2314 results about "Risk Control" patented technology

The invention provides a knowledge graph generation method and system for science and technology project risk control, and the method comprises the steps: obtaining a multi-source heterogeneous data set of a target science and technology project, converting structured index data into a standard vector sequence through a heterogeneous data fusion mechanism, and converting unstructured text data into a semantic vector sequence; converting the time sequence behavior data into a behavior pattern vector sequence, inputting the three into a risk quantitative evaluation model, generating a risk entity feature matrix and a risk association strength matrix, and determining a node distribution topology of the knowledge graph according to entity feature vectors in the risk entity feature matrix; and according to association strength values in the risk association strength matrix, determining an entity relationship topology of the knowledge graph, generating a dynamic knowledge graph of the target science and technology project, and identifying a potential risk propagation path in the dynamic knowledge graph. According to the invention, the risk identification result has the dynamic characteristic of real-time updating, and the traceability of the multi-dimensional risk characteristic is maintained.

The invention provides a risk control big data mining method and system based on the Internet of Vehicles, and the method comprises the steps: collecting a real-time operation data flow of a target vehicle in an Internet of Vehicles environment, including vehicle state time sequence data, a driving behavior event set, external environment perception data and a vehicle-mounted communication interaction record; and then respectively extracting a combined operation feature vector, an abnormal behavior mode identifier and a dynamic environment risk factor from different data, converting to generate a structured communication feature matrix, constructing a space-time association risk analysis map based on the structured communication feature matrix, and carrying out driving risk dynamic assessment on a target vehicle through the space-time association risk analysis map. And finally, generating a risk control decision instruction set according to the result, and feeding back the risk control decision instruction set to the vehicle-mounted control terminal and the Internet of Vehicles cloud platform, thereby realizing comprehensive assessment and effective management and control of vehicle risks.

The invention provides a method and system for realizing electronic channel risk control processing based on a code insertion technology, and relates to the technical field of electronic channel risk control, and the method comprises the steps: encrypting user information and channel identification information through a dual Hash algorithm to generate a dynamic session identification code, and forming a target coding sequence through employing a self-adaptive segmentation algorithm and a business feature flag bit. And constructing a dynamic risk control rule model by using a graph neural network, extracting user multi-dimensional features, carrying out weighted fusion to generate a user risk score, comparing the user risk score with an adaptive risk threshold, analyzing abnormal behaviors based on a probability graph model, and generating an intelligent risk early warning report. And calling a reinforcement learning risk control strategy engine to determine an optimal disposal strategy, and issuing a multi-factor interaction verification instruction. And after verification, selectively activating a blocking mechanism according to a verification result, synchronizing interaction data and a result to a distributed risk control data center, and continuously optimizing the risk control model based on federal learning.

The invention discloses a risk control credit monitoring method based on cloud computing, and belongs to the technical field of cloud computing, and the risk control credit monitoring method based on cloud computing comprises the following steps: S1, collecting user transaction data and behavior track data in real time; s2, cleaning and standardizing the data; s3, constructing a multi-dimensional risk assessment model based on machine learning; s4, dynamically generating a credit score according to the risk characteristics; s5, triggering an early warning mechanism for abnormal transactions in real time; and S6, generating a visual risk control report and updating a monitoring strategy. According to the method, multi-source heterogeneous data are integrated through federated learning, hierarchical privacy protection is realized in combination with homomorphic encryption and differential privacy, risk assessment real-time performance is improved by using a hybrid cloud resource scheduling and dynamic model updating technology, and a compliance audit closed loop is constructed based on a block chain and interpretability analysis.

The invention relates to the technical field of vehicle insurance marketing management, in particular to a vehicle insurance channel whole-process marketing management method and system, and the method comprises the steps: collecting and integrating various data of clients from multiple data sources to construct a resource library; carrying out customer segmentation and accurate portraying by utilizing algorithms such as clustering analysis and the like; predicting demands based on the portraits, historical behaviors and seasonal factors; personalized pricing is made in combination with characteristics such as risk levels; predicting customer loss by means of logistic regression and the like; implementing a corresponding customer management strategy according to the loss prediction; carrying out personalized recommendation by adopting a deep learning recommendation algorithm; advertisement putting is optimized in combination with a dobby machine and reinforcement learning; evaluating the risk by using multiple algorithms; fraud is prevented and controlled through anomaly detection; optimizing a claim settlement process based on the claim settlement data; and the marketing and advertising effects are evaluated through A / B testing and the like. According to the method, the precision marketing level can be improved, pricing and risk control are optimized, efficient operation and decision support are realized, and the market competitiveness and sustainable development capability of companies are enhanced.

