165 results about "Business context" patented technology

The invention relates to the technical field of data analysis, in particular to an operation decision intelligent analysis method and system based on cross-domain data fusion. The method comprises the following steps: firstly, based on an enterprise multi-domain ontology knowledge base, performing entity identification and relation mapping on heterogeneous data from different business systems through a semantic mapping-based multi-source heterogeneous data dynamic fusion algorithm, and establishing a unified data model; then, a causal reasoning and deep learning fused hybrid intelligent decision engine is adopted to analyze and process the model; then, a multi-level causal relationship network among business variables is constructed through a causal relationship discovery algorithm by utilizing an analysis result of the hybrid intelligent decision engine, and an adaptive business scene analysis model based on reinforcement learning is used to dynamically adjust an analysis strategy according to business environment changes; generating a Pareto optimal decision scheme set through a multi-objective optimization algorithm, and outputting operation decision suggestions; according to the invention, the comprehensiveness and accuracy of intelligent analysis of enterprise operation decisions are improved.

The invention provides a low-code rule engine method and platform supporting dynamic service adjustment, and relates to the technical field of data processing.The method comprises the steps that original rule data are obtained, and the original rule data are subjected to structured conversion and unified format specification; generating a visual rule component, presenting rule logic through a graphical interface, supporting business personnel to edit and modify without coding, and converting a modification result into component rule data; performing real-time analysis and verification on the component rule data to obtain operation rule data; performing version identification on the operation rule data, and storing the operation rule data in a rule warehouse; based on the current service context, matching an applicable version, automatically selecting the operation rule data, and loading the operation rule data to a rule execution engine for execution; the autonomy and accuracy of the low-code rule engine are improved.

The invention relates to a multi-view knowledge graph-driven industrial chain collaborative recommendation method, which comprises the following steps of: constructing knowledge graph views of a plurality of business dimensions through multi-source heterogeneous data, and performing feature decomposition and adaptive feature regulation and control; a multi-head knowledge propagation mechanism fusing a business context is provided, and hierarchical volume accumulation and dynamic path selection are carried out in combination with multi-view representation; through wavelet transform and multi-scale time sequence decomposition, constructing dynamic representation of a business entity in combination with an adaptive scale selection mechanism and an attention mechanism driven by industry characteristics; static and dynamic features are fused by using a gating network, a multi-party matching scoring mechanism based on comprehensive representation is constructed, and multi-party business collaboration and recommendation in an industrial chain are supported. By fusing multi-view knowledge graph construction, dynamic business entity modeling and comparative learning technologies, relevance between business of enterprises is disclosed, business recommendation is generated for related enterprises, and a foundation is laid for business resource allocation in an industrial chain.

A computerized system for dynamic cybersecurity policy using AI-based contextual adaptive learning includes an AI system that evaluates business contexts, risk tolerance, and productivity impact to generate threat intelligence assessments. The system includes a Contextual Adaptive Learning module that dynamically adjusts cybersecurity policies based on threat assessments to create security workflows. A Cybersecurity Mesh Development module that integrates policies across security frameworks. A Dynamic Scenario Catalog module that updates policy adjustments based on threat intelligence. An Automated Workflow Orchestration module that creates and refines security workflows for optimal efficiency. A Policy Recommendation and Automation module that generates prioritized security recommendations and automates policy changes based on organizational risk profiles and current security controls. This system harmonizes security policies while considering business context, risk, and productivity impacts.

The invention discloses a natural language database query method and system based on large-model two-channel context enhancement, and the method comprises the steps: carrying out the parallel execution of a business link and a mode link on a query statement, and respectively outputting business context information and mode link information; carrying out vectorization fusion on the two types of information to generate combined context information; generating a query intention vector based on the combined context information, and outputting a complexity label of the query statement through a complexity calculation model; and generating a prompt template according to complexity label differentiation, inputting a large model to generate an SQL statement, and executing query. Through a double-channel cooperation mechanism, the problems of semantic ambiguity, database structure complexity, poor field adaptability and the like in natural language query are solved, query accuracy and response efficiency are improved, and the method is suitable for multi-industry complex database scenes.

The invention discloses a radiation safety management method and system based on cloud platform data driving, and relates to the technical field of cloud platform radiation management. The method comprises the following steps: collecting radiation field data and environment state data of a target area through a deployed intelligent sensing node; uploading the radiation field data, the environment state data and the context data from the service module to a cloud platform, performing energy compensation and radiation unmixing, and constructing a multi-dimensional feature vector; and processing the multi-dimensional feature vector by using a pre-trained situational radiation perception model, identifying radiation field features and an environment situation, and generating a situational radiation safety early warning signal. The technical problem that in the prior art, radiation safety monitoring depends on single-point measurement, comprehensive judgment cannot be carried out in combination with environment and service context data, and consequently the radiation field anomaly recognition capability is insufficient is solved, and the purpose that the radiation field anomaly recognition capability is improved through cloud platform data driving and context awareness model fusion is achieved. And the technical effects of high-precision identification and situational safety early warning of the radiation field state are realized.

