86 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 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 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.

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 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 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.

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 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 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.

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.

The invention provides a business influence prediction method and device for network cutover, equipment and a storage medium, and relates to the technical field of big data, and the method comprises the steps: predicting an influenced business set when a to-be-cutover network element is unavailable according to a pre-constructed business path knowledge base; wherein the service path knowledge base is used for storing a mapping relationship between service identifiers and service path information; the service path information is used for indicating a plurality of network elements through which a service flow passes; the affected service set comprises a plurality of affected services, and service flows of the affected services pass through the network element to be cut over; and in multiple dimensions, the influence of the unavailable network element to be cut over on the affected service set is quantitatively analyzed, and a multi-dimensional influence quantitative result is obtained. According to the scheme of the invention, the accurate prediction and quantitative analysis of the influence of the network element cut over can be realized before the network element cut over, so that the security and reliability of the network cut over can be improved.

The application relates to the technical field of network security and discloses an attack surface management method based on multi-source intelligence fusion, which comprises the following steps: collecting original data through a dynamic asset fingerprint library construction module and generating asset portrait data by using a machine learning algorithm; mapping the asset portrait data into a graph database node and an edge through an attack path simulation engine, executing a graph path search algorithm, outputting an attack path simulation result and a business influence score; executing a repair suggestion instruction through an automatic repair verification module, sending a re-simulation request to the attack path simulation engine, and verifying whether the attack path is blocked according to a secondary simulation result. The application can realize real-time discovery of dynamic assets and supply chain risks, accurate quantification of attack path influence by using a graph database, effective cutting of attack paths by repair measures and non-introduction of new risks through a secondary simulation and an abnormality detection mechanism, and dynamic and accurate closed-loop management of attack surface risks.

This invention relates to the field of software development technology and discloses a method, apparatus, device, and storage medium for interface iteration risk alarm. The method includes: acquiring data returned by the interface after an iterative update as comparison data, and acquiring pre-stored data returned by the interface before the iterative update as reference data; converting the comparison data and reference data into a structured data model in a preset format using a nested structure to generate a first structured data representation and a second structured data representation; performing a multi-dimensional comparison based on the first and second structured data representations to obtain a multi-dimensional comparison result; and determining whether to trigger an alarm notification based on the business impact of the multi-dimensional comparison result. Applying the technical solution of this invention can solve the problems of inefficiently handling complex data structure comparisons during interface iteration and the imperfect alarm mechanism in the prior art.

The invention discloses a multi-node rapid disaster recovery switching method and system based on AI, and the method comprises the steps: collecting disaster recovery configuration information and backup data features, and building a data dependence relation and influence range association map; a directed acyclic graph of the fault combination is constructed, and a q-learning algorithm is adopted to optimize verification time sequence arrangement; performing risk scoring according to the verification result, and generating a verification report; training a local disaster recovery efficiency evaluation model based on verification results of the multiple data centers; carrying out weighted aggregation on model parameters by using a FedAvg algorithm through a federated learning mechanism, and generating a global evaluation model; and an evaluation result is fed back to the disaster recovery system for continuous optimization. According to the method, through collaborative modeling of all the data centers, dispersed professional knowledge is integrated on the premise of protecting data privacy, the verification coverage rate is increased, the service influence is minimized, the verification accuracy is enhanced, and the technical problems that a traditional disaster recovery verification method is low in efficiency, incomplete in coverage, incapable of sharing experience and the like are effectively solved.

The application discloses a cross-platform session state reconstruction method and system based on event trace source, relates to the field of cross-platform customer service integration, and comprises the following steps: introducing a measurable business impact weight for cross-platform field events in a business semantic-based event formatting mode, and maintaining cross-platform event order in combination with a vector clock; through time sequence cumulative calculation of the event weight, a state summary of a truly key event in business significance is adaptively selected to generate, instead of taking a snapshot based on fixed time or a fixed number, so that the data transmission amount and the calculation overhead required for state reconstruction are significantly reduced; in the client recovery stage, only the summary and the incremental event after the summary need to be transmitted, so that the client can still quickly reconstruct the state at a low cost in a weak network and low bandwidth environment. Therefore, the differentiated compression and recovery of the cross-platform event trace source semantic level are realized, and the session reconstruction efficiency and resource utilization efficiency in a large-scale multi-platform scenario of customer service are greatly improved.