The invention discloses a government affair data sharing system based on a data security law risk control mode, and relates to the technical field of data management. According to the method, full-process closed-loop management from data classification and grading, differential desensitization, law and regulation compliance review, access authority precise control to behavior trajectory real-time analysis and risk trend prediction is realized, and the risk trend prediction is realized by introducing key technologies such as a context sensitive desensitization algorithm, legal knowledge graph reasoning, behavior portraits, semantic trajectory similarity scoring and risk rise rate monitoring. The method effectively improves the recognition and response capability of the hidden data re-recognition risk, and achieves the safe, controllable, compliant and ordered intelligent risk control management of government affair data in the sharing process.

The invention discloses a DDGS three-dimensional storage device based on PLC control and a management method of the DDGS three-dimensional storage device, relates to the technical field of intelligent storage management, and solves the problems that in traditional DDGS feed storage, the storage space utilization rate is low, the storage turning cost is high, risk control is not timely, and a quick response strategy is lacked. According to the method, the dynamic perception metadata warehouse is constructed, and DDGS material characteristic key data is monitored in real time to support anomaly detection; a storage control topology model is generated by using a graph theory modeling and dynamic attribute injection method to map an exception type; periodically collecting equipment data and fusing and updating a storage state; state diagram nodes are enhanced, risk response priorities are calculated, and grading operation is triggered; pLC control logic is dynamically adjusted based on historical data, and real-time monitoring and multi-device linkage control are achieved; the intelligent level of DDGS three-dimensional warehouse management is remarkably improved, the warehouse space utilization rate is increased, and the risk control capability is enhanced.

The invention discloses a financial data risk control system and method based on big data, and relates to the technical field of financial science and technology, and the system comprises a multi-source data collection module which achieves cross-mechanism safety collection through federal learning; the data cleaning and preprocessing module is used for processing abnormal values and missing values by using an improved algorithm; the knowledge graph construction module is used for constructing a dynamic knowledge network based on an innovative algorithm; the risk assessment engine fuses various models to assess risks; the real-time monitoring and early warning module is used for realizing second-level response by utilizing multi-scale analysis; the decision support module is used for optimizing a strategy based on reinforcement learning; and the audit tracking module guarantees evidence storage and privacy through zero-knowledge proof, and all the modules cooperate to improve the risk control capability. According to the financial data risk control system and method, risks are accurately recognized, real-time monitoring and early warning are achieved, data security sharing is achieved, risk control strategies are dynamically optimized, risks and business development are balanced, the risk prevention and control capacity and economic benefits of financial institutions are improved, and data privacy and risk control transparency are guaranteed.

The invention relates to a dynamic risk control method and device, equipment and a storage medium. The method comprises the following steps: collecting and preprocessing multi-source data to obtain a standardized multi-dimensional data matrix; constructing a dynamic risk control model according to the standardized multi-dimensional data matrix, and calculating a risk control model parameter set; performing risk factor sensitivity analysis by using the risk control model parameter set and the standardized multi-dimensional data matrix to obtain a dynamic weight vector; executing iterative risk assessment based on the dynamic weight vector to obtain a comprehensive risk score; constructing a multi-layer threshold structure based on the comprehensive risk score and executing dynamic adjustment to obtain a personalized risk threshold; and according to the comprehensive risk score and the personalized risk threshold, triggering a risk control measure and executing a feedback mechanism to obtain a risk control execution result. The importance of different risk factors can be adjusted in real time, the market environment change is quickly responded, and the exploration capability of the system for unknown risks is enhanced.

The invention discloses an intelligent risk control system and method for cross-border e-commerce digitization. Comprising an identity verification module, a transaction monitoring and abnormal behavior detection module, a credit assessment and risk quantification module, an anti-fraud and anti-money laundering check module, a compliance auditing module, a user behavior analysis module, a risk control rule engine module and a comprehensive risk assessment module. The problems of how to make a risk control system capable of meeting individual requirements and having cost benefits for small and micro enterprises and how to effectively evaluate and improve the ability of salesmen in the aspect of risk control are solved, and the overall risk control efficiency and accuracy are further improved. Therefore, personalized and intelligent setting of risk control is realized, a visual portrait of the ability of a salesman is provided, intelligent assessment and standard improvement of the ability of risk control personnel are promoted, risk control optimization is realized on the whole, the risk management level of cross-border e-commerce is improved, and enterprises can develop steadily in a complex and changeable international trade environment.

