131 results about "Business impact" patented technology

The invention provides an intelligent customer risk assessment system and method based on a large language model, and relates to the technical field of risk assessment, and the method comprises the steps: obtaining multi-modal data of a customer, carrying out the preprocessing, and extracting structured and unstructured features; constructing a hierarchical risk knowledge system, and realizing adaptive evolution of the knowledge system through a graph neural network and a generative model; constructing an initial negative sample library, and constructing a negative sample database in combination with a non-risk mode labeled by an expert and derivative layer analysis; optimizing the large language model by adopting a strong supervision, weak supervision and reinforcement learning cooperative training mechanism under each classification according to the customer type; mining risk features in a text by using the optimized large language model, processing multi-modal data through a multi-level attention network, and generating a positioning report including contradiction type coding, service influence dimension evaluation and risk level quantification; the accuracy, efficiency and flexibility of customer risk assessment are improved, and the risk management strategy is optimized.

The invention provides a digital asset-oriented network security management method and system. The method comprises the following steps: acquiring a digital asset list and a business process document, identifying keywords in the document by using a natural language processing technology, constructing an association relationship graph of digital assets and business processes, and discovering potential association; directly connected business process nodes of each digital asset are extracted from the incidence relation graph, an asset business influence range report is generated, and a many-to-many mapping relation between the assets and the businesses is displayed in a matrix form; in the review process, association and quantitative indexes, including a risk thermodynamic diagram, an influence degree scatter diagram and the like, among assets, risks and business processes are dynamically presented through a visualization technology, and a visual information transmission channel is provided for all parties; and after meeting review is consistent, qualitative influence analysis of the business department and quantitative loss estimation of the technical department are integrated into a business influence analysis result by adopting a fuzzy comprehensive evaluation method, and a comprehensive business influence level is obtained.

The invention discloses an AI-based network security event tracing method, system and device and a medium, and the method specifically comprises the steps: constructing a network entity association graph based on a multi-modal data set, mining the implicit association between entities through a graph convolutional network, recognizing an APT attack chain, and obtaining graph feature data; based on the multi-modal data set, an LSTM-Transform hybrid model is adopted to analyze time sequence characteristics of network traffic, slow penetration and low-frequency detection behaviors are detected, and time sequence characteristic data are obtained; based on the graph feature data and the time sequence feature data, high-value features are screened through a genetic algorithm, and cross-modal combination features are generated by using a depth auto-encoder; based on cross-modal combination features, a network environment digital twin is constructed, an attack diffusion path is simulated, and a service influence range is quantified. According to the method, accurate tracing of the network security event is realized, and the detection and tracking capabilities of complex network attacks and the intelligent level of a response strategy are comprehensively improved.

The invention discloses a backup disaster recovery optimization method, and relates to the technical field of IT operation and maintenance and disaster recovery, and the method comprises the steps: analyzing a statistical dependency relationship between basic performance indexes through employing a causal discovery algorithm based on a standardized monitoring data flow, and constructing a directed weighted graph; when a basic performance index in the directed weighted graph deviates from a normal performance baseline, triggering a reverse traceability positioning root cause component, identifying a service influence path through forward path analysis, and generating a backup enhancement instruction; in a digital twin environment, according to historical drill records and a strategy library, multiple potential disaster recovery switching schemes are generated, parallel deduction is carried out, the performance influence of each potential disaster recovery switching scheme on a virtual service link is quantitatively evaluated in the deduction process, and the optimal disaster recovery switching scheme is selected by comparing evaluation results. According to the invention, the prospective optimization of the disaster recovery decision and the minimization of the business influence are realized, and the automation, intelligence and business adaptation capabilities of the backup disaster recovery system are enhanced.

