308 results about "Risk index" patented technology

The invention relates to the technical field of industrial chain monitoring and treatment, in particular to a monitoring method and system for industrial chain breakpoint treatment. The method comprises the steps that production, logistics, finance, policy and environment dynamic information is acquired through multi-source data, industrial chain comprehensive characteristics are generated through standardization, fractal dimension embedding expression and cross-dimension fusion, and historical trend dependency is introduced to enhance prospective prediction; a dynamic coupling network is constructed, and risk propagation intensity between nodes is quantified by using a dynamic edge weight, so that cross-level breakpoint propagation analysis is realized; risk indexes are calculated by fusing node features and a network structure, breakpoint candidate nodes are screened in combination with an adaptive threshold value, and a multi-step evolution trend is predicted by adopting a nonlinear propagation function and mapped into a multi-level early warning level. And generating a governance strategy according to the risk level, and evaluating the effect in real time and dynamically adjusting parameters through a closed-loop optimization mechanism. According to the invention, closed-loop management of risk identification, prediction and adaptive treatment is realized.

The invention relates to the technical field of electric power system risk assessment, and discloses a risk assessment system based on an electric power transaction market, which realizes real-time acquisition and dynamic processing of electric power transaction price data through a price data real-time acquisition module in combination with a real-time deviation analysis module, calculates a price deviation value in real time and captures related influence factors, thereby realizing risk assessment of the electric power transaction market. The dynamic parameter adjustment module can establish a correlation model, automatically trigger parameter adjustment when real-time factor fluctuation exceeds a threshold value, and realize automatic updating of computational logic by combining scene adaptation and emergency adjustment subunits, so as to ensure that a price deviation value accurately reflects an actual risk; the risk assessment module combines dynamic parameters to construct a model, calculates risk indexes and divides grades, the risk early warning module generates early warning information in time, and the risk conduction analysis and pressure test unit provides comprehensive risk pre-judgment, assists market subjects to scientifically make plans, and maintains the stability of the power transaction market.

The invention relates to a shipping risk assessment method, which comprises the following steps of: S1, establishing a semantic network of entities related to shipping and relationships among the entities, and determining a shipping basic information knowledge graph; s2, acquiring multi-source data related to shipping risks, and extracting risk entities and risk information from the multi-source data; s3, expanding a shipping basic information knowledge graph according to the risk entity and the risk information, constructing a risk association knowledge graph, and obtaining relationships between different risk factors and ship risks; s4, the risk factors are clustered, a risk identification model is constructed according to a clustering result to carry out quantitative evaluation on risks, a shipping risk level is obtained according to a shipping risk inference rule, if the shipping risk level is a low risk or a high risk, direct output is carried out, and otherwise, S5 is carried out; and S5, based on a quantitative evaluation result, carrying out weighted average calculation on the multi-factor risk to obtain a risk index, and determining a shipping risk level of the ship according to the risk index. Compared with the prior art, the method has the advantages of being high in precision and reliability.

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 discloses a gate opening adaptive control method and system based on resonance risk dynamic assessment, and relates to the technical field of data processing, and the method comprises the steps: obtaining a current time point, obtaining a processing time period, and obtaining a historical water flow frequency and a historical gate frequency; obtaining a deviation degree corresponding to each to-be-processed time point, obtaining a gradient correction value corresponding to the current time point based on the prediction model and the deviation degrees corresponding to the plurality of to-be-processed time points, and obtaining a trend correction value corresponding to the current time point based on the trend model and the deviation degrees corresponding to the plurality of to-be-processed time points; obtaining the current water flow frequency and the current gate frequency at the current time point, and obtaining a risk index according to the current water flow frequency and the current gate frequency; and a target opening degree is obtained according to the risk index, the gradient correction value and the trend correction value, and the gate is controlled to be opened according to the target opening degree. The method has the advantages of dynamic perception, trend prediction and adaptive control.

The invention discloses a social risk early warning method and system based on multi-source data, and the method comprises the steps: collecting multi-source heterogeneous data, and constructing a risk index library; identifying key risk indexes from the evaluation values by using a clustering algorithm; finding an alternative index for an unquantized key risk index, and establishing a quantitative model; updating the risk index library, performing hierarchical division, and calculating the weight of each risk index; constructing an index comprehensive weight matrix; quantized values of the key risk indexes are obtained through a quantitative model, and a comprehensive score is obtained in combination with the weight matrix and used for social risk early warning. According to the method, for social risk indexes which are difficult to quantify, index substitutes and a measurement and calculation model are constructed through expert experience and data analysis, and index quantification is achieved. Meanwhile, an analytic hierarchy process is adopted to construct a risk index system and give a weight, and a risk comprehensive scoring mechanism is formed. According to the method, the index system is dynamically adjusted by continuously collecting risk data and applying a time sequence analysis method and the like, so that the effectiveness of early warning is ensured.