The application relates to the fields of data processing and intelligent prediction, in particular to a quality prediction system and method based on multi-dimensional feature coupling driving, which comprises a business logic solidification module, a real-time working condition comparison module, a risk trend extrapolation module and a final risk grading module; the business logic solidification module is used for refining and solidifying business influence logic and generating static risk factors; the real-time working condition comparison module is used for comparing 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 forward-looking risk track; and the final risk grading module is used for integrating the static risk factors and the forward-looking risk track, grading and outputting a final quality risk evaluation value. The application solves the problem that traditional methods are difficult to integrate multi-dimensional information by deeply coupling the static inherent risk of a business process with the dynamic evolution trend of real-time data, quantifies the global correlation effect of the current deviation in the business process, and improves the accuracy and interpretability of quality risk prediction.

Embodiments of the present application relate to the field of photovoltaic technology, and disclose a photovoltaic monitoring system abnormal test data generation method, device, equipment and medium, the method comprises the following steps: constructing a GAN model, constructing a fusion framework of a knowledge graph and a probabilistic graph model, including constructing an ontology model in the field of photovoltaic, and establishing a probabilistic graph model according to the constraint relationship in the ontology model, iteratively training the GAN model to convergence, and then outputting a preset number of evaluation test samples of a predetermined photovoltaic fault type, inputting the preset number of evaluation test samples into the probabilistic graph model, and outputting a multidimensional influence probability corresponding to each evaluation test sample, obtaining a business impact degree corresponding to the evaluation test sample according to the multidimensional influence probability, and obtaining test data by screening the evaluation test sample according to the business impact degree. The test data generated by the embodiments of the present application can be widely applied to the test and verification of data cleaning, fault tolerance processing, fault detection and other functions of the photovoltaic monitoring system.

The application relates to a fault prediction method and system based on service influence driven parameter fine tuning and adaptive structure pruning, and belongs to the technical fields of artificial intelligence, time series data analysis and intelligent operation and maintenance. The application comprises the following steps: S1: service influence data modeling: integrating multi-source operation and maintenance data and historical service fault event data, and constructing a service influence quantification model; S2: service influence guided parameter efficient fine tuning: loading a pre-trained fault prediction model, and performing parameter efficient fine tuning on the model based on a service influence signal; S3: service influence driven adaptive structure pruning; S4: dynamic reasoning and alarm generation: realizing a dynamic reasoning mechanism of the pruned fault prediction model, and generating a structured and service value oriented fault prediction alarm according to a reasoning result; and S5: closed loop optimization and continuous evolution. The application improves the configuration accuracy of operation and maintenance resources.

The present application provides an authentication information transmission method, which is applied to an authentication information sending module in a terminal, including: obtaining authentication information of a data packet to be sent in a TCP long connection; wherein the TCP long connection is a TCP long connection established between a client in the terminal and a server; constructing an authentication data packet; sending the authentication data packet to an authentication information receiving module of the server, so that the authentication information receiving module can confirm the authentication information of the data packet received by the server. In the present application, the communication security of the business system can be improved without updating communication equipment and communication protocols, reducing the communication upgrade cost of the business system, and having no additional business impact on the business system. The present application also provides an authentication information transmission system, a computer-readable storage medium, and an electronic device, which have the above-mentioned beneficial effects.

The invention relates to a method and equipment for tracing and repairing water conservancy data quality abnormity, and a medium, and belongs to the technical field of intelligent water conservancy. Collecting multi-source water conservancy data, extracting data features and generating a standardized feature vector set; constructing a three-layer Bayesian network topological structure of circulation link-business influence factor-anomaly type, training to obtain a dynamic weight Bayesian network model adaptive to the water conservancy scene, inputting the standardized feature vector into the dynamic weight Bayesian network model obtained by training, outputting quality anomaly root cause probability distribution, and obtaining the quality anomaly root cause probability distribution. The posterior probability of each abnormal root cause is calculated through a Bayesian formula, and a root cause output traceability result is judged; performing graded repair and verification according to different traceability results; and the repaired qualified data is synchronized to a data asset module of the water conservancy credible data space for service output and generation of a visual data quality report. According to the method, the traceability precision is remarkably improved, and the service adaptability is higher.

The invention relates to the technical field of data storage, in particular to a large-scale data storage optimization method and system based on artificial intelligence. The method comprises the following steps: acquiring large-scale data, and performing multi-dimensional feature analysis and business influence evaluation based on artificial intelligence to obtain a business influence degree corresponding to each piece of sub-data; access frequencies and access time intervals corresponding to all the sub-data are obtained, importance level classification evaluation and storage device hierarchical mapping are carried out, and corresponding structured data and unstructured data in the large-scale data are stored in corresponding storage devices in a layout optimization mode; according to the method, the disk I / O rate and the storage utilization efficiency corresponding to the storage device are obtained, dynamic storage optimization adjustment is carried out, a large-scale data device dynamic storage strategy is generated, corresponding device dynamic storage layout adjustment work is executed, and the corresponding storage access speed is increased. The efficiency, reliability and space utilization rate of large-scale data storage can be improved.