672 results about "Risk prevention" patented technology

The invention provides a supply chain multi-dimensional fluctuation prediction and inventory optimization method based on artificial intelligence, and relates to the technical field of supply chain management, and the method comprises the steps: obtaining multi-dimensional data, carrying out the preprocessing of the multi-dimensional data, generating a standardized time series data set and a knowledge graph, carrying out the multi-scale decomposition through wavelet transformation, and extracting fluctuation features; and constructing a state matrix by combining a graph attention network and a multi-head self-attention mechanism, predicting a risk conduction path based on historical fluctuation pattern matching and a graph convolutional network, and generating a risk prevention and control scheme. According to the invention, the fluctuation characteristics and the risk conduction path in the supply chain system can be accurately identified, and the early warning accuracy and the response timeliness are improved.

The invention provides a livestock breeding risk intelligent assessment method and system based on multi-source data fusion, and the method comprises the steps: collecting livestock individual vital sign data, breeding environment parameters, management behavior data and risk-related historical data through Internet of Things equipment, and carrying out the data preprocessing to form a standardized multi-source data set; extracting risk features of individual, group and environment levels based on the data set, and fusing the risk features to form a multi-dimensional risk feature library; utilizing machine learning to construct a differentiated risk assessment model; analyzing the incidence relation between the risk factor and the actual event through the Bayesian network to calibrate the model; realizing livestock risk grade dynamic division and early warning based on the calibrated risk scoring system; and finally, generating intervention suggestions for risk quantitative evaluation, risk prevention and control decision and loss evaluation. According to the method, accurate evaluation of livestock breeding risks is realized, decision support is provided for breeding safety management, and the method has relatively high application value.

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 provides an enterprise data risk processing method and system based on a dynamic knowledge graph, and the method comprises the steps: collecting a real-time data flow generated in the operation process of an enterprise, extracting an entity object and an incidence relation in the real-time data flow, and forming an initial node and an initial edge; and receiving a risk processing request, positioning an initial node corresponding to the target entity from the dynamic knowledge graph according to the risk attribute identifier, and determining an association propagation path generation rule of the target entity based on the risk influence range description. And calling a pre-trained risk propagation model, traversing adjacent nodes in the dynamic knowledge graph according to a correlation propagation path generation rule by taking the initial node as a starting point, and generating a risk propagation path set corresponding to the target entity. And calculating a comprehensive risk value corresponding to the target entity according to the node connection density and the risk attribute superposition intensity of each risk propagation path, and triggering a risk early warning signal when the comprehensive risk value exceeds a preset threshold. The enterprise risk prevention and control initiative and decision-making accuracy can be improved.

The invention relates to the technical field of data processing, and discloses an AI knowledge base construction method and system for intelligent water conservancy. The method comprises the following steps: forming a water conservancy intelligent perception data set by integrating hydrological monitoring data, engineering operation records and expert literatures; knowledge sprouts are identified, and a water conservancy native knowledge unit is generated; key features are extracted, knowledge element decomposition is carried out, and a knowledge evolution chain network is constructed through correlation analysis; screening in combination with a historical application effect and expert evaluation to obtain a preferred knowledge body; performing intelligent reconstruction on a scheduling rule, risk prevention experience and an operation and maintenance method to form an innovative knowledge base; and finally, intelligently matching with real-time engineering requirements to generate an intelligent water conservancy AI knowledge base. According to the application, by establishing a self-adaptive knowledge acquisition, organization and application mechanism, the knowledge base can autonomously update the knowledge content according to the environment change, the knowledge structure is optimized, and the intelligent operation management level of the water conservancy project is improved.

The invention provides a digital port intelligent customs affair method and system based on artificial intelligence, and the method comprises the steps: collecting multi-source data, and carrying out the standardization processing of a data base, and generating customs declaration data flow; predicting the risk by using federal learning, and reinforcing learning to generate a checking scheme; verifying document consistency in combination with a block chain, and verifying document logic through OCR and a knowledge graph; and dynamically adjusting a cargo distribution strategy, performing digital twinborn simulation emergency response, and finally generating a full-process visual supervision view. The customs clearance efficiency, the risk prevention and control capability and the emergency response level can be improved.