The invention discloses an enterprise credit risk assessment method based on big data collection, and relates to the technical field of big data analysis, and the method comprises the steps: constructing a space-time fusion engine, carrying out the fusion through combining a cross-modal attention mechanism, generating a space-time fusion feature, and carrying out the adversarial training through a gradient inversion layer and a domain classifier, eliminating space-time fusion feature distribution differences; based on space-time fusion features, constructing a causal graph skeleton by adopting a conditional independent test algorithm, quantifying causal effect intensity among nodes through a machine learning model, constructing a risk conduction dynamic model, and predicting risk conduction intensity based on the causal effect intensity; and based on the risk conduction intensity, calculating a risk index weight through a dynamic game network, generating an enterprise risk score in combination with the space-time fusion feature, and generating a dynamic risk score through a time sequence neural network. According to the invention, by constructing the space-time fusion engine and combining the cross-modal attention mechanism, efficient fusion of multi-modal data is realized, and the accuracy and robustness of feature expression are improved.

The invention relates to the technical field of intelligent quantitative research platform systems, in particular to an intelligent quantitative research platform system based on a knowledge graph and a method thereof. The financial knowledge graph construction module is used for constructing a dynamic financial knowledge graph based on the multi-source data; the factor screening and optimizing module is used for extracting and optimizing quantitative factors from the financial knowledge graph and outputting an optimized factor set; the model analysis module is used for analyzing the contribution of the factor output by the factor screening and optimizing module to the income; the back-testing module is used for carrying out strategy back-testing based on the historical data and the factor set and verifying the yield and the risk index; and the result display module is used for displaying and outputting an analysis result of the back-testing module, and the dynamic weighting mechanism effectively improves the screening precision of factors, so that the model can keep higher income stability when facing different market conditions.

The invention provides a quantitative transaction strategy dynamic generation method and system based on a large model, and a storage medium. The method comprises the following steps: S1, collecting market data, related financial news and information of financial products; s2, inputting market data, financial news and information into a large language model, analyzing current market environment characteristics, and intelligently selecting a matched transaction strategy type through a strategy generator; s3, performing simulated transaction verification on the generated strategies by using historical market data, and selecting the strategy with the optimal performance from a plurality of candidate strategies through risk assessment, parameter optimization and strategy screening; s4, deploying the strategy passing the verification of the large model to a transaction execution system, monitoring the execution effect, the market environment and the risk index of the strategy, and dynamically adjusting the strategy parameters or regenerating the strategy according to the monitoring result; and S5, a feedback learning step. The method has the advantages that multiple strategy types are integrated, and diversity and innovativeness of strategies are improved.

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

The invention relates to the technical field of data analysis, can be applied to the field of medical health and financial science and technology businesses, and discloses a cross-domain abnormal reason determination method, which comprises the following steps: preprocessing heterogeneous data, and storing the preprocessed data into a unified database; extracting time sequence data from the unified database, inputting the time sequence data into a Transform encoder, and outputting a feature vector; inputting the feature vector into an interpretable elevator for training, extracting a causal contribution value, and performing standardization processing on the causal contribution value to obtain a causal influence score; sorting the causal influence scores according to a system health target, and outputting a processing sequence; and according to the processing sequence, recording and tracing the abnormal reason of the risk index through the explainable shape function and feature interaction of the elevator. According to the method, time series data in heterogeneous data are converted into feature vectors through a Transform encoder, causal contribution values are extracted through training of an interpretable elevator and standardized to obtain CIS, the negative CIS is sorted according to a system health target, a processing sequence is output, and abnormal reasons are traced according to EBM interpretability.

The invention belongs to the technical field of insurance product pricing, and more specifically relates to a multi-factor fusion insurance product dynamic risk pricing optimization method. Collecting multi-source information data, and carrying out data preprocessing; on the basis of travel trajectory analysis of vehicle-mounted OBD data, in combination with high-risk road sections, driving behaviors and environmental factors, a space-time accumulation risk index is constructed, a graph convolutional network is used for modeling a risk transfer effect in a social circle of vehicle owners, mutual influences among the vehicle owners in the social circle are captured, and a neural network based on multi-task learning is used for establishing a social network model. Dynamic risk, social risk features and other related data are comprehensively processed, the risk probability and loss amount prediction are optimized through a shared network layer, and the insurance pricing is generated in combination with external market data, so that the comprehensiveness and accuracy of the insurance pricing are ensured; the method solves a blind area existing in a conventional pricing method, enables the insurance pricing to be more intelligent and precise, and can reflect the actual risk condition of a vehicle owner in real time.

