirements in complex environments.

The invention relates to the technical field of software engineering and automatic testing, and discloses a self-adaptive multi-dimensional risk testing strategy design method and system and a storage medium. The method comprises the following steps: triggering a risk assessment service through a CI / CD assembly line, preprocessing original data obtained by a plurality of heterogeneous data sources, and generating a risk feature vector; outputting a multi-dimensional risk index by using the risk assessment model, fusing the multi-dimensional risk index and the dynamic weight thereof based on an adaptive weighted fusion model, and calculating a comprehensive risk score; dynamically updating a risk score threshold value used for dividing the risk level, automatically generating a test strategy matched with the current risk level according to a preset mapping rule of the risk level and the test strategy, and calling the test suite to automatically execute the test strategy; and collecting a test execution result and online operation data as training data, and regularly optimizing parameters of the risk assessment model. The test strategy can be dynamically adjusted according to the real-time risk situation.

This disclosure relates to quality risk detection technology, and provides a quality risk detection device and method, which acquires quality performance information of products manufactured based on design conditions that have a predetermined level or higher similarity to the design conditions from a first database; calculates a first quality risk index or a second quality risk index by judging whether the number of acquired quality performance information is N (N is an integer greater than or equal to 1) or more, based on a judgment result; and outputs the quality risk detection result of the design conditions based on either the first quality risk index or the second quality risk index.

The invention discloses an intelligent early warning method and device for anti-electricity fraud and a medium, and relates to the technical field of data processing methods based on financial purposes. The method comprises the following steps: acquiring multi-source data from a plurality of heterogeneous data sources, and performing data treatment on the multi-source data to generate a standardized risk data set; the multi-source data comprises a real-time data stream, a quasi-real-time data stream and an offline data stream, and the data management comprises data splicing, data cleaning and data labeling processing; based on the standardized risk data set, executing index calculation through a calculation engine to generate a plurality of risk indexes, and inputting the risk indexes into a multi-modal risk decision engine to execute expert rule decision and machine learning model decision in parallel to generate a risk early warning signal; generating a risk check list based on the risk early warning signal, and issuing the risk check list to a specified mechanism to execute a corresponding disposal action; the processing actions comprise transaction blocking, account management and control and verification issuing.

The invention relates to the technical field of data processing, and discloses a commercial tenant risk rating method and system based on multi-source data fusion, and the method comprises the steps: obtaining the multi-dimensional data of a commercial tenant, and carrying out the heterogeneous data alignment, and obtaining the standard data of the commercial tenant; performing data credibility evaluation on the standard data to obtain a quality score of the standard data; based on the quality score, performing multi-dimensional feature fusion on the standard data to obtain merchant associated feature data of the merchant; based on a preset risk research and judgment standard, carrying out risk quantitative analysis on the merchant associated feature data to obtain a risk index of the merchant; performing comprehensive evaluation on the risk index to obtain risk evaluation information of the merchant; performing level threshold comparison on the risk assessment information to obtain a risk level of the merchant; according to the invention, the accuracy of merchant risk rating based on multi-source data fusion can be improved.

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.

The invention relates to the field of financial transactions, and particularly discloses a stock transaction risk control method and device, equipment and a medium. The method comprises the following steps: constructing a standardized data set which contains technical indexes and is subjected to LinearSVC sentiment analysis through data acquisition and processing; performing basic stock price prediction by adopting machine learning or a neural network model; constructing a state vector containing VaR and CVaR risk indexes by using reinforcement learning, performing risk perception correction on a prediction result through a TQC algorithm, and generating a transaction signal; and finally calculating and displaying a risk income index and a net value curve. According to the method, deep learning prediction and reinforcement learning risk control are creatively combined, risk factors are subjected to internal biochemical treatment in a prediction stage, forward-looking inhibition of market tail risks and dynamic optimization of transaction signals are realized, the problem of separation of prediction and risk control links in a traditional transaction system is effectively solved, and the risk control efficiency is improved. The earnings after risk adjustment are significantly improved, and an integrated solution with intelligent prediction and robust risk control is provided for security transactions.

The embodiment of the invention provides a risk transaction early warning method and device, equipment, a storage medium and a program product, and relates to the field of financial science and technology. The method comprises the steps of obtaining transaction behavior data of a target transaction, generating behavior characteristic data based on the transaction behavior data, matching the behavior characteristic data with a plurality of preset rules in a preset rule base to obtain a rule deviation degree of the target transaction, and matching the behavior characteristic data with a plurality of historical behavior characteristics to obtain a rule deviation degree of the target transaction. According to the method, the historical case deviation degree of the target transaction is obtained, then the risk index of the target transaction is determined based on the rule deviation degree and the historical case deviation degree, a hierarchical early warning mechanism is triggered based on the risk index, implicit illegal behaviors such as improper commitment and an abnormal operation chain are timely and comprehensively identified, and a more accurate risk detection result is obtained. Therefore, the accuracy and timeliness of risk detection are improved, and workers can process corresponding risk transactions in time based on differential early warning.