The invention discloses a mine safety risk prevention and control method and system based on multi-source data fusion, and particularly relates to the technical field of mine safety monitoring, and the method comprises the steps: obtaining surface topography, an underground geologic structure and real-time monitoring parameters through remote sensing images, drilling data and dynamic monitoring equipment, and constructing a mining area geologic database with consistent time and space; fusing geologic feature data, integrating earth surface and underground feature data by using a three-dimensional geologic modeling technology, and mapping dynamic monitoring parameters into a model to generate a dynamic geologic feature field; performing clustering analysis on the abnormal feature points based on a spatial clustering algorithm, and calibrating a potential safety hazard region; calculating a regional risk comprehensive index by combining the risk values of the dynamic features, the geological features and the spatial features; early warning is triggered in a graded mode according to the comprehensive index, prevention and control measures are put forward, and the evaluation model is dynamically updated; comprehensive support is provided for mine potential safety hazard area monitoring and safety management decision making, and the method is suitable for risk management scenes of complex mining areas.

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 provides a risk defense method and device for jailbreak attack of a large language model. Risk defense is carried out from the input side, the model side and the output side of the large language model. The risk defense method comprises the following steps: S1, receiving input text information; s2, establishing a dynamic malicious keyword library on the input side of the large language model; s3, on the model side of the large language model, based on the training data transmitted in the step S2, training and learning model operation feedback information and model internal state information, and based on a dynamic adjustment strategy, adjusting the large language model; and S4, constructing an output auditing library on the output side of the large language model based on the pre-output text information transmitted in the step S3 and the dynamic malicious keyword library in the step S2. Through the cooperative work, the defense measures of each layer can play the maximum effect in the whole security framework, so that various potential attacks are effectively prevented and dealt with, and the overall defense capability of the model is jointly improved.

The invention discloses an intelligent planning method for space monitoring of an unmanned aerial vehicle. The method comprises the steps that intelligent path allocation is realized by constructing a task demand priority matrix; the method comprises the following steps: firstly, collecting geographical, climate and environmental parameters of a monitoring area, quantifying regional complexity and color features of a monitoring target by combining high-resolution image data with a neural network model, and generating a priority matrix according to task importance, change frequency and risk level; a Dijkstra algorithm is adopted to plan an initial flight path giving consideration to priority and flight limitation, a path complexity index is calculated, and the index comprehensively considers a target priority weight, a task detouring coefficient and a path relaxation degree; and finally, dividing a monitoring area into height layers according to a path complexity index threshold value, performing height layer adjustment on the initial path, and generating a dynamic flight path containing height layer switching, thereby realizing efficient resource allocation and accurate risk prevention and control in a complex monitoring scene.

The invention discloses a planning and scheduling system for optimizing road and bridge construction progress by using artificial intelligence, and relates to the technical field of road and bridge construction. The system comprises a data interaction system module, and the data interaction system module is bidirectionally and electrically connected with a model collaboration mechanism module. Through multi-source data fusion and model collaboration, the construction progress prediction precision is improved by 30%, resource waste is reduced by 20%, in the aspect of risk prevention and control, the geological disaster early warning advance rate reaches 85% through a space-time risk model, the social risk occurrence rate is reduced by 89% through public opinion monitoring, resource scheduling can be adjusted in real time through reinforcement learning and an evolutionary strategy, and the construction progress prediction efficiency is improved. The emergency risk response time is shortened by 50%, the block chain storage ensures that data cannot be tampered, the intelligent contract improves the multi-party collaboration transparency, and the construction management level is comprehensively improved. The problem that a road and bridge construction progress planning and scheduling system in the prior art does not dynamically adjust resource allocation in combination with risk factors and does not cover collaborative analysis of multi-source data is solved.

