221 results about "Risk characteristics" patented technology

The invention discloses a project performance evaluation and risk early warning method for multi-source heterogeneous scientific research data, and relates to the technical field of scientific research project management, and the method comprises the steps: collecting the multi-source heterogeneous scientific research data, cleaning, converting, filling missing values, and storing in a unified format; extracting performance and risk features, and fusing to obtain a fused feature set; combining an analytic hierarchy process, an entropy weight method and the like to construct a dynamic weight performance evaluation model; constructing a multi-modal risk early warning model by adopting multiple algorithms and optimizing a threshold value; collecting data in real time to update a feature set, and dynamically adjusting the model; and visually outputting a result, generating an improvement suggestion, and forming closed-loop feedback. According to the method, the multi-source data processing efficiency and evaluation accuracy are improved, the risk early warning timeliness and adaptability are enhanced, a management closed loop is formed, and the problems of difficult data integration, evaluation lagging, insufficient early warning and the like of a traditional method are solved.

The invention relates to the technical field of computers, particularly discloses an engineering investment project multi-dimensional risk dynamic assessment and early warning system, and aims to solve the problems that existing risk assessment is single in dimension, insufficient in timeliness and lack of dynamic early warning and intelligent decision support. The system comprises a data acquisition and preprocessing module, a multi-dimensional risk feature construction module, a dynamic risk assessment and prediction module, a risk early warning and visualization module and an intelligent decision support and optimization module. Through integration of multivariate data, machine learning and deep learning algorithms, risk dynamic modeling, real-time evaluation and trend prediction are realized, and in combination with intelligent early warning and decision support, risk management is changed from post-remedy to beforehand prevention.

The invention discloses a specific scene safety risk identification method based on visual dynamic analysis, and belongs to the technical field of safety monitoring, and the method specifically comprises the steps: collecting environment parameter time sequence data and cargo state time sequence data of a monitoring region; respectively extracting an environment characteristic component and a cargo response component; calculating a quantized value of correlation strength of the two through an information entropy algorithm, and marking the quantized value as a cargo response entropy value; a cargo response entropy evolution curve is monitored, and when the change rate of the evolution curve exceeds a historical threshold value and the change acceleration is not zero, it is judged that a state transition event occurs; extracting environmental incentive parameters and cargo response parameters of a state transition event, encoding the environmental incentive parameters and the cargo response parameters into risk feature tensors, inputting the risk feature tensors into the accident incentive chain knowledge base, outputting a matched critical intervention parameter set, and formulating an environmental reconstruction instruction set and a cargo intervention instruction set according to the critical intervention parameter set; according to the invention, the intelligent level and risk response capability of dangerous cargo safety early warning are effectively improved.

The invention discloses an engineering safety early warning method and system based on artificial intelligence real-time risk identification, and relates to the technical field of engineering safety, and the method comprises the steps: collecting scattered engineering safety data from each engineering platform in batches, and carrying out the preprocessing of the data, and constructing a dynamic database; according to the engineering safety data collected in batches, an engineering safety knowledge graph is constructed, and different risk levels are preset according to engineering safety standards. According to the method, multi-source engineering safety data are integrated, dynamically changing risk characteristics are analyzed in real time by using an AI risk identification model, the hysteresis of traditional manual inspection and static analysis is overcome, a nonlinear relationship among the risk characteristics is captured by using a random forest model through integrated learning of multiple decision trees, and the risk characteristics are analyzed in real time. And the probability values of high, medium and low risk levels are output in combination with Softmax probability normalization, so that the evaluation precision is remarkably improved, the risk features are positioned, the scientificity of risk traceability is ensured, and data-driven decision support is provided for engineering safety management.

The invention provides a risk early warning method and system for medical insurance settlement data based on a knowledge graph, and relates to the technical field of medical insurance settlement, and the method comprises the steps: formulating a risk rule, obtaining the medical insurance settlement data, extracting a first entity and a relation thereof from the data, constructing the knowledge graph, and storing the knowledge graph in a graph database; and searching the second entity according with the risk rule and the relationship thereof in the graph database by utilizing the reasoning ability of the knowledge graph, extracting the risk characteristics of the second entity, determining the risk level of the second entity according to the risk characteristics, and sending a corresponding early warning signal, thereby improving the recognition rate of the complex associated risk in the process of medical insurance settlement.