The invention discloses a dynamic quantitative evaluation method and device for comprehensive risks of network public opinions, and the method comprises the steps: constructing an index system of network public opinion risk symptoms, the index system comprising a public opinion symptom index set and a public opinion symptom index association graph; based on an actually occurring public opinion risk event sequence, matching a public opinion symptom index set, generating an incidence matrix according to the public opinion symptom index association graph, and quantifying an association risk value of each public opinion risk event through risk intensity; based on a risk time sequence attenuation model, performing time attenuation calculation on the associated risk value of each public opinion risk event to obtain a real-time risk value; on the basis of all real-time risk values, the comprehensive risk index of the network public opinions is calculated through an improved logistic model, factors in multiple aspects are considered, mathematical modeling is combined to simulate a real scene, and a wait index association diagram, a risk time sequence attenuation model and the improved logistic model are introduced in the calculation process, so that the evaluation result tends to the real situation more.

The invention discloses a bidding margin intelligent management method and system based on a dynamic risk portrait, and the method comprises the steps: building a dynamic risk portrait through obtaining project attribute data and supplier multi-source heterogeneous behavior data, and inputting a pre-trained bidding risk prediction model to calculate a dynamic risk index; and synchronously executing cash deposit payment state rule verification and dynamic risk index evaluation at the bid opening verification node, generating a comprehensive verification report, calculating a real-time commercial credit score through a credit evaluation engine, generating a differentiated cash deposit management strategy according to the score, and outputting a final state scheme. According to the invention, the conversion of bidding margin management from formal review to risk intelligent early warning is realized, the bidding risk is effectively identified, the risk prevention and control accuracy is improved, and the safety and intelligent level of government purchase are significantly improved based on a differentiation strategy of real-time credit scoring.

The invention provides a power distribution network dynamic risk early warning method and system considering multi-factor coupling, and the method comprises the steps: collecting basic parameters and historical and predicted data of a power distribution network, constructing a power distribution network time sequence risk early warning model considering multi-factor coupling, generating an initial scene through Latin hypercube sampling, and carrying out the early warning of the power distribution network dynamic risk. A typical scene is obtained through reduction of a depth auto-encoder and fuzzy C-means clustering; on the basis of a time sequence risk early warning model, time sequence load flow calculation is carried out on a typical scene, a time sequence risk calculation model is constructed on the basis of element layer risk indexes, a dynamic risk aggregation model is established with system layer risk indexes as the core, and daily comprehensive risk indexes are obtained through dynamic aggregation of an attention mechanism; based on index calculation, counting a historical early warning result and an actual power grid event, and grading risk indexes according to a daily comprehensive risk index and a reduced supply load ratio; and outputting a risk early warning result. According to the invention, the accuracy and timeliness of dynamic risk early warning of the power distribution network can be improved.

The invention discloses a financial power transmission right transaction decision optimization method and system based on multi-dimensional dynamic modeling, and relates to the technical field of power market optimization, and the method comprises the steps: collecting multi-source data of a power transmission market, carrying out the preprocessing, extracting a multi-dimensional feature matrix, carrying out the multi-model dynamic collaborative modeling, and generating a joint prediction result; risk index assessment is carried out based on the joint prediction result, and a financial power transmission right transaction decision target expression is generated in combination with constraint conditions by taking revenue maximization and risk minimization as double targets; and solving the financial power transmission right transaction decision target expression by adopting an improved particle swarm optimization algorithm to obtain an optimal financial power transmission right transaction decision scheme. According to the method, the joint prediction result is realized through multi-model dynamic collaborative modeling, the transaction decision target expression is obtained by taking benefit maximization and risk minimization as double targets, and the improved particle swarm optimization is introduced for adaptive optimization, so that the accuracy and robustness of transaction decision can be effectively improved.

The invention relates to the technical field of computer data processing, and discloses a dynamic standing book and risk early warning method for a bill full life cycle, and the method comprises the steps: collecting structured bill information and external credit investigation data; establishing a dynamic machine account by using the collected information, and performing automatic circulation management on the life cycle state of the bill based on a state machine model; performing risk monitoring on bills in the dynamic machine account, calculating credit risk scores of acceptors through weighted fusion of external credit scores and internal performance scores, and further calculating bill cashing risk indexes in combination with bill amounts and remaining days; and finally, based on the state change event or the risk early warning result of the bill, business and financial integrated cooperative processing is executed. According to the invention, automation and refinement of bill management are realized, a quantitative and dynamic risk early warning mechanism is established, deep fusion of business and financial processes is promoted, and management efficiency and risk prevention and control capability are significantly improved.