The invention provides a financial machine room AI operation and maintenance method and device and a readable storage medium. The method comprises the steps of obtaining multi-modal data; preprocessing the multi-modal data to obtain preprocessed data; transform depth feature fusion is carried out on the preprocessed data, and a fusion feature vector is obtained; according to the fusion feature vector, LSTM time sequence prediction and evaluation are carried out, a prediction result is obtained, and the prediction result comprises at least one of the equipment health degree score, the fault occurrence probability, the business influence degree and the residual service life of the equipment. According to the application, the physical state monitoring of the equipment and the real-time state of the financial service are deeply fused, so that the fundamental transformation from the traditional passive response to the active prevention is realized. According to the invention, a business-aware intelligent prediction model and a transaction-aware-free resource scheduling mechanism are constructed especially for the high availability requirement and the transaction continuity guarantee requirement of a financial machine room.

The invention discloses a work order risk assessment method and device and electronic equipment. The method comprises the steps that work order information of a target work order is acquired, and the work order information comprises a business system related to the target work order and a business influence range of the target work order on a business process; a target risk rule triggered by the work order information is determined, a risk score corresponding to the target risk rule is determined, and the target risk rule is used for extracting risk features in the work order information; and determining the business system, the business influence range and the risk score as influence factors, and determining the risk level of the target work order according to the influence factors. According to the invention, the technical problem that a work order risk identification method adopted in the related technology is difficult to accurately identify and measure the work order risk is solved.

The invention discloses an intelligent service access management method and system based on a Python service management platform, and relates to the technical field of industrial internet, and the method comprises the steps: collecting fine-grained telemetry data associated with a service context; generating a real-time state portrait which contains a predictive future risk score and faces a specific business request type for a downstream service instance based on the data; automatically diagnosing whether the service instance has a silent fault or performance slow descent or not by applying a machine learning algorithm; the business influence domain of the diagnosed fault on the specific business process or the key performance indicator is automatically determined in combination with the transaction context graph and the tracking data; and when a service access request is received, intelligent decision making and execution are carried out. According to the invention, through providing refined and foresight evaluation of the service state and automatic association analysis capability of hidden faults and business influences thereof, intelligent risk perception and regulation of service access are realized, and improvement of stability, reliability and operation and maintenance efficiency of a business platform is facilitated.

The invention discloses a fault positioning method, device and equipment and a computer program product, and relates to the technical field of computer software, and the method comprises the steps: carrying out the anomaly detection of a to-be-detected business system, and obtaining a plurality of pieces of anomaly information; analyzing the multiple pieces of abnormal information to obtain an abnormal association relationship; and identifying the abnormal association relationship, and performing layer-by-layer recursive positioning of a fault cause according to the obtained abnormal topological relationship until analysis of each layer in the abnormal topological relationship is completed. The abnormal information in the service system is detected in real time, and data support is provided for fault positioning. By analyzing the abnormal association relationship between the abnormal information and further identifying the abnormal topological relationship, the service influence range can be identified. And according to the topological relation, recursively drilling down to a downstream service and resource level layer by layer until the root cause of the fault is positioned, so that the fault type and the fault point position are accurately positioned, and guarantee is provided for service continuity.

A dynamic, infrastructure-aware incident response and automated problem resolution system (100) for cloud environments, comprising: (a) an infrastructure and topology mapping module configured to continuously scan, discover, and map virtualized and containerized cloud resources and their interdependencies in real time; (b) an incident detection and correlation module configured to ingest telemetry data from distributed monitoring sources and apply rule-based and machine learning models to detect, correlate and classify system incidents; (c) a context analysis and impact assessment module configured to analyse the scope, severity and business impact of incidents based on real-time infrastructure context and service dependency diagrams; (d) a policy-driven decision engine configured to dynamically select response actions based on predefined rules, compliance policies, service level agreements and historical incident data; e) an Automated Remediation Orchestrator configured to execute predefined or dynamic remediation playbooks through integrations with cloud orchestration and configuration tools; and (f) a feedback loop and learning module configured to collect post-incident data, evaluate the effectiveness of remedial actions, and continuously refine the detection and response logic using machine learning algorithms.