The invention relates to the technical field of project management and artificial intelligence, in particular to a project progress tracking and risk prediction system and method based on an improved knowledge graph and a multi-view graph neural network, and the system comprises a knowledge graph construction and dynamic updating module, an improved graph neural network analysis module, and an anomaly detection and risk prediction module. An intelligent early warning and visualization module; the method has the beneficial effects that anomaly detection is carried out through the GAN, possible delay and risk of a project are predicted, a risk early warning report is automatically generated, and optimization suggestions are provided for project managers. The system has the advantages of dynamically updating the knowledge graph in real time, improving risk prediction accuracy and enhancing project management perspectiveness and intelligent decision support, and is suitable for progress tracking and risk control in complex project management.

The invention relates to the technical field of data processing, and provides an intelligent risk control method for carbon asset transaction, which is used for solving the problems of data island, response lag, low efficiency, safety risk and the like in traditional carbon transaction. According to the invention, standardized access of a carbon emission permit trading system, a registration system and enterprise internal data is realized by constructing a carbon asset data middle platform, integrating a block chain and an RPA technology, a price fluctuation rate and a quota gap risk are calculated in real time based on Apache Flink, a carbon price trend is predicted through an LSTM + Attention model, and an intelligent contract is triggered to generate a hedging instruction; a three-level risk prevention and control system is constructed by adopting an ABAC model and biological characteristic verification, a quota regulation path is optimized through a graph neural network, automatic performance is driven, strategy self-adaption is realized in combination with PPO reinforcement learning and BERT policy analysis, and the problems of data islands, response lag, low efficiency and security risks in traditional carbon transactions are systematically solved.

The invention discloses a power grid power flow risk control method based on topology analysis, and relates to the technical field of power flow risk control, and the method comprises the following steps: obtaining first data of a power grid in real time, and constructing a node admittance matrix; when a switch displacement signal or distributed power supply power mutation is detected, topology reconstruction is carried out based on a node admittance matrix, and a dynamic topological graph is generated; establishing a fault current characteristic matrix based on the dynamic topological graph in combination with the bidirectional power flow characteristics of the active power grid; establishing an adaptive risk early warning model based on the dynamic topological graph and the fault current characteristic matrix, and outputting the overload probability and the voltage out-of-limit risk index of each branch; and performing analysis according to the branch overload probability and the voltage out-of-limit risk index, and generating a multi-target optimization control instruction set. Through adaptive topology adjustment, load flow calculation and a risk early warning model, automatic power grid optimization control can be realized, and in combination with convergence verification of a virtual impedance observer, effectiveness of topology adjustment and control operation is ensured.

The invention provides a financial data risk analysis method and system based on a large model, and relates to the technical field of intelligent risk control, and the method comprises the steps: obtaining financial data from a participant, carrying out the standardization and aggregation through a federated learning framework, generating a federated feature vector, constructing a bidirectional knowledge distillation model, receiving a vector, and carrying out the risk analysis of the financial data. A teacher model and a student model are deployed, a risk conduction map is constructed, a generative adversarial network is utilized to monitor changes of an enterprise association network, a risk conduction edge weight is adjusted, a financial report time sequence and a transaction event sequence are aligned through a space-time coupling encoder based on an LSTM and Transform architecture, and a multi-modal fusion feature vector is generated and input to a bidirectional knowledge distillation model; and a final risk score is obtained by combining the atlas, a grading early warning mechanism is triggered, and a risk disposal suggestion and a conduction path are generated, so that the data standardization and aggregation efficiency can be improved, and the dynamic coupling capability of a multi-modal fusion mechanism and the adaptability of a risk conduction model can be enhanced.

The invention discloses a contract compliance examination method and platform based on multi-agent cooperation, relates to the field of artificial intelligence, and is used for improving the safety and accuracy of contract examination. The method comprises the following steps: acquiring latest rule data by a data collection agent; the data construction agent constructs retrieval data and a rule knowledge graph based on the rule data; the document analysis agent extracts key information of the target contract and retrieves related rules from the retrieval data according to the key information; the compliance review agent performs risk item assessment on the target contract according to the related rules and the rule knowledge graph; the risk management and control agent generates a response measure of the risk item based on the retrieval data; the report generation agent generates a compliance review report based at least on the evaluated risk items and corresponding countermeasures. According to the invention, the security, accuracy and efficiency of contract review are improved.