The application provides a financial risk prediction method based on multi-modal data fusion, and belongs to the technical field of artificial intelligence risk prediction, and comprises the following steps: S1, audio feature vector extraction is performed on a financial report telephone conference of a target company; S2, text summary feature vector extraction is performed on the financial report telephone conference of the target company; S3, a large language model is used to summarize the text summary of the financial report telephone conference of the target company, and an embedding vector corresponding to the summary of the text summary is obtained; S4, news text features of the target company are extracted, and a news text feature vector is obtained; S5, feature extraction is performed on time series transaction data of the target company in a period before a target date, and a time series data feature vector is obtained; S6, a joint representation vector is obtained; and S7, the joint representation vector is input into a multi-task learning framework to perform risk index prediction. The application has the advantages of significantly improved prediction accuracy, comprehensive utilization of implicit information, and good multi-task learning prediction capability.

The invention discloses an insurance service intelligent management system and method based on cloud computing, and relates to the field of data management, and the system comprises an identification module which is used for traversing electronic insurance policies stored in an insurance service background, and extracting insurance policy expiration time and associated key information from each electronic insurance policy based on character identification; the risk assessment module is used for acquiring insurance policy expiration time and associated key information extracted by the electronic insurance policy based on character recognition, and assessing an insurance policy risk index based on the insurance policy expiration time and the associated key information; according to the method, the expiration time and the related information of the electronic insurance policy are acquired through real-time traversal of the electronic insurance policy in the insurance service background, so that the risk index of the insurance policy is digitally evaluated based on the acquired information, the insurance policy to be prompted is further selected based on the evaluation result, interaction with online salesmen in the insurance service background is synchronously formed, and the risk of the insurance policy to be prompted is effectively improved. And the processing task of the to-be-prompted insurance policy is distributed to the online salesman, so that the insurance policy protected user can adaptively and effectively manage and pay attention to the insurance policy.

The embodiment of the invention provides a power transmission line icing falling jump risk influence factor analysis method and device, and the method comprises the steps: determining a risk index value under each working condition of a power transmission line and a line deicing jump influence factor, and enabling the risk index value to be related to the maximum jump height of a lead of the power transmission line; constructing a first matrix based on the working conditions, the influence factors and the risk index values; performing standardization processing on the first matrix by adopting a range standardization algorithm to obtain a second matrix; determining a standard deviation of each influence factor based on the second matrix; and determining the weight of each influence factor based on the standard deviation of each influence factor, wherein the weight represents the influence degree of the influence factor on the icing shedding jump risk of the power transmission line.

The invention discloses a big data security risk assessment method and system, and relates to the technical field of big data processing, and the method comprises the following steps: obtaining a big data operation scene type in an operation state, service data streams and system operation logs corresponding to the scene types are collected in real time or periodically through a data collection module; determining a risk modeling mode corresponding to the operation scene type according to the operation scene type; extracting and determining risk assessment parameters for subsequent analysis; and performing comprehensive processing and analysis on the original risk indexes through the risk assessment parameters, thereby generating a safety risk assessment result and outputting the safety risk assessment result. The problems that an existing method is insufficient in authenticity and poor in adaptability can be effectively solved. By obtaining the big data operation scene type and combining the real-time service flow log and the historical security event data, the risk modeling mode matched with the scene is dynamically selected, and therefore the pertinence and flexibility of risk modeling are guaranteed.

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 invention discloses a use case design method based on an agile test, which comprises the following steps: dividing a system to be tested into different function modules, and determining the weight of each function module; designing a basic test case for each functional module; automatically executing the test cases, and recording the number of defects of each functional module and the number of the test cases; calculating the defect density and the risk index of each functional module; supplementing test cases for the functional modules with the risk indexes higher than a threshold value; and performing iterative optimization. According to the embodiment, key test modules of the system are identified by using a data analysis technology, and test resources are focused on key tests of the key modules; by analyzing the execution condition of the historical use cases and defect data, the test use cases are supplemented in a targeted mode, the dynamic test use case set is constructed, and the contradiction between quality and efficiency in agile testing is effectively solved. On the premise of ensuring the defect detection rate, the test resources are concentrated to carry out key tests on key modules, so that the test resources are redistributed, and the test efficiency and the defect discovery capability are remarkably improved.

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.

The application particularly relates to a power system risk assessment method based on a primary-secondary coupling multiple fault model, which comprises the following steps: calculating a power transmission line fault probability of a power system; performing multiple fault sampling based on a Latin hypercube sampling method; establishing a corresponding primary-secondary coupling multiple fault model for each scene; determining a power transmission line fault state and a protection action state, and then assessing a risk index of the corresponding scene; calculating a corresponding comprehensive severity index based on the risk index of a single scene, and then obtaining one or more scenes with the highest comprehensive severity index through screening and sorting to analyze weak links of the power system; and calculating a system risk value by comprehensively considering the risk index of all scenes and a protection refusal probability, so as to assess the overall operation risk of the power system. The application can reflect the influence of the expansion of the fault range caused by incorrect protection action on the operation safety of the power grid, and can normalize and scientifically sort different dimension risk indexes.