The invention discloses a cross-platform session state reconstruction method and system based on event traceability, and relates to the field of cross-platform customer service integration, and the method comprises the steps: introducing a measurable business influence weight for a cross-platform field event through an event formatting mode based on business semantics, and maintaining a cross-platform event sequence in combination with a vector clock; according to the method, the real key events in the business meaning are adaptively selected to generate a state abstract through the time sequence accumulation calculation of the event weights, and snapshot is not carried out based on fixed time or a fixed number, so that the data transmission quantity and the calculation overhead required by state reconstruction are remarkably reduced; in the recovery stage of the client, only the abstract and the increment event after the abstract need to be transmitted, so that the client can still quickly reestablish the state at lower cost in a weak network and low bandwidth environment. Therefore, differential compression and recovery of a cross-platform event traceability semantic level are realized, and session reconstruction efficiency and resource utilization efficiency in a customer service large-scale multi-platform scene are greatly improved.

The invention provides a database anti-ransomware method and device based on user SQL (Structured Query Language) feature behavior analysis, and relates to the technical field of database security. The method comprises the following steps: constructing an SQL data set by utilizing a multi-source data aggregation algorithm, a real-time and historical combination algorithm and a data cleaning and checking algorithm; extracting SQL statement structure features according to BERT and a semantic matching method; according to the features, training a user SQL behavior model based on a Transform; in the real-time monitoring stage, a monitoring engine captures a new SQL operation in real time, rapidly extracts features and inputs the features into the trained behavior model for evaluation. Once the deviation between the operation and a normal behavior model exceeds a preset threshold value and suspicious behavior modes such as data encryption, large-scale data deletion or abnormal high-authority operation similar to ransomware are presented, an alarm mechanism is triggered immediately, and a preset protection strategy is executed automatically, so that data loss and business influence are reduced to the greatest extent.

The invention discloses a system for analyzing the influence of Internet finance on the traditional business of a CB bank, and the system comprises a data collection module, a data processing module, an influence analysis module, a visual display module, a coping strategy module, and a trend prediction module. The data acquisition module is used for collecting traditional businesses and macroeconomic data of an Internet financial platform and a CB bank, the data processing module cleans, arranges and stores the acquired data, and the influence analysis module analyzes the Internet financial businesses and the traditional banking businesses through comparison. And the influence of Internet finance on the traditional business of the CB bank is researched. By taking a CB bank as an example, the influence of Internet finance on deposit, loan and payment businesses of the CB bank is empirically analyzed from a microscopic perspective; comprehensive theory and practice support is provided by integrating literature, data, comparison and case analysis methods; a specific coping strategy is also provided, and reference is provided for the CB bank and other commercial banks to cope with challenges.

The large-model-based data security risk automatic research and judgment processing system comprises a fusion research and judgment module which is used for carrying out comprehensive research and judgment on the risk level, attack intention and potential influence of a security event based on security data in combination with deep learning and a large-scale language model, and transmitting a research and judgment conclusion to an automatic processing module; the business process disassembling and analyzing module is used for automatically discovering and modeling based on the security data through process mining and flow analysis, perceiving a business process and a dependency graph associated with a security event in real time, evaluating potential influences of different disposal measures on business continuity, and transmitting business influence evaluation to the automatic disposal module; and the automatic disposal module is used for selecting and executing an optimal risk disposal strategy from the security strategy library by adopting a distributed architecture of an AI intelligent agent according to the research and judgment conclusion and the business influence evaluation. The accuracy of alarm study and judgment is improved, 'alarm fatigue 'is relieved, and the method has high elasticity, high fault tolerance and strong cooperative capability.

The embodiment of the invention discloses a distributed database synchronization method, a computer device, a medium and a product. The distributed database synchronization method comprises the following steps: determining to-be-synchronized information in a source database; the to-be-synchronized information comprises a plurality of nodes and data table information corresponding to each node; capturing data change information of the to-be-synchronized information in a preset period; the data change information comprises a data change frequency and a query frequency in a data table corresponding to each node; determining business influence based on the data change frequency and the query frequency; dynamically adjusting the data synchronization priority of the corresponding node based on the service influence and the data change information; and executing a data synchronization task based on the dynamically adjusted data synchronization priority. According to the method, data synchronization in different environments can be met, the flexibility is high, the universality is good, the synchronization efficiency is high, the accuracy is high, and the timeliness and the consistency of the data in the distributed environment can be realized.

The invention discloses a security vulnerability detection system for computer application software, and relates to the technical field of software security, the system comprises the following components: a multi-source data acquisition module for acquiring a security vulnerability by analyzing source codes, analyzing dependent files, capturing network resources and investigating a business process mode; a software code structure, third-party component dependence, a CVE vulnerability library, an attack chain knowledge library, industry compliance requirements and business process rule data are respectively collected, and standardized preprocessing is carried out; according to the invention, industry scenarized knowledge is fused through a knowledge graph to form a system containing association of "vulnerability hazard-business influence-compliance risk", and in a vulnerability detection and analysis module, based on association information output by a graph neural network, in combination with a node association relationship in the knowledge graph, the vulnerability hazard-business influence-compliance risk is determined. The method can accurately analyze the influence of vulnerabilities on the business process and the compliance risk caused by the vulnerabilities, and can generate a detailed vulnerability detection report.

The invention discloses an electric power system vulnerability dynamic risk assessment method and system. The method comprises the steps of obtaining power grid assets, business functions borne by the power grid assets and vulnerability information; calculating asset physical importance based on the real-time data of the power grid; for each vulnerability, respectively calculating a physical risk evaluation value and a business influence evaluation value of the vulnerability; and finally, carrying out weighted fusion on the two to obtain dynamic comprehensive risk scores and sorting. By fusing vulnerability static attributes, asset dynamic physical importance and business criticality, a scene-adaptive multi-dimensional risk assessment model is constructed, and the transformation of the vulnerability risk of the power system from'static pervasive assessment 'to'dynamic accurate portrait' is realized. According to the method, the integrating degree and decision guidance value of the risk assessment result and the actual security situation of the power grid are improved, the problem that the assessment result is one-sided and rigid due to separation from a business scene and a physical operation state in the prior art is solved, and the initiative and accuracy of network security protection of the power dispatching system are remarkably improved.

The invention relates to the technical field of data processing and intelligent prediction, in particular to a quality prediction system and method based on multi-dimensional feature coupling driving, and the system comprises a business logic solidification module which is used for extracting and solidifying business influence logic, and generating static risk factors; the real-time working condition comparison module is used for comparing the real-time operation data and generating a dynamic deviation file; the risk trend extrapolation module is used for processing the time sequence of the dynamic deviation file and outputting a prospective risk track; and the final risk grading module is used for integrating the static risk factor and the prospective risk track, grading and outputting a final quality risk assessment value. According to the method, the static inherent risk of the business process and the dynamic evolution trend of the real-time data are deeply coupled, so that the problem that multi-dimensional information is difficult to fuse in a traditional method is solved, the global association effect of the current deviation in the business process is quantified, and the accuracy and interpretability of quality risk prediction are improved.

The invention relates to a fault prediction method and system for parameter fine tuning and adaptive structure pruning based on business influence driving, and belongs to the technical field of artificial intelligence, time series data analysis and intelligent operation and maintenance. Comprising the following steps: S1, business influence data modeling: integrating multi-source operation and maintenance data and historical business fault event data, and constructing a business influence quantitative model; s2, efficient fine tuning of service influence guide parameters: loading the pre-training fault prediction model, and performing efficient fine tuning of the parameters on the basis of service influence signals; s3, business influence driven adaptive structure pruning; S4, dynamic reasoning and alarm generation: realizing a dynamic reasoning mechanism of a pruned fault prediction model, and generating a structured and business value oriented fault prediction alarm according to a reasoning result; and S5, performing closed-loop optimization and continuous evolution. According to the invention, the configuration accuracy of the operation and maintenance resources is improved.