The invention relates to the technical field of laboratory safety management, and discloses an AI model fused laboratory safety management risk intelligent pre-judgment method and system. The method comprises the steps of collecting original monitoring data of a laboratory monitoring area, and extracting risk signals and abnormal signals corresponding to abnormal events from the original monitoring data; obtaining relevance characteristics between the risk signals and adjustment characteristics of the risk signals by abnormal events; risk features representing risk differences are generated according to the features, and target risk probability distribution is obtained based on the risk features; and performing interference suppression processing on the original monitoring data according to the target risk probability distribution to separate a prediction risk waveform of the target risk source. According to the method, through deep mining of the relevance of risk signals and the adjustment effect of abnormal events, the accuracy of risk pre-judgment is improved, potential risks can be effectively captured, and a more intelligent and more reliable risk pre-judgment means is provided for laboratory safety management.

The invention provides an engineering dynamic risk assessment early warning system and method, and belongs to the technical field of engineering risk assessment. According to the method, engineering stages are divided, a stage label incidence matrix is constructed, historical cases are collected, an engineering stage-risk feature incidence matrix is constructed, a weight dynamic adjustment mechanism is set, construction data are collected in real time, the current stage is identified, risk features are extracted, risk weights are automatically adjusted, and dynamic risk comprehensive scores are calculated. And then the preset risk threshold is automatically called to divide the risk early warning level, so that scientific and dynamic assessment and early warning of the engineering risk are realized, core risk characteristics of different engineering stages can be accurately identified, the risk weight is dynamically adjusted according to real-time data, the risk assessment is enabled to better fit the actual construction condition, the risk early warning level is timely and accurately divided, and the risk early warning efficiency is improved. Powerful support is provided for engineering safety management, and the probability of occurrence of engineering accidents is effectively reduced.

The invention relates to the technical field of intelligent auxiliary approval, in particular to a collaborative process intelligent auxiliary approval method and system based on dynamic risk modeling, and the method comprises the steps: obtaining a delay factor of each approval role type node according to the difference between all approval durations of each approval role type node in all structure trees and a reference approval duration; obtaining an overall delay factor of each approval task according to the delay factor and the weight coefficient; mapping all approval tasks in the history in the risk feature space to obtain a plurality of data points; clustering all data points in the risk feature space to obtain a plurality of class clusters; and according to the distance between each point to be analyzed and the center points of all the class clusters and the distance between the center points of the class clusters and the original point, obtaining an abnormal risk factor of each point to be analyzed so as to obtain a priority, and performing approval through the priority. According to the invention, the efficiency and fluency of intelligent auxiliary approval are improved.

The invention relates to the technical field of fire detection, and provides an intelligent sensing multifunctional integrated sensor for fire. The sensor realizes synchronous real-time monitoring of a plurality of risk characteristics of a target area through a plurality of sensing monitoring modules triggered by a weight-based composite event, and when any sensor monitoring module monitors that the target area has the risk characteristics and the corresponding characteristic value reaches a preset characteristic threshold value, the risk characteristics of the target area can be monitored on the basis of different weight mechanisms. The dynamic risk weight of the target area is calculated through the feature dynamic weights of different sensing monitoring modules, and risk dynamic grading early warning is carried out on the target area; in the process of monitoring the target area by the sensing monitoring module, the feature dynamic weight of the sensing monitoring module dynamically changes along with the change of the feature value of the monitored risk feature, so that the fault-tolerant capability of the target area during risk early warning is improved, and the problems of false alarm and missing alarm caused by the single monitoring function of the existing sensor are effectively solved.

The invention discloses a risk assessment method and device based on a trusted data space and related equipment. The method comprises the steps of obtaining a digital contract, participant information, transaction data, environment data and data space operation data of the trusted data space; carrying out anchor point hash calculation to determine whether tampering is carried out; obtaining a plurality of risk indexes and risk characteristic values; and based on the global weight and the risk feature value of each risk index, obtaining the size of each risk level preset by the digital contract. According to the method, whether the data information is tampered or not can be quickly determined through anchor point hash calculation, changes of multi-source heterogeneous data such as digital contracts, participant information, transaction data, environment data and data space operation data can be determined in real time by utilizing the preset large language model, and risk judgment is made in real time. In addition, when the risk is judged based on the risk characteristic value of each risk index, the importance degree of each risk index in the global data contract is considered, so that the judgment is better.