The invention discloses an event-driven business agent dynamic construction method and equipment, belongs to the technical field of intelligent application, and is used for solving the technical problems that the intelligent transformation cost of a traditional business system is high, and a manually constructed business agent is logically solidified and cannot quickly cope with business requirements and environment changes. The method comprises the following steps: monitoring a service event occurring in a service system, obtaining corresponding service event information and extracting upstream service context information corresponding to the service event; identifying a downstream service of the service event in the service system according to the service event and the upstream service context information, and acquiring service information of the downstream service; based on the business event information and the business information of the downstream business, constructing a business agent for executing the downstream business action, and storing the business agent to a business agent center; and when the new business event is monitored, a corresponding business agent is matched in the business agent center, so that the business agent executes a downstream business action triggered by the business event.

The invention provides a full-dimension intelligent perception type firewall system, and the system comprises a two-factor authentication module which is used for generating a digital identity certificate through living body detection, micro-expression analysis and dynamic token verification; the AI strategy management module is used for verifying the authority level of the digital identity credential and generating an access control rule with a digital signature according to the authority level; the traffic perception and analysis module is used for configuring traffic acquisition parameters according to the access control rule and generating an abnormal traffic report of the traffic acquisition parameters; the attack intention recognition module is used for generating a forbidding instruction containing a source IP, a target IP and a forbidding level based on the abnormal traffic report and in combination with service context information; and the infinite address governance module is used for performing duplicate checking comparison on the IP address in the forbidding instruction and the IP address in the intranet address library, and marking the compared risk address, so that the network attack can be accurately and effectively intercepted.

The invention provides a joint retrieval method and system based on knowledge enhancement, a medium and a terminal, and ensures that a retrieval result is not limited to text segments with similar literal surfaces through a structured knowledge base, a domain label knowledge base and a multi-source heterogeneous knowledge graph. And instead, an organic knowledge whole integrating direct clauses, related law articles, judgment key items and internal rules is formed, so that the generated reply has a rigorous legal basis and a complete business context, and claim settlement disputes and legal risks caused by lack of key knowledge association are reduced. And the compliance control is changed from lagging manual inspection to real-time beforehand system built-in, so that the compliance vacuum period caused by policy update lagging is reduced, and the supervision punishment and reputation risk of an enterprise is reduced. The historical versions are reserved, and the version replacement chain is established, so that explanation based on historical contexts is provided for insurance policy life cycle query crossing different time points, and the credibility and accuracy of customer services are enhanced.

The invention discloses a business process control method and system based on a decision engine, and relates to the technical field of business process control. The method comprises the following steps: collecting environmental change data in a business process in real time, classifying and filtering the data, and dividing the data into a significant change group and a tiny fluctuation group to obtain an environmental change core set; strategy matching analysis is carried out on the significant change group, and an adaptive decision adjustment scheme set is determined; then, the execution efficiency and stability of all the schemes are evaluated through historical execution records, comprehensive sorting is carried out, and candidate decision combinations are determined; and finally, evaluating the applicability of the candidate scheme in combination with environmental characteristics. According to the method, quick response to environment change is realized, through historical data analysis and multi-dimensional evaluation, the decision making process is optimized, the decision making accuracy and execution efficiency are improved, the applicability of a complex and changeable business environment is improved, and dynamic optimization and continuous improvement of decision making are realized.

Data retrieval via secure database query generation is disclosed, including: receiving, via a user interface, a user submitted request for data associated with a business context; generating a prompt to an initialized database query generation model specific to the business context based at least in part on the user submitted request; providing the prompt to the initialized database query generation model; determining a database query based at least in part on an output from the initialized database query generation model; and querying a database for matching data using the database query, wherein the matching data comprises a set of data values fetched from one or more tables of data.

The invention relates to the technical field of electronic data digital processing, and discloses a data maintenance management method and system based on artificial intelligence. The method comprises the following steps: acquiring multi-dimensional features such as an access time sequence, metadata, an association relationship and a service context of data; constructing a hybrid model fusing a long-short-term memory network and a graph attention network, and predicting a future value score of the data; dynamically generating a hierarchical storage, archiving or deleting strategy based on the score and storage cost-performance model; and through the access feedback after the execution of the monitoring strategy, the prediction model is optimized in a closed-loop manner by using a reinforcement learning mechanism. The system comprises a data feature acquisition module, a value prediction module, a strategy generation module and an execution feedback module. According to the method, self-adaptive, refined and intelligent management of the full life cycle of the data is realized, the utilization efficiency of storage resources is remarkably improved, and the operation and maintenance cost is reduced.

The invention provides a level protection-oriented network security configuration checking method, which comprises the following steps of: constructing a knowledge graph associated with a security control item and a technical implementation mode by relying on a standard document and configuration data, and implementing a control item logic dependency and semantic weight reconstruction model; security configuration of assets is automatically collected and semantically mapped to map nodes, configuration deviation is recognized, a risk influence path template is matched in combination with asset importance and service context, semantic propagation path influence intensity is dynamically calculated, and an accumulated risk influence value of configuration deviation is generated in an aggregation mode; according to the method, risk level judgment is carried out by adopting a piecewise nonlinear function, a structured risk assessment record is output, risk identification and reinforcement suggestion closed loop are realized, and the hierarchical management and control and automatic compliance response capability of security configuration deviation in a network environment is improved.

The invention discloses an exception alarm processing method and device based on a user journey view and a storage medium, relates to the technical field of distributed system monitoring and operation and maintenance, and discloses an exception alarm processing method based on the user journey view, and the method comprises the steps: obtaining an exception message generated by interface calling when an exception of interface calling is detected; querying pre-configured user journey configuration based on an abnormal interface in the abnormal message, and obtaining a user journey node corresponding to the abnormal interface so as to complement the service context of the abnormal message; and based on the complemented abnormal message, matching a preset alarm rule to generate an alarm notification. According to the method, the interface exception is associated with the user journey node, so that deep fusion of the exception alarm and the service scene is realized, the alarm information has a clear service context, the problem that the traditional alarm information is isolated and lacks a service view angle is solved, and the accuracy of exception positioning and the operation and maintenance response efficiency are remarkably improved.

The invention discloses an AI-based private network abnormal risk traffic multi-modal detection method and system, relates to the technical field of network security, and is used for solving the problem that compound attacks which are high in concealment and have service context association are difficult to effectively identify in a private network. The method comprises the following steps: firstly, injecting business semantics for flow analysis through multi-modal data acquisition and business intention marking; then, by adopting a mode of combining session boundary stability analysis and protocol compliance verification, detecting anomalies from two dimensions of microscopic protocol operation and macroscopic behavior trend respectively, and generating risk events of different levels; finally, an attack chain judgment mechanism is constructed through time sequence correlation analysis, a front-end detection strategy is dynamically optimized based on a judgment result, a closed-loop detection system from data collection to threat response is formed, and the detection accuracy of advanced threats in the private network environment and the self-adaptive capacity of the system are effectively improved.

The invention provides an automatic planning integrated service system based on artificial intelligence. The automatic planning integrated service system based on artificial intelligence comprises a multi-source heterogeneous data access and processing module used for preprocessing multi-source heterogeneous data from a sensor, a historical business log and a third-party API in real time; the data unified modeling and storage module is used for carrying out semantic modeling storage on the multi-source heterogeneous data through a knowledge representation model; and the intelligent planning and scheduling module is used for reasoning and optimizing the timeliness, the cost and the resource utilization rate. According to the automatic planning integrated service system based on artificial intelligence, a decision strategy can be continuously refined, and it is ensured that an optimal or near-optimal scheduling scheme is always provided in a complex and changeable business environment, so that the overall operation efficiency and the resource utilization level are remarkably improved.

Robust systems and methods are disclosed for fake account detection on digital platforms, integrating provenance analysis to scrutinize data origins, ownership, and history, thereby unveiling potential sources of fraudulent activities. They leverage dynamic feature generation, using advanced algorithms to assess user behaviors and interactions, ensuring the model stays attuned to the evolving landscape of cyber threats. Incorporating Quantum-assisted optimization, the method employs Quantum algorithms to expedite feature selection, enhancing detection efficiency. Quantum simulation further refines this process, creating sophisticated verification patterns and analytical techniques to distinguish genuine from fake accounts with higher accuracy. A comprehensive analysis amalgamates provenance data, telemetry, and dynamic features, forming a holistic detection approach. This system optimizes features through Quantum simulation, tailoring them to specific business environments, and deploys them via AI-ML DevOps, streamlining orchestration across various operational settings.

The invention belongs to the field of artificial intelligence, and relates to an operation authority regulation and control method and device, equipment and a medium, and the method comprises the steps: obtaining the operation behavior data, spatial-temporal feature data and service context data of a user terminal when the target operation of the user terminal on a target system is detected; processing the obtained data to obtain an operation sequence data set; performing anomaly recognition on the operation sequence data set through a long-short-term memory network to obtain an operation sequence anomaly recognition result; calculating an operation path deviation degree of the user terminal through a graph neural network based on the operation sequence data set and the standard operation and maintenance knowledge graph; generating a dynamic risk scoring result based on the operation sequence anomaly recognition result and the operation path deviation degree; and regulating and controlling the operation authority of the user terminal based on the dynamic risk scoring result and the hierarchical authority control strategy. The method can be applied to the business field of financial insurance and the like, and the accuracy and real-time performance of safety operation and maintenance of the target system can be improved.

The invention provides a non-perceptual data dynamic migration method which comprises the following steps: acquiring a difference between a source database and a target database, and making a data lossless migration plan; comparing the data difference between the source database and the target database, judging the data volume needing to be synchronized each time, and predicting the required network bandwidth resources; estimating transmission time sequences and transmission paths of different data blocks by using a Kalman filtering algorithm according to importance and priority of an application system and data; continuously monitoring data synchronization quality and application system performance indexes in a data migration task execution process, and judging a smooth transition effect; after core service data migration is finished, paused non-core services are recovered step by step until migration and synchronization of all service data of the standby data center are finished; a gray release mode of gradually expanding the weight is adