252 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 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 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 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 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 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 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 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.

The invention relates to a power transmission and transformation project cost risk whole-process dynamic early warning method and system. The method comprises the following steps: obtaining cost data and reference data of each stage of a power transmission and transformation project, and constructing a standardized cost database; based on a preset risk rule, constructing a risk index system comprising a static risk index, a dynamic risk index and a stage association index; performing real-time comparison and trend prediction on the cost data and reference data by using a preset deep learning model, and accurately identifying cost risk points; determining a risk level according to the deviation degree of the risk point, generating graded early warning information, and pushing the graded early warning information to a corresponding terminal; and the processing result is recorded and the database is updated, so that the whole-process dynamic monitoring and risk early warning are realized, the real-time performance, the accuracy and the decision-making efficiency of the cost risk management are effectively improved, and the problems of early warning lag and incomplete risk coverage in the traditional method are solved.

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 relates to the field of data processing, in particular to an oil temperature monitoring method for an equipment lubrication system, which comprises the following steps: acquiring preprocessed oil temperature data of the lubrication system and operation condition data in the operation process of a press machine in real time; calculating a risk index of the oil temperature data per second according to the operation condition data; taking the current time point as a reference, tracing a preset time window, calculating the mean value and the standard deviation of the risk indexes in the time window, dividing the absolute deviation between the risk index corresponding to the current time point and the mean value of the risk indexes in the time window by the standard deviation of the risk indexes in the time window, and normalizing to obtain the risk index corresponding to the current time point. The change rate of the risk index of the oil temperature data at the current time point is obtained; and abnormal monitoring is conducted on the oil temperature according to the change rate, and an oil temperature monitoring report is generated. According to the invention, through multi-dimensional dynamic analysis and risk quantification, the accuracy of oil temperature abnormity monitoring is effectively improved.

The invention provides an icing risk grade prediction method and device in combination with line body parameters, and relates to the technical field of computer data processing, and the method comprises the steps: collecting meteorological factors, geographic data and line body parameters; determining icing thickness data through an icing thickness model according to the meteorological factors and the geographic data; taking the meteorological factors and the geographic data as input samples, taking the icing thickness data as an output label, and obtaining an icing thickness model through model training; determining an icing risk grade through an icing risk grade prediction model according to the icing thickness data, the meteorological factors and the line body parameters, and providing a processing strategy corresponding to the icing risk grade; setting a line icing risk index domain according to the risk factor, and dividing an icing risk level according to the line icing risk index domain; and taking the risk factor as an input sample, taking the risk level as an output label, and obtaining an icing risk level prediction model through model training.

The invention discloses an energy consumption risk index system construction method, system and device based on knowledge graph modeling and a medium, and belongs to the technical field of energy system risk management, and the method comprises the steps: constructing a dynamic knowledge graph ontology based on a risk propagation operator; based on the dynamic knowledge graph, calculating equivalent impedance of a risk conduction path and identifying a key path, performing risk potential energy field dynamic simulation, and generating a global risk index; receiving a user instruction to perform situational pruning on the global atlas, and generating a focus sub-atlas; receiving an intervention instruction on the focus subgraph, simulating an intervention measure and deducing a new index after the change of the risk field; and based on the risk index and the key path data, generating an interpretable risk cause knowledge card and performing visual presentation. According to the method, the full-chain capability from risk conduction quantification, global situation simulation, man-machine collaborative pruning and intervention deduction to interpretable analysis is realized, and the refinement, intelligence and credibility level of energy system risk management is improved.

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 designs a decision result interpretation system and method based on a financial risk control user portrait, and the method comprises the steps: collecting user data, and constructing a user portrait; inputting the preprocessed user portrait data and other risk indexes into a decision model, outputting a risk assessment result, and storing the risk assessment result; in combination with key information in the user portrait data, mapping a relationship between a risk assessment result and an input feature, and using an explanatory algorithm to explain the risk decision model; the decision interpretation result is displayed through a chart, a flow chart and a key index mode; and according to the user feedback and the interpretation effect, optimizing the interpretation algorithm and the risk decision model to form a closed-loop feedback mechanism. According to the method, the key information is extracted by using the user portrait, the key information and the important variables in the decision model are mapped to form a decision result explanation basis, and an explanatory algorithm is introduced to explain the black box risk decision model, so that the transparency of the decision result is realized.

The invention relates to the technical field of intelligent payment, in particular to an intelligent payment mode recommendation method based on transaction context, and the method comprises the following steps: a payment gateway obtains information from a current payment request, distributes a corresponding reference risk score for each piece of information according to a preset reference score table, and generates a quantitative risk factor vector; in a payment request initiating stage, a quantitative risk factor vector is generated by extracting geographic position anomaly, time anomaly, amount anomaly, merchant risk level, equipment credibility and network environment state directly associated with a transaction environment in real time, and numerical mapping is carried out on each state in combination with a reference score, so that a payment result is obtained. And high-dimensional quantification of the transaction background is realized. In the construction process of the risk index, the risk potential energy of each risk factor is bound with the historical transaction state, the transaction risk index is formed in a dynamic accumulation mode, and the continuity and sensitivity of risk description are enhanced.

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.

The invention provides an intelligent management and control system for enterprise finance and taxation risks. Invoice data, enterprise finance data and industry tax data of a target enterprise are acquired through a data acquisition module; through a data fusion module, performing distributed cleaning on abnormal data in the invoice data, the enterprise financial data and the industry tax data based on the established data mapping rule base, and performing real-time fusion to obtain a dynamic data pool; using a risk diagnosis module to extract compliance rules from enterprise finance and taxation legal provisions as conditions, using the trained random forest regression model to predict the dynamic data pool to obtain a reasonable tax threshold value, and determining a risk diagnosis result according to the reasonable tax threshold value; the risk diagnosis result is analyzed through the early warning module, the weight of each risk index is obtained and used for early warning the enterprise finance and taxation risk and generating the risk report of the target enterprise, the island problem of enterprise finance and taxation data is solved, and the finance and taxation management efficiency is improved.

The invention provides a commercial risk assessment management method and device and electronic equipment, and belongs to the technical field of financial science and technology. The method comprises the following steps: collecting data sources from different channels, wherein the data sources comprise historical transaction records, market information, financial statements and social media feedback; defining a preset feature index according to the business demand, and constructing a risk assessment model by applying a machine learning algorithm; performing risk assessment on the data source by using the risk assessment model, calculating the numerical value of each risk index and generating a comprehensive risk score; and setting a risk level threshold, and triggering an early warning mechanism when the comprehensive risk score exceeds the risk level threshold. Through fusion of multi-source heterogeneous data and a machine learning algorithm, full-process intelligent evaluation and dynamic early warning of commercial risks are realized, and accuracy, response speed and decision support capability of risk identification are improved.