The invention discloses an intelligent enterprise financial data analysis system and method based on artificial intelligence, and belongs to the technical field of enterprise informatization management and intelligent finance. The objective of the invention is to solve the technical problems of low processing efficiency, insufficient data insight, lagging risk identification, weak intelligent decision-making assistance capability and the like in the existing enterprise financial data analysis process. The system generally integrates a data automatic acquisition and integration module, a financial data deep analysis and modeling engine based on artificial intelligence, a key financial index and operation performance intelligent calculation module, a multi-dimensional financial risk intelligent identification and dynamic early warning module and an interactive visual analysis report and decision support module. The core of the method is that internal and external multi-source heterogeneous financial and operation data of an enterprise are automatically collected and fused, deep processing and intelligent analysis are performed on the data by applying artificial intelligence technologies such as machine learning and natural language processing, and accurate portrait of the financial condition of the enterprise, dynamic evaluation of operation performance and real-time monitoring and prediction of financial risks are realized. And analysis reports and optimization suggestions with insight are generated, so that enterprise managers can make efficient and scientific operation decisions. According to the method, the automation and intelligence level of enterprise financial data analysis can be remarkably improved, the financial information quality and decision support efficiency are improved, the risk prevention capability of enterprises is enhanced, and the enterprises are assisted to realize refined operation and value creation.

The invention discloses an information management method and system based on an electronic bidding transaction platform, and relates to the technical field of information management. By constructing a multi-dimensional and multi-level information management system, deep mining and intelligent identification of potential association relationships and behavior patterns among bidding units are realized, so that the capability of the electronic bidding platform in the aspects of risk prevention and control and abnormal behavior identification is effectively improved. By fusing multi-source heterogeneous data such as industry and commerce registration information, bidding history, project contracts and the like and introducing atlas modeling and semantic analysis technologies, key risk factors such as legal person penetration, historical cooperation, high bidding document similarity and the like can be accurately identified, then the risk association strength between bidding units is quantitatively evaluated, and the risk assessment efficiency is improved. And a visual map and an auxiliary auditing mechanism are formed, so that the judgment efficiency of supervisors is improved.

The invention provides an intelligent risk identification and analysis method based on multi-modal heterogeneous data fusion, which relates to the technical field of risk management, and comprises the following steps of: acquiring multi-source data, constructing symbol-nerve double-space processing, generating semantic vectors, extracting features through orthogonal matrix decomposition and a bidirectional long-short-term memory network, and obtaining an intelligent risk identification and analysis result; according to the method, multi-modal information is fused by using an adaptive weight mechanism, and a risk propagation topology network is constructed by applying a graph embedding algorithm, so that accurate identification and early warning of risks are realized, and the risk prevention and control capability and prediction accuracy are effectively improved.

The invention relates to the field of intelligent traffic safety, in particular to a hazardous chemical substance transportation path dynamic risk prevention and control system and method based on space-time fusion, and provides a method for calculating a multi-dimensional risk index matrix through a risk index quantification module and combining a space-time environment sensitivity prediction matrix generated by an environment sensitivity analysis module. According to a risk spreading probability cloud picture of the risk spreading prediction module, accurate evaluation of the dynamic risk of the road section is realized; the system establishes a collaborative risk factor assessment model, adjusts a safety threshold according to historical accident data, and generates a space-time road network risk scoring matrix; the risk prevention and control decision module determines an early warning level, selects prevention and control measures, optimizes a transportation path, formulates an emergency response plan for a high-risk road section, and outputs an intelligent decision instruction set; according to the system, comprehensive quantification of hazardous chemical substance characteristics is realized, and the comprehensiveness of risk assessment and the intelligence of prevention and control decision making are improved.

The invention relates to the technical field of big data processing and analysis, in particular to a cross-border payment path optimization system and method based on multi-dimensional data analysis. Payment transaction data and exchange rate data are collected, a multi-dimensional payment path evaluation model is constructed from the angles of payment cost, payment timeliness, payment risk, payment availability and the like, and each payment path is scored in real time; constructing a payment path optimization model, dynamically selecting an optimal path and performing intelligent scheduling; a machine learning algorithm is introduced, an abnormal transaction mode is automatically identified from mass payment data, and a real-time anomaly detection and risk prevention and control strategy is established; according to the method provided by the invention, the success rate of cross-border payment can be improved, the cost is reduced, the payment time is shortened, and the payment risk is prevented, so that the cross-border payment service quality and the user experience are comprehensively improved.

The invention discloses an engineering construction quality safety risk management system, and belongs to the technical field of safety risks. Comprising a project management module, a quality safety risk management and control module, a compliance review and data analysis module, an integrated project management module, a risk identification and evaluation module, a risk prevention and control strategy module, a dynamic monitoring and early warning module, an accident handling and tracing module, a compliance review auditing module and a data analysis report module. According to the invention, basic information and design parameters of construction projects are collected through the project management module, a BIM model is established, construction simulation, conflict detection and resource optimization are carried out, an actual construction plan and schedule are formulated, multivariate data in the construction process are collected in real time, early recognition, evaluation and positioning of construction safety quality risks are realized by using the model, and the construction safety quality risk is evaluated and positioned. A risk prevention and control strategy is formulated, key parameters are monitored in real time, early warning is given out immediately when data exceed a threshold value, a risk model and prevention and control measures are improved, and efficiency and safety are improved.

The invention relates to the technical field of semantic decision management and control, in particular to a multi-modal semantic alignment intelligent decision and risk management and control system, which comprehensively and accurately captures cross-modal semantic association through multi-modal semantic alignment processing so as to generate a plurality of possible reasoning chains with reliability and interpretability. And combining dimensions such as knowledge conflicts and historical risks, calculating reasoning overlapping values to evaluate inter-chain association, realizing multi-dimensional and multi-angle analysis of potential risks, bringing risk fingerprint values and the reasoning overlapping values into a quantitative calculation framework of decision response values, dynamically setting a decision response threshold value, and realizing quantitative calculation of the risk fingerprint values and the reasoning overlapping values. The system can flexibly trigger emergency, early warning or monitoring response according to different risk levels, and refinement and differentiation of risk management and control are realized. The mechanism not only ensures timely disposal in a high-risk scene, but also avoids resource waste in a low-risk scene, and effectively balances risk prevention and control and execution efficiency.

The invention relates to the technical field of electrical safety, in particular to an electrical operator behavior analysis and safety risk prevention and control system which comprises a behavior data acquisition module, a data preprocessing module, a behavior analysis module, a historical risk database, a safety risk assessment module and a real-time early warning module. Wherein the behavior data acquisition module is used for acquiring behavior data and environment data of operating personnel; the behavior analysis module is used for constructing a behavior recognition model; the security risk assessment module is used for performing multi-dimensional risk assessment; the real-time early warning module is used for sending out early warning signals in various forms; according to the invention, through combination of accurate behavior feature identification, multi-dimensional risk assessment and a dynamic early warning technology, the problems of insufficient real-time monitoring of operation behaviors, incomplete risk assessment and untimely early warning response in the prior art are solved, and the safety and risk prevention and control capability in the electrical operation process are improved.

The invention relates to the technical field of meteorological engineering, and discloses a meteorological engineering project management risk assessment method, which comprises the following steps of: firstly, collecting historical risk event data of a meteorological engineering project and related meteorological conditions, engineering parameters and management measures, and performing data cleaning and integration to form a standardized risk data set; and based on the data set, constructing a risk prediction model, and predicting types and probabilities of risks possibly encountered by the project in the future. Meanwhile, a risk influence assessment system is established in combination with historical data, and the risk is comprehensively assessed from three dimensions of risk severity, occurrence possibility and controllability. The risk prediction and the assessment system are combined, the risk comprehensive influence value is calculated, then the risks are sorted and graded, and coping strategies are made for high-risk and medium-risk links. And finally, the risk assessment result and strategy are presented in a visual report form, comprehensive risk assessment support is provided for project management personnel, and the risk prevention and control capability of meteorological engineering project management is effectively improved.

The invention relates to the technical field of ballast water, in particular to a ship ballast water comprehensive treatment system which comprises a ballast water dynamic adjustment module, a ballast water risk management module, a ballast water safety optimization module and a ballast water ecological monitoring module. According to the invention, by monitoring the key sea condition data and the ship state in real time, the ballast water configuration can be adjusted in real time, and the balance and operation flexibility of the ship can be optimized, so that the ship can be kept stable in a changeable marine environment, the extra fuel consumption caused by improper configuration of the ballast water is obviously reduced, and the service life of the ship is prolonged. Potential risks in ballast water are intelligently assessed through the Bayesian network, the system can early warn the risks in advance and formulate coping strategies, so that the safety of the ship is improved, ecological threats are recognized in time through careful monitoring and analysis of biological and chemical components of the ballast water, the cross-regional spreading risk of pests and pathogens is reduced, and the risk of risk prevention and treatment is reduced. The ecological safety and the biological diversity of the destination water area are effectively protected.

The invention discloses an AI-based tendering and bidding full-process intelligent generation and review method and system. The method comprises the steps of tendering full-process intelligent generation, bidding full-process intelligent generation and bidding review. An AI scoring model is adopted for review, the technical scheme of each bidding document is deeply analyzed through a natural language processing technology, and real-time cross validation is carried out with a knowledge graph, so that the score of each sub-item is provided with detailed comments and a definite evidence chain, the objectivity, transparency and credibility of review are greatly improved, and the review efficiency is improved. The review quality is improved from experience judgment to a data and knowledge driven level; secondly, potential violation behaviors are identified through a surrounding bidding risk report generated through multi-dimensional correlation analysis, and the risk prevention and control capability is effectively enhanced; and finally, based on comprehensive data analysis, automatically generating a bid-winning candidate sorting and comprehensive review report, and ensuring that the calibration result is optimal.

The invention discloses an intelligent financial risk early warning method based on multi-modal data fusion, particularly relates to the field of financial risk management and control, and is used for solving the problem of existing enterprise transaction authenticity risk prevention and control. According to the method, a cross-modal transaction behavior chain of supervision enterprises is constructed through merging of transaction data in continuous account periods and modal information aggregation; generating an initial transaction relation graph based on the behavior chain, identifying business associated enterprises, and screening pseudo independent enterprises with control path crossing; performing modal semantic consistency check on the transaction path of the pseudo independent enterprise, identifying a semantic mismatch node, tracing a fund return path by taking the semantic mismatch node as a starting point, identifying a transaction closed-loop structure, and constructing a pseudo compliance risk chain; finally, the control relation and modal data are integrated, a structured evidence chain is generated, risk early warning output of involved enterprises is rapidly completed in a high-risk marking mode, and the automation degree, interpretability and accuracy of abnormal financial risk discrimination are improved.

The invention discloses a big data abnormal behavior risk detection method and device, equipment and a medium, and the method comprises the steps: constructing a time-space behavior tuple containing a timestamp, a geographic position and behavior characteristics through user behavior data, environment sensor data and external event data collected by terminal equipment; inputting the time-space behavior tuple into a neural network model, and generating a portrait feature vector representing a user behavior pattern; performing time sequence conversion on the portrait feature vector, and outputting an abnormal probability value through a multi-scale Transform model; based on the abnormal probability value, clustering behavior data of the same region and the same time period, and combining the external event data to construct a causal association map; and calculating contribution weights of environmental factors according to the causal association map, and determining risk tags according to the contribution weights. By analyzing the time-space behavior tuple of the user, the abnormal behavior detection and risk prevention and control capabilities are improved.

The invention relates to the technical field of coal mine safety, in particular to a coal mine major disaster risk prevention and control system and method based on artificial intelligence, and the system comprises a data collection module, a data transmission module, a data processing module, a disaster prediction module, a decision support module and a user interaction module. The data transmission module transmits collected environment data to a ground control center, the data processing module ensures the accuracy and consistency of data through data preprocessing, data fusion and anomaly detection, the disaster prediction module predicts potential disasters and generates a risk assessment report, and the decision support module determines the risk assessment report according to the prediction result. According to the system, multiple intelligent technologies are integrated, comprehensive prevention and control of major disasters of the coal mine are achieved, and the safety management level of the coal mine is improved.