The invention provides a risk control early warning method and system based on bank flow analysis, and relates to the technical field of computers, bank flow data of a target user in a bank system is collected, the collected bank flow data is fused with multi-source cross-domain data, and time sequence data of transaction behaviors is generated according to a time sequence; performing data feature extraction on the time sequence data to obtain transaction features; constructing a framework of a risk prediction model, and obtaining the risk prediction model through training and verification; real-time flow data of a target user in a bank system is monitored in real time, a trained risk prediction model is utilized to evaluate transaction behaviors of clients in real time, an anomaly detection result of an anomaly detection model is combined with output of the risk prediction model, a comprehensive risk score is formed to identify potential risks, and an early warning mechanism is triggered. Through deep analysis of bank flow data, real-time risk monitoring and early warning are realized, and potential financial risks can be effectively identified.

The present invention discloses a digital management method and system for a water conservancy project construction process, and belongs to the technical field of water conservancy projects. The method specifically comprises: real-time collection of relevant data from a water conservancy project construction site, and preprocessing, by comparing the actual construction progress with the planned progress, calculating the construction progress deviation, and analyzing the future construction progress trend, analyzing the construction quality data, training a prediction model through historical data, identifying construction quality hazards, combining the construction progress and quality, resource conditions and external environmental factors, intelligently scheduling construction resources, building a risk prediction model, and evaluating the risks in the construction process; in the context of complex water conservancy projects, it is possible to achieve process progress and quality management, reasonable resource scheduling and risk control, and better ensure the smooth progress of the project.

The invention relates to a steel logistics full-process real-time dynamic management method and device, and belongs to the technical field of steel logistics control methods and devices. According to the technical scheme of the invention, data acquisition and monitoring are carried out, and transport vehicle and cargo states are tracked; demand prediction and inventory dynamic adjustment are carried out, and high-frequency cargo response emergency orders are allocated according to shipment frequency; visual management of the supply chain is carried out, shared data of all links of the supply chain is established, a 3D logistics map is constructed, and inventory, transportation states and bottleneck nodes are displayed in real time; and risk control management: quickly starting the alternative scheme when the early warning is triggered. The method has the advantages that efficiency is optimized, limitation of a traditional single transportation mode is reduced, rolling updating and emergency response of demand prediction are achieved, data transparency and credibility are improved, rapid risk response is achieved, and logistics full-life-cycle management is achieved.

The invention provides an investment decision-making system and method based on federated learning and multi-modal risk control, and the system comprises a data collection layer which is used for integrating satellite remote sensing data, supply chain Internet of Things data and financial market data; the federal processing layer is used for constructing a distributed training framework based on a FedAvg algorithm and realizing cross-mechanism data fusion and global model parameter updating through homomorphic encryption and differential privacy; the intelligent decision-making layer adopts a TD3 reinforcement learning engine and adopts a dynamic gating mechanism to adjust fusion weights of texts, time sequences and image modalities in real time; and the execution monitoring layer is used for detecting transaction execution deviation in real time through a digital twinning technology, realizing abnormal response by adopting a CUSUM algorithm, and dynamically adjusting a federal learning rate through Bayesian optimization. The key technical defects of data islands, tail risk response delay, insufficient cross-market linkage analysis and the like of a traditional investment system are overcome through an innovatively designed federated learning architecture and a time-varying risk control model.

The invention discloses a failure chain quantitative analysis and risk assessment method and system based on a multi-level security model, and aims to solve the defects that accident cause analysis of a complex social technology system is inaccurate, and a risk assessment result is lack of effective verification. According to the method, a multi-level causal model is systematically constructed, a multi-dimensional failure chain (MDFC) is extracted, multi-dimensional risk quantification is performed on the MDFC, a directed weighted failure propagation network is constructed based on the multi-dimensional risk quantification, and structural features of the directed weighted failure propagation network are analyzed to identify key risk factors. The core innovation of the method is that reverse accident reason tracing and forward risk propagation path analysis based on the weighted network are fused, mutual verification and iterative optimization are realized by comparing analysis results of the two paths, so that the understanding of an accident evolution mechanism is deepened, and the reliability of evaluation is improved. The system vulnerability can be revealed more comprehensively, powerful support is provided for formulating accurate risk control measures, and the overall safety level of a complex system is improved.