The invention discloses a tunnel construction safety risk management and control method and system. The method comprises the following steps: collecting multi-source sensor data of a construction site, constructing a continuous time sequence monitoring data set, introducing a Hampel model to detect abrupt change and abnormity and identify sudden risk features, and introducing a CUSUM model to detect a slow change trend and extract evolutionary risk features. Time synchronization and vector combination are carried out on the two types of risk features, a joint risk feature matrix is constructed, dominant risk types are further judged, dynamic weighted fusion is implemented, a fusion risk index is generated, risk levels are divided according to the fusion risk index value, and the risk level is determined by combining the dominant risk types and the risk trend. And an informatization risk response result including a response level, a response strategy and trend prediction is generated, and accurate identification and intelligent management and control of tunnel construction risks are realized. The invention is suitable for dynamic risk early warning and management of a tunnel engineering construction site, and belongs to the technical field of tunnel engineering safety management.

The invention relates to the technical field of risk identification, in particular to an intelligent automobile operation high-risk scene identification method and device based on deep learning and a storage medium, and the method comprises the steps: collecting multi-modal data of an intelligent automobile operation scene; determining the space-time evolution of the risk features based on the fused feature sequence, and updating the global risk feature map based on the space-time evolution; and determining a risk mark of the operation scene based on the updated global risk characteristic spectrum, and distinguishing a static risk region and a dynamic risk region in the risk mark based on the unupdated global risk characteristic spectrum and the fusion characteristic sequence to obtain a high-risk candidate region corresponding to the operation scene. According to the method, the multi-modal data is collected in real time, and the data is fused and coded in combination with deep learning, so that a high-risk scene in the operation of the intelligent automobile can be accurately identified, and potential risks can be dynamically evaluated and predicted, thereby providing more accurate decision support for the intelligent automobile and avoiding possible accidents.

The invention discloses a dam hidden danger risk grade assessment method. The method comprises the following steps: S1, acquiring detection data by adopting a comprehensive geophysical detection technology; s2, performing mileage checking and standard batch processing on the acquired detection data; s3, acquiring spatial feature information of hidden dangers; s4, acquiring physical parameter information of different mileage hidden dangers of the dam profile and rock-soil parameter information of the dam structure, and establishing a relation criterion between physical parameters and rock-soil parameters; s5, establishing a hidden danger risk characteristic evaluation index data system; s6, establishing a multi-source information fusion neural network model; and S7, inputting hidden danger risk characteristic evaluation index data into the model, and calculating risk grade division results of different mileage positions of the dam. According to the method, the conversion from the dam hidden danger to the hidden danger risk is realized, the hidden danger interpretation and risk assessment model is established, the dam risk grade mileage map and the dam profile map of the corresponding risk mileage are drawn based on the model result, and the grading of the dam risk is realized.

The invention provides a panoramic analysis method and device for historical risk data, and relates to the technical field of Internet. According to the method, panoramic splitting is performed on multi-source data of historical risk events from two aspects of time and space, risk features such as personnel, time, places, subjects, behaviors and influence ranges of each stage of each historical risk time are extracted, causal chain deduction is performed, panoramic causal analysis among multiple dimensions related to the risk events is realized, and the risk events are analyzed more accurately. According to the method, the inducing causes and conduction paths of the risk events can be analyzed from multiple dimensions, so that the risk causes can be identified more quickly and accurately. The problems that a traditional single data source risk analysis mode is low in recognition efficiency and high in false alarm rate are solved, the recognition efficiency of risk events is improved, and the false alarm rate is reduced.