The invention discloses a source load risk management and control method for cooperative operation of multiple types of adjusting power supplies and new energy, and the method comprises the steps: depicting a prediction error tail dependency relationship through employing an extreme value theory and a Copula function, constructing a distribution robust optimization model in combination with a Wasserstein distance, and obtaining the standby capacity sharing, triggering threshold and risk index of the multiple types of adjusting power supplies; performing conformal prediction based on the prediction residual error to generate a dynamic confidence interval, and mapping the dynamic confidence interval into a pipeline radius of model prediction control; executing rolling MPC containing information bottleneck regularization under the constraint of the reserve capacity and the pipeline, and generating a robust scheduling strategy; and constructing a polyhedral feasible region model of the flexible load group according to the scheduling strategy and the risk index, and performing scaling to obtain a group control action. And the new energy consumption level and the system operation safety are obviously improved.

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 finance, and discloses a dynamic financing risk management method based on a data model, and the method comprises the steps: S10, collecting pledge related data and creditor enterprise data from multi-source heterogeneous data, the pledge related data comprising a pledge BOM table and a raw material real-time market price; s20, the multi-source heterogeneous data are converted into isomorphic data corresponding to multiple items of information data, the isomorphic data are identified, a data model cluster is constructed through a statistical analysis method, and the data model cluster comprises a quality real-time valuation identification model; s30, dynamic value evaluation is carried out on the pledge through a real-time quality valuation identification model, the dynamic value evaluation is to calculate the dynamic cost sum of raw materials, the cost sum is correspondingly adjusted according to factors influencing the value of the pledge, and an valuation confidence interval is output and is a possible range of valuation; and S40, calculating a dynamic pledge rate LTV according to a lower limit value of the estimated value confidence interval, and generating a financing risk index.

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

The invention discloses an export enterprise risk prediction method and system based on Transform, relates to the field of data processing, is applied to an enterprise risk control system, and comprises the following steps: constructing a multi-dimensional index system, and dynamically distributing index weights by adopting a subjective and objective fused multi-target combination weighting model; designing a Transform risk prediction model, capturing dynamic association of risk indexes through a space-time attention mechanism, and training the risk prediction model in combination with historical risk evaluation index data; establishing a weight comparison correction mechanism, comparing a combined weighting result with a model prediction value, and optimizing weight distribution through error feedback; and establishing a risk early warning linkage mechanism of a regional level by combining a corrected weighting result, and triggering graded early warning when an enterprise risk value exceeds a threshold value, thereby realizing accurate prediction and collaborative prevention and control of export enterprise risk indexes.

The invention 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 relates to the technical field of finance, and discloses a dynamic financing risk management method, monitoring equipment is arranged in a monitoring warehouse, and the method comprises the following steps: S10, collecting multiple items of information data of pledges and credits from a multi-source heterogeneous data source; s20, converting the multiple items of information data into isomorphic data corresponding to the multiple items of information data, identifying the isomorphic data, and constructing a data model cluster by a statistical analysis method; s30, performing value evaluation on the pledge provided by the creditor to obtain a value evaluation result; s40, acquiring basic information of the pledge and inputting the basic information into a financial institution system; s50, updating the related data of the data model cluster according to whether the creditor accesses the pledge from the supervision warehouse or not; s60, analyzing and processing the data model cluster, outputting a data value matched with a preset risk mechanism parameter, and generating a plurality of risk indexes; and S70, carrying out risk determination based on the plurality of risk indexes, and when the risk is determined to be a high risk, carrying out financing risk early warning.

The invention provides a port dangerous cargo risk assessment method and system based on a V-HAZOP model, and relates to the technical field of risk assessment, and the method comprises the steps: obtaining the feature information and additional information of port dangerous cargos, and building a risk assessment basic data set; preprocessing the risk assessment basic data set based on an HAZOP method, and establishing a risk data index library; analyzing the risk data index library based on a correlation analysis algorithm, and updating the risk data index library; obtaining final risk index data on the basis of a Bayesian network conversion method based on the fault tree analysis model; performing visual data modeling according to the final risk index data, and constructing a V-HAZOP model; displaying the V-HAZOP model by using a VIEW-Glass multi-dimensional visualization technology so as to obtain data; according to the invention, the management difficulties of various types of dangerous goods, different physicochemical properties, high operation requirements and the like in the existing port can be solved, the safety production operation of the dangerous goods storage yard is optimized comprehensively, and the professional storage yard intelligent operation management of the dangerous goods in the port is realized.