The invention discloses a digital twinning-based major engineering construction safety active control method, system and equipment, and belongs to the technical field of construction active control. The problems of low safety risk identification efficiency and inaccurate risk control means in the major engineering construction process are solved, major engineering construction site environment and structural feature data are obtained through space-air-ground-water multi-way intelligent means and equipment, a digital twinborn model of a real-time mapping construction site is established, and the safety risk identification efficiency of the major engineering construction site is improved. Correcting the digital twinborn model by using real-time data; a digital twinborn model is used as a basic model of finite element analysis, mechanical properties of different construction stages are analyzed, dynamic early warning is performed on an analysis result in combination with real-time data, a safety risk derivation model is established to perform real-time analysis on key problems such as a risk point location, a risk influence range and risk derivation prediction in the digital twinborn model, and the safety risk derivation prediction result is obtained. Data support is provided for engineering safety active control, an active control strategy model is established, a control strategy is output to a servo control device according to risk information, strategy dynamic adjustment is performed according to a real-time effect, and high-precision cooperative control of major engineering safety monitoring is realized.

The invention relates to a real-time monitoring method and related equipment for an intelligent manufacturing digital twinning system, and the method comprises the following steps: constructing a three-dimensional digital twinning model based on a real-time image sequence; based on the three-dimensional digital twinborn model, analyzing whether an abnormal phenomenon exists in the production field, and if yes, performing abnormal region identification in the three-dimensional digital twinborn model through a preset abnormal identification algorithm to obtain abnormal state data; performing abnormity deduction on the production field based on the abnormal state data to obtain an abnormal evolution trend; and performing risk assessment on the production field based on the abnormal evolution trend to obtain a risk assessment result, and selecting a corresponding risk control strategy based on the risk assessment result, thereby solving the problem that a traditional method is difficult to provide enough details to comprehensively know reasons behind an abnormal phenomenon, and improving the risk control accuracy. And subsequent risk assessment and selection of control strategies are influenced.

The invention discloses an automatic contract text auditing method and system based on clause unified modeling, and belongs to the technical field of contract auditing, and the auditing method comprises the following specific steps: I, accessing a third-party credit investigation mechanism or a credit rating platform to extract the credit information of both parties of a contract, and carrying out the credit investigation and auditing of the both parties of the contract; iI, establishing an initial contract template classified according to economic businesses, and extracting contract key information through an NLP technology; the optimal rule can be quickly identified and selected in a large number of detection rules, the auditing efficiency is remarkably improved, the time required by manual intervention is shortened, the auditing process is more flexible, the method can adapt to specific requirements of different contracts, potential risks in the contracts can be comprehensively identified, auditing personnel can be helped to find high-risk terms in time, and the contract auditing efficiency is improved. The law risk in contract fulfillment is reduced, the benefits of colleges and universities are protected, more stable risk control and compliance guarantee are provided for the colleges and universities, the contract fulfillment condition is monitored through integration with an accounting system, the compliance and the executive force of the contract are ensured, and the transparency and the traceability of contract management are improved.

The invention discloses a full-life-cycle underground engineering geological safety risk collaborative management and control method and system. The method comprises the following steps: performing regional geological structure identification and geological risk identification on a target construction region, and constructing a regional geological digital model; regional construction monitoring information is obtained, and construction risk analysis and cavity prediction are performed in combination with a regional geological digital model, so that regional construction risk analysis information is obtained; constructing a carrier risk assessment model, and analyzing whether the current construction risk affects a building object on the ground or not through the regional construction risk analysis information and the regional construction monitoring information to obtain carrier risk assessment information; and based on the regional construction risk analysis information and the carrier risk assessment information, judging whether construction risk management and control need to be carried out, and if so, generating a risk management and control scheme to carry out regional construction safety risk collaborative management and control. And timeliness, comprehensiveness and decision reliability requirements of underground engineering construction management and control in a complex geological environment are improved.

According to the technical scheme, the method comprises the steps that input risk control detection is conducted on input questions of a user, if risks exist, a default risk control answer template is inquired, and all characters of the risk control answer template are sequentially returned in a streaming mode; if the risk does not exist, the problem is transmitted to a large model to generate streaming output; generating a to-be-detected text based on streaming output, performing content security detection on the to-be-detected text, determining a content security detection result, and if the detection result is no risk, continuing to generate and output a large model; and if the detection result is that the risk exists, stopping the generation of the large model, replacing the currently buffered content with the preset prompt information, and then outputting the preset prompt information. According to the scheme of the invention, through risk control detection of input and output stages in a large model dialogue, the detection efficiency is improved, and the problems of long user waiting time and poor experience in full-buffer detection during large model streaming output, frequent detection interface calling and low generation speed in streaming output real-time detection are solved.