The invention provides a self-adaptive collaborative security protection method based on attack path deduction, a medium and a system, and belongs to the technical field of network security. The method comprises the following steps: sampling possible paths from a current attack node to high-value assets based on an adaptive path sampling algorithm of Monte Carlo tree search to generate an attack path graph, calculating a protection effectiveness score and a service influence score according to key nodes and predicted attack actions, and generating an optimal protection instruction set based on a multi-target tradeoff function; an instruction is converted into a heterogeneous security device configuration command through a strategy execution cooperative controller, multi-device synchronous activation is realized by adopting a distributed transaction protocol based on a logic clock, and a protection effect is monitored and fed back to an artificial intelligence model for parameter optimization; the problem of protection strategy failure caused by asynchronous network security protection response and attack evolution is solved.

The invention provides an attack detection and processing method and device, electronic equipment, a medium and a program product, and can be applied to the technical field of big data, the technical field of artificial intelligence and the field of financial science and technology. The method comprises the following steps: acquiring multi-source network security data, and constructing an entity relation graph based on the multi-source network security data; carrying out attack chain analysis on the entity relation graph to obtain an attack chain analysis result; constructing an attack graph based on the attack chain analysis result; and generating a processing strategy based on the attack graph, and executing the processing strategy in response to the fact that the processing strategy is verified to have no service influence.

Disclosed are methods, systems, and computer-readable media to perform operations including: A method for a cybersecurity risk assessment, including: acquiring information about business environment and business operations of a business entity; determining an engagement scope through facilitated sessions; performing cybersecurity control review and gap assessment to generate a control gap report and a control effectiveness report; performing cybersecurity threat analysis to generate an operational threat profile; performing a cybersecurity risk assessment to generate an operational risk profile; performing a business impact assessment to generate an operational impact profile; and providing the cybersecurity risk assessment to business sectors of the business entity.

The invention relates to the technical field of network security asset risk pricing and management, in particular to a business influence-oriented network security asset risk pricing and management method and system. Historical business data, infrastructure resource data and external environment data are integrated to construct a business-resource portrait library; a potential interruption event prediction model is trained based on a random forest or a long short-term memory (LSTM) network, a multi-target cost optimization function including resource preset cost, service interruption loss cost and resource idle penalty cost is constructed, and an optimal resource preset strategy is solved by adopting a non-dominated sorting genetic algorithm NSGA-II or a particle swarm optimization algorithm. And after execution, feedback data update models and functions form closed-loop optimization. According to the method, accurate risk prediction, dynamic cost balance and continuous strategy adaptation are realized, the service continuity is effectively guaranteed, the overall operation cost of an enterprise is reduced, and the pertinence and effectiveness of network security asset risk pricing and management are improved.

The invention provides a program code data processing method and device, electronic equipment and a storage medium. The method comprises the steps of obtaining first running data generated when a server runs a target program code of a first version; obtaining a target execution statement from the first operation data, and determining a code call chain corresponding to the target execution statement based on the first operation data; and determining code difference data of the function indicated by the code call chain based on the target program code of the second version and the target program code of the first version. In the mode, a user can clearly understand the code execution process corresponding to the preset requirement influenced by the modified function by checking the code calling chain, the efficiency of analyzing the service function influenced by code modification is improved, the code calling chain is generated based on the operation data of the server, the code calling relation is more accurate, and the user experience is improved. Therefore, the analysis accuracy of the business influence effect is improved.

The invention provides a penetration testing method and system based on multi-source data association and risk aggregation, and the method comprises the steps: firstly carrying out the automatic collection and normalization processing of heterogeneous security data from penetration testing, source code detection, vulnerability scanning and the like, and breaking an information island; and intelligent association and attack path discovery are carried out on discrete vulnerabilities based on an attack chain model, and a dynamic risk quantification model fusing a business influence weight and attack path complexity is innovatively introduced to carry out comprehensive risk assessment. According to the technical scheme, the problems of data splitting, single analysis dimension and disjunction between risk assessment and services of a traditional scheme are effectively solved, accurate identification and quantitative grading of composite attack chain risks are finally achieved, the safety analysis operation efficiency is remarkably improved, expert knowledge is solidified in the system, and the safety analysis efficiency is improved. And a visual and reliable data support is provided for safety investment decision-making of key information infrastructure industries such as power generation and the like.