The invention provides a blue carbon market adaptive accounting method based on multi-source data and Bayesian updating, and belongs to the technical field of natural resource planning and ecological management. The method comprises the following steps: firstly, constructing a standardized multi-source fusion data set through a systematic process, and then preprocessing collected data to generate a standardized blue carbon multi-source fusion data set; then, through a Bayesian-based carbon reserve estimation model, outputting a monitoring point carbon reserve prior value, fusing a monitoring point carbon reserve mean value with a spatial prior value, and updating monitoring point carbon reserve distribution; and finally, constructing a carbon credit risk index system, calculating risk indexes of the plot layer and the region layer, updating a plot layer carbon reserve mean value by using the updated monitoring point carbon reserve distribution, and generating a blue carbon project reference transaction price according to the carbon reserve mean value and the risk indexes. According to the invention, a full-chain blue carbon intelligent accounting system is constructed, and interdisciplinary fusion of an ecological model, deep learning and a carbon market mechanism is realized.

The invention discloses a low-altitude flight risk assessment method and device, electronic equipment and a storage medium, and the method comprises the steps: determining an initial weight corresponding to a multi-level index in a multi-level risk index system, and the multi-level index comprises a comprehensive risk index, a multi-dimensional risk index and a basic risk source index; based on the current flight state information, adjusting an initial weight corresponding to the multi-level index to obtain a combined weight adaptive to the current flight environment; determining a target index from the multi-level indexes, and obtaining an original value associated with the target index; and performing risk assessment based on the original value and the combined weight to obtain a risk assessment result. According to the method, the risk index weight can be dynamically adjusted according to the current flight environment, and the real-time performance and accuracy of risk assessment are improved.

The invention relates to the technical field of intelligent decision making, can be applied to business system platforms of financial science and technology, medical health and the like, and discloses an insurance control value analysis method, device, equipment and medium for an insurance applicant, and the method comprises the following steps: obtaining multi-dimensional risk data of the insurance applicant, and carrying out implicit association feature cross extraction on the multi-dimensional risk data to obtain feature association data; determining a comprehensive risk index of the multi-dimensional risk data according to the feature association data, and performing correlation analysis on the comprehensive risk index and the multi-dimensional risk data to obtain an analysis result; performing data extraction on the multi-dimensional risk data according to an analysis result to obtain key risk data; and constructing a condition set according to the key risk data, and calculating an initial control value of the multi-dimensional risk data according to the condition set and the key risk data. Through hidden correlation feature cross extraction, a richer basis is provided for risk assessment and decision making, and meanwhile, a condition set is adjusted and optimized, so that a risk management system keeps dynamic adaptability.

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 traffic public opinion risk tag classification early warning method based on a large model. At least one tag corresponding to each of multiple pieces of first public opinion data in a preset time window is determined based on the large model; determining a plurality of pieces of second public opinion data in the preset area, and for at least one tag, determining the number of the public opinion data corresponding to the tag in the plurality of pieces of second public opinion data, the emotional property corresponding to the plurality of pieces of second public opinion data and the number of collection channels corresponding to the plurality of pieces of second public opinion data, according to a preset distance value, the number of the public opinion data corresponding to the tag in the plurality of pieces of second public opinion data; determining a popularity index corresponding to the tag; determining a plurality of label combinations, screening out high-popularity label combinations, and determining risk indexes of the high-popularity label combinations; and if the risk index is greater than a preset risk threshold, performing early warning on the high-popularity label combination. The accuracy of traffic public opinion risk label classification early warning is improved.

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

The invention discloses a 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 belongs to the technical field of power system optimization, and particularly relates to a power system optimization planning method and device considering supply and demand probability balance, and the method comprises the steps: defining edge distribution for a random variable of each geographic position; a hierarchical Copula model is constructed; estimating parameters in the joint Copula model by using a maximum likelihood estimation method; a sample is extracted from the joint Copula function through a Monte Carlo method, and a joint scene is generated; based on the power loss risk and the resource waste risk, setting a comprehensive risk index under the joint scene; establishing a power system planning model, and setting an objective function and constraint conditions; and solving the power system planning model by using a Benders decomposition method to obtain a power system optimization planning scheme. According to the method, the space-time uncertainty of the new energy power system can be accurately quantified, the planning model considering the investment cost and the operation risk is constructed, and the reliability and the economical efficiency of the system are remarkably improved.

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.