The invention belongs to the field of multi-modal artificial intelligence risk identification. The core comprises a multi-source heterogeneous data parallel acquisition module; a modal exclusive feature extraction module; a graph attention driven dynamic fusion module; and a risk classification module with an attention mechanism. A real-time fusion weight is generated through a cross-modal incidence matrix, adaptive feature weighting and hidden risk association mining are realized, and the recognition accuracy and interpretability of a complex scene are remarkably improved. The method is applied to the fields of financial risk control and industrial monitoring.

The invention belongs to the technical field of decision optimization, and relates to an enterprise-level intelligent risk control decision system combined with a real-time data stream, and the system comprises a heterogeneous data distribution module which is used for obtaining multi-source heterogeneous data streams inside and outside an enterprise; the behavior time sequence splicing module is used for executing cross-system user ID association and time sequence recombination on the real-time data flow to generate a user behavior chain with continuous time stamps; the feature fusing calculation module is used for receiving the user behavior chain, performing feature extraction and outputting a real-time feature vector with a quality flag bit; the incremental model updating module is used for respectively generating a baseline risk score and a dynamic risk score; and the dynamic weight decision module is used for generating final decision parameters. And the risk control processing execution module responds to the final decision parameter to trigger a processing action, and configures a manual auditing arbitration channel and a feedback data generation unit. According to the method, the problems that a dual-check algorithm is not deeply coupled with a business index, and abnormal data which passes hash check but has logic violation flows into a real-time channel are solved.

The invention discloses a financing guarantee business digital intelligence risk control method and system, and belongs to the field of financial science and technology, and the method comprises the steps: obtaining multi-source heterogeneous data of government affairs, supply chains, Internet and the like; calculating a data type comprehensive score D and judging a data type through a dynamic weight model based on a unified data parameter system comprising a source identifier, a structure specification, a time sequence, entity association and content characteristics; for different types of data, technologies such as block chain evidence storage, federated learning, a graph neural network and natural language processing are adopted to carry out differentiated risk assessment, guarantee limit output and risk early warning and other decisions. The system comprises a four-layer architecture of data acquisition, processing, modeling and decision support, and supports real-time access of multi-source data and automatic risk control. The method effectively deals with emerging scenes such as false trade and public opinion risks, meets the requirements of data security compliance, and provides an efficient and accurate digital intelligent risk control scheme for financing guarantee.

The invention discloses a digital product online leasing system based on a cloud native micro-service architecture and a construction method thereof, and the method specifically comprises the steps: firstly, carrying out containerized deployment and micro-service hierarchical design, including a plurality of business modules of user service, product management, leasing order, payment settlement, credit evaluation and recommendation, and enabling each module to operate in an independent service form; efficient cooperation is realized through service registration and discovery; a cloud native platform is utilized to realize elastic expansion and automatic operation and maintenance of the system, lease life cycle management is realized through an event-driven mechanism, links of ordering, payment, delivery, lease renewal, return and maintenance are covered, and a credit determination and risk control mechanism is introduced to support multiple lease modes such as detention-free lease and the like. The digital product leasing platform supports multi-terminal access and unified entrance management, has good system expansibility and leasing service flexibility, improves the concurrent processing capability, deployment efficiency and user experience of the digital product leasing platform, and has wide commercial application prospects.

The invention relates to the technical field of internet risk control management, and discloses an internet risk control optimization method based on multi-dimensional data analysis, which comprises the following steps: acquiring internet user behavior data, transaction data, equipment information and environment data in real time, and carrying out data preprocessing on the data to generate a sample data set; key risk control features are extracted from the sample data set based on a feature importance analysis strategy, and a feature subset is generated; performing parameter optimization on the integrated gradient boosting tree and the deep learning network by using an improved sparrow search algorithm, and obtaining a risk control model after optimization; and inputting the feature subset into a risk control model for analysis, outputting a risk score, and generating a risk control decision in combination with a dynamic threshold adjustment strategy. According to the method, the model iteration period is shortened, the training convergence speed is improved, the overall prediction performance and generalization ability of the risk control model are improved, and more reliable technical guarantee is provided for risk identification.