The present invention relates to a method, device, storage medium and equipment for warning user risks in marketing activities, including a characteristic vector representation corresponding to user risk characteristics corresponding to multiple user risk types existing in the marketing activities, a risk type rule tree is constructed through a chi-square coefficient, and a target rule tree is formed by associating warning conditions. The user data of the marketing activities is monitored, and when the warning conditions are triggered, the virtual business logic flow is guided to avoid the impact on the real business. At the same time, the user account is locked, and a risk warning is generated according to the warning conditions of the target node and the associated node, so as to effectively identify and respond to the user risks in the marketing activities.

The invention relates to the technical field of application system evaluation, and provides an application system fault influence evaluation method and system, and the method comprises the steps: obtaining the customer complaint amount of different platforms for an application system fault, and calculating a reputation influence score in combination with the weights of different platforms; obtaining losses of different types of services when the application system fails, and calculating service influence scores by combining weights of the different types of services; obtaining influence quantities of different types of clients when the application system fails, and calculating user influence scores in combination with weights of the different types of clients; obtaining values of different types of performance indexes when the application system fails, normalizing the threshold sensitive index by using a tangent function, normalizing the progressive deterioration index by using an exponential function, and calculating a performance influence score in combination with weights of the different types of performance indexes; and performing weighted summation to obtain a total score, and determining the fault level of the application system. And the accuracy of fault influence evaluation is improved.

The invention relates to the technical field of intelligent decision making, can be applied to business system platforms of financial science and technology, medical health and the like, and discloses a double-graph fused network security regulation and control method, device and equipment and a medium, and the method comprises the following steps: constructing an abnormal behavior graph and an operation and maintenance topological graph based on multi-source heterogeneous data; performing association matching on the abnormal behavior nodes and asset nodes to generate a node mapping relation table; constructing a dynamic Bayesian network according to the node mapping relation table, and performing abnormal diffusion analysis on the associated nodes by using the dynamic Bayesian network to obtain service influence data; generating a hierarchical response strategy based on the service influence data, and obtaining service state feedback data in an execution process of the hierarchical response strategy; and fusing and rendering the abnormal behavior graph, the operation and maintenance topological graph, the service influence data and the service state feedback data into a network security situation graph, and executing network protection regulation and control operation on the target network by utilizing a regulation and control instruction. The accuracy of network security protection is improved.

A system for fully integrated collection of business impacting data, analysis of that data and generation of both analysis-driven business decisions and analysis driven simulations of alternate candidate business actions has been devised and reduced to practice. This business operating system may be used predict the outcome of enacting candidate business decisions based upon past and current business data retrieved from both within the corporation and from a plurality of external sources pre-programmed into the system. Both single parameter set and multiple parameter set analyses are supported. Risk to value estimates of candidate decisions are also calculated.

The invention relates to the technical field of data processing, in particular to a business data processing method and device, equipment and a medium, which are applied to a financial scene or a medical scene, and are used for obtaining business data of a plurality of business management systems corresponding to preset businesses and business process relationships and business process types corresponding to the preset businesses. And then classifying the business data based on the business process relationship and the business process type to obtain target business data, monitoring whether the target business data has production and consumption abnormity or not based on a preset monitoring rule, and if the target business data has the production and consumption abnormity, obtaining consumer information related to the abnormal target business data, the consumer information comprises the consumer configuration information and the consumer process identification information, so that the normal circulation of the business process is ensured, the influence of abnormity on the business is reduced, and the data processing efficiency is improved.

The invention discloses a self-healing control method and system for an industrial internet of things. The method comprises the following steps: constructing a digital twinborn model synchronous with a physical system; based on historical security data and business dependence, generating an initial self-healing rule and encoding the initial self-healing rule as an initial population of a genetic algorithm; simulating threats in digital twinning, carrying out iterative evolution on the population according to business influence evaluation through a genetic algorithm, and carrying out convergence to obtain an optimal self-healing rule; deploying the optimal rule to a field control point, and performing real-time monitoring and feedback; according to the method, safe off-line evolution is carried out in digital twinning, the optimal dynamic strategy can be automatically generated, and the automation and prospective self-healing capability of an industrial system is remarkably improved.