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 discloses a dynamic risk assessment method based on multi-source heterogeneous data and application. The method comprises the following steps: extracting risk features from the multi-source heterogeneous data; constructing a multi-dimensional risk assessment model based on the labeled risk features; and based on the multi-dimensional risk assessment model, the risk is scored. According to the method, multi-source heterogeneous data can be effectively fused, risk features are extracted through a unified data processing flow, a multi-dimensional risk assessment model is constructed, risk scoring based on the multi-dimensional risk features is realized, the problems of coarse assessment granularity and insufficient information coverage caused by dependence on a single data source in the prior art are solved, and the risk assessment efficiency is improved. And the comprehensiveness and the accuracy of a risk assessment result are remarkably improved.

The invention discloses a stable support analysis method for a tunnel face of a shield tunnel in a composite stratum, which relates to the technical field of support analysis, and comprises the following steps of: dividing the tunnel face and a front area thereof, classifying the areas with similar deformation tendency or instability tendency into the same kind, and setting differentiated support control targets for each area, the method comprises the following steps: constructing a balanced scheduling framework of support and settlement, extracting related risk characteristics before entering a risk working condition, inputting the related risk characteristics into a safety evaluation model, performing preventive correction on a tunnel face pressure regulation amplitude, a propulsion rate change slope and a grouting strategy according to an evaluation result, and in the whole analysis and control process, performing safety evaluation on the tunnel face pressure regulation amplitude and the propulsion rate change slope. And performing cross check by using a numerical method of different dimensions and field measurement, feeding back to the equilibrium scheduling framework when a difference is found, and re-correcting a support strategy. According to the analysis method, a scheduling framework is constructed through real-time data and partition classification, safety evaluation and cross checking are combined, the defects of composite stratum analysis are overcome, and the stability of the tunnel face and the construction safety are improved.

The embodiment of the invention discloses a risk quantification and dynamic evaluation method and device, electronic equipment and a storage medium, and the method comprises the steps: accessing risk related data from a plurality of data sources, and carrying out the preprocessing, so as to obtain risk feature information; calculating a risk weight based on an improved entropy method in combination with the risk diffusion factor and the risk correlation degree; calculating an association degree matrix among the risks by using a time sequence analysis model, and generating a conduction probability through iterative simulation by taking the association degree matrix as an input parameter of conduction simulation; generating a dynamic risk matrix according to the risk weight and the conduction probability, and displaying the dynamic risk matrix in a visual mode; monitoring data change in a data source in real time, triggering recalculation of the risk weight when detecting that a risk state is changed, and dynamically adjusting simulation parameters; quantitative and dynamic assessment of risks can be realized, and the defects of subjective deviation, weak multi-risk association analysis capability, lack of dynamic adaptability and the like of a traditional risk assessment method are overcome.

This invention provides a dynamic assessment method and system for cloud service intelligent operation and maintenance security risks based on artificial intelligence, belonging to the field of artificial intelligence technology. First, it captures all operational and maintenance data of cloud services in real time to form a data set covering various key data types. Then, it inputs the data set into a risk causal tracing model to mine the causal correlation between the data and historical security risk events, generating tracing results. Based on the tracing results, it drives the self-evolutionary adjustment of security risk assessment dimensions, generating a self-evolutionary assessment dimension set adapted to the current risk characteristics. Next, it inputs the data set and tracing results into this evolutionary assessment dimension set to simulate changes in the cloud service's operational state, obtaining risk evolution simulation results. Finally, based on the risk evolution simulation results and the current operational resource status, it generates a response strategy that coexists with the risk evolution trend and sends it to the operation and maintenance execution system. This invention can dynamically adapt to changes in cloud services, ensuring the stable and secure operation of cloud services.

This application relates to the field of network situational awareness technology, and discloses a method and device for network security situational awareness. The method includes: identifying regulatory entities and collecting abnormal events; identifying interactive and operational behaviors as risk characteristics; constructing a first risk identification model and deploying it on an edge terminal; collecting the type and sensitivity attributes of the regulatory entities; dynamically forming regulatory assistance pairs based on the attributes; generating a second risk identification model by weighted averaging of the parameters of the first model based on the regulatory assistance pairs and deploying it in the cloud; real-time interactive data is preferentially screened by the cloud-based second model; if a risk is identified, a structured report is generated and distributed; if not identified, it is reviewed by the first model at the edge terminal. This application achieves distributed intelligent perception with cross-domain collaboration and data not leaving the domain, solving the model bias and centralized delay problems caused by insufficient samples of a single entity, and significantly improving detection accuracy, response efficiency, and collaborative defense capabilities in low-sample scenarios.