The invention discloses a multi-energy access electric power spot market risk prevention and control method and system, and relates to the technical field of risk prevention and control, and the method comprises the following steps: obtaining multi-dimensional data of a to-be-detected region, obtaining the risk conduction intensity between nodes based on a power grid topological structure, and constructing a dynamic risk topological network model; according to the risk topology network model, node operation characteristics are extracted, evaluation data of the classification model are synchronously obtained, model switching regulation and control are carried out according to the evaluation data, and the model switching regulation and control further comprises the step of outputting a risk overflow probability; through the method based on the dynamic risk topology network and multi-model adaptive switching, the problems of inaccurate risk prediction and untimely response in multi-energy access to the electric power spot market are solved.

The invention discloses a supply chain data-oriented enterprise upstream and downstream collaborative risk control prediction method and system, and relates to the technical field of supply chain financial risk management, and the method comprises the steps: extracting a payment time deviation value between enterprises to construct a payment behavior sequence, calculating a supply chain collaborative credit rating based on a fluctuation amplitude and a historical default record, and recognizing a risk source enterprise; adopting a recursive partition time window to extract a period index and a sudden change index of the payment behavior, and constructing a payment behavior portrait; identifying associated enterprises through the phase overlapping degree of the payment behavior track to form a risk conduction sequence; and based on the evolution law and the risk conduction sequence of the payment behavior portrait, predicting a risk triggering node, and generating a risk prevention and control scheme including a credit line regulation and control instruction and a behavior monitoring strategy. The method can effectively identify the supply chain risk source, predict the risk conduction path, and improve the financial risk control capability of the supply chain.

The invention discloses a power operation mode intelligent arrangement and risk prevention and control method under mass new energy access, and relates to the technical field of power system operation control, and the method comprises the steps: constructing a main distribution network architecture data model, carrying out the fusion processing of dynamic data of different voltage levels and professional attributes, and generating a system operation state model; based on the system operation state model, topological structure analysis is executed, and a network connection relation model is established; and calculating a new energy access optimization scheme according to the network connection relation model, and generating an operation mode arrangement result and a risk prevention and control strategy. According to the method, the problems of data islands and inaccurate topology identification in the prior art are effectively solved. By constructing the multi-objective optimization model, the new energy access capacity can be scientifically evaluated, the optimal operation scheme can be generated, and the operation efficiency, safety and reliability of a power system under the condition of massive new energy access are remarkably improved.

The invention discloses an urban financial service full-link risk prevention and control management system, and belongs to the technical field of risk prevention and control. Comprising a multi-source heterogeneous data acquisition and treatment module, a dynamic risk portrait construction module, a real-time risk monitoring and early warning module, a risk disposal and decision support module, a cross-scene risk linkage analysis module, a compliance and privacy protection module and a system adaptive optimization module. Automation and manual cooperation of risk disposal are realized; a low-risk event automatically executes a preset action through a formula, so that the disposal efficiency is improved; medium and high risk events are pushed to an expert system, a disposal scheme is generated in combination with historical cases, and a block chain records an operation log to ensure transparency and traceability; the module balances machine decision and manual auditing boundaries, and the misjudgment risk is reduced.

The invention relates to the technical field of financial risk management, in particular to a big data-driven financial risk early warning system, which comprises a data detection module, a behavior risk rating module, a dynamic risk monitoring module, a behavior pattern clustering module and a financial risk early warning module. According to the method, the sensitivity and accuracy of risk detection are improved by analyzing small deviation between transaction behaviors and historical data, the dynamic risk monitoring function can realize quick response to abnormal transactions by continuously tracking the fund flow direction and the transaction frequency, and the risk detection accuracy is improved by analyzing the relationship between the fund flow direction and past risk events. According to the method, normal financial activities and potential risk behaviors can be effectively distinguished, an enterprise can take prevention measures before the risk actually occurs, and the clustering analysis of the risk behaviors can help to more accurately position a risk source, so that the measures are taken in a targeted manner, financial resources are optimized, and the risk prevention and control capability and decision-making efficiency are enhanced.