The present application relates to the field of information technology services, and discloses a record product risk early warning method and system based on a component library, comprising: extracting a full component list of a record product from product record data to construct a component database corresponding to the full component list by using a preset component matching rule; establishing a component quick search index and a risk feature set of the component database to match potential risk components corresponding to the full component list in the component database; constructing a multi-dimensional risk portrait corresponding to the record product by calculating the compliance correlation degree of each component in the full component list and the potential risk components; and outputting a risk early warning result of the record product according to a risk assessment conclusion corresponding to the record product and the multi-dimensional risk portrait. The present application can greatly improve the initiative foresight and prevention accuracy of record product risk early warning.

This invention relates to the field of nursing technology and provides an internet-based remote monitoring method and system for health care. The method includes: screening for deviation events and identifying risk monitoring nodes, extracting sampling data only for these risk monitoring nodes; performing comparative analysis on computational parameters in chronological order, comparing the magnitude of parameter fluctuations with preset fluctuation thresholds based on historical data statistics, and identifying key parameters with significant fluctuations; and generating predictive intervention nodes through a preset assessment model. This invention avoids indiscriminate processing of all data by locating effective data segments with high-risk characteristics from the raw data, reducing computational load and data transmission pressure, and improving the targeting and efficiency of risk assessment. By identifying fluctuation nodes where key parameters change, it can more sensitively capture the initial signs of potential risks and improve the accuracy of risk identification.

The application discloses a kind of project-oriented full life cycle intellectual property risk collaborative management method and system, it is related to project full life cycle management data processing technical field, the present application obtains the multi-source relevant data of each stage of target project full life cycle, constructs project intellectual property risk management and control benchmark database, completes data standardization processing and risk characteristic mapping, establishes the intellectual property risk grading management and control system covering project full stage, completes project full cycle risk identification, grade division and early warning by intellectual property risk intelligent identification model, issues risk disposal task to corresponding responsibility subject to realize multi-subject collaborative management, iteratively optimizes risk management and control strategy and outputs adapted management and control optimization scheme.The present application realizes project full life cycle intellectual property risk full coverage management and control, improves risk identification accuracy and multi-subject collaborative disposal efficiency, provides stable support for project intellectual property risk management and control.

The application relates to a kind of port entry and exit special goods risk identification methods based on knowledge graph construction and entity linking, comprising: according to the risk identification demand of port entry and exit special goods in advance, constructing hierarchical risk factor knowledge graph;Obtain the declaration material of risk to be identified, carry out entity extraction from the declaration material, retrieve historical similar cases according to the extracted entity, build enhanced context containing declaration material and its historical similar cases, and carry out overall vectorization processing;Adopt three-granularity matching strategy: first, through Top-K screening, to match special goods subclass with coarse granularity;Then through threshold filtering, to match risk factor with medium granularity, finally, fine-grained matching risk feature node, to obtain the risk identification result of port entry and exit special goods.Compared with the prior art, the present application has the advantages of more precise risk feature expression, explicit risk knowledge audit, and realizes accurate matching from declaration material to knowledge graph node.

The invention discloses a meat safety risk early warning model based on AI fast detection data and a service method, and relates to the field of food risk control. By extracting four types of risk features of time, space, category and circulation, an LSTM + DBSCAN + XGBoost fusion model is constructed, and the early warning advance period is longer than or equal to 24 hours; a regional thermodynamic diagram is output for the government, a batch prevention and control scheme is pushed for an enterprise, the supervision efficiency is improved by 90%, and the annual average loss of the enterprise is greater than or equal to 80,000 yuan. The method solves the problems of passive response, low data value and single service, realizes active prevention and control of meat safety, and is suitable for government supervision and enterprise quality control scenes.

The invention discloses a high plateau airport operator unsafe behavior monitoring and early warning method and device, and relates to the technical field of plateau operation monitoring. According to the method, algorithm such as YOLOv8 and feature fusion are combined to collect physical and behavior data from multiple sources and extract features, then a bimodal model is constructed by SVM and logistic regression, and a comprehensive fatigue level is generated by matching with an evidence theory, so that the limitation of single-modal data is avoided; meanwhile, a fuzzy Bayesian dynamic fusion model is introduced, and a fatigue score is corrected in combination with specific environmental data (low pressure, low temperature and the like) of the high plateau, so that risk judgment is more adaptive to physiological-environmental collaborative risk characteristics of the high plateau scene; and finally, the graded early warning is triggered based on the comprehensive risk grade of fatigue, environment and space dimensions, so that the accuracy of risk identification is realized, the timeliness of high-plateau operation safety management is improved through differentiated intervention, and the potential safety hazard of high-plateau operation is effectively reduced.

The invention discloses a multi-modal AI-based project risk prediction and dynamic early warning method and system. The method comprises the steps of collecting multi-modal data and extracting feature information; constructing the feature information into a standardized risk feature model with a unified structure; carrying out calculation through the risk prediction model, and outputting a multi-dimensional risk index; a corresponding early warning and response mechanism is automatically matched and triggered through a dynamic rule engine; and automatically generating at least one risk disposal scheme based on a case-based reasoning method in combination with the current risk characteristics and the multi-dimensional risk indexes. According to the method, the risk feature model is innovatively constructed, the characterization of heterogeneous features is unified, and the problems of data islands and feature isomerism are fundamentally solved; a multi-dimensional risk index is output through the risk prediction model, and three-dimensional risk situation assessment is provided; through a case-based reasoning engine, a customized and operable disposal scheme can be automatically generated based on historical experience, and the problems that a response mechanism is rigid and depends on manpower are solved.

This invention relates to the field of AI-based risk control technology in financial payments, specifically to an AI-based real-time payment risk early warning method and system. The method includes: acquiring transaction behavior feature records containing standardized transaction amount ranges, transaction time slice distributions, and a relational graph of transaction participants; deconstructing the relational graph using a graph structure analyzer to extract potential indirect correlation paths between the transaction initiator and historical risk accounts, generating a risk correlation feature vector, and combining this with multi-dimensional behavioral analysis to obtain a behavioral deviation index; inputting the two types of features into a risk decision fusion model to obtain a comprehensive risk score; generating a dynamic risk threshold based on the risk level; triggering an early warning signal when the score exceeds the threshold, and generating an early warning instruction containing a risk cause code and interception suggestions. This method can uncover multi-level hidden risk correlations, dynamically adapt to the threshold settings for transaction risk levels, improve the completeness of risk feature extraction, and optimize the adaptation effect of risk early warning judgment.

The invention discloses a production process quality system risk assessment method for a digital workshop, and relates to the technical field of intelligent quality management and control, and the method comprises the steps: endowing each work-in-process entering the digital workshop with a unique initial risk identifier; after the processing of the current procedure is completed, collecting industrial data of the in-process product, generating a risk characteristic value based on the industrial data, and updating the initial risk identifier to form a dynamic risk chromosome; the industrial data comprises quality detection data, process parameter flow and equipment state data; constructing a cellular automaton network based on a process logic relationship in the production scheduling plan; when the work-in-process flows to a new process, inputting the risk characteristic value into a cellular automaton network, and calculating a risk conduction direction and a risk conduction probability through a cellular state evolution equation; according to the method, the process logic relation in the production scheduling plan is mapped into the cellular automaton network, so that dynamic modeling and quantitative analysis of the risk conduction path in the production process are realized.

The invention provides a business risk identification method and device, equipment, a medium and a program product. Relates to the technical field of risk identification. The method comprises the following steps: for any online service, acquiring a service image corresponding to the online service at a preset time interval in the online service handling process; the service image comprises a user who handles the online service; for any business image, determining image risk features of the business image according to a multi-modal large model and a preset risk feature database; and according to the image risk characteristics of each service image, determining a service risk level in the online service handling process, and when the service risk level is greater than or equal to a preset level threshold, determining that the online service has a risk and stopping service handling. According to the method provided by the invention, the technical problem of poor generalization ability of the existing risk identification technology in the online business handling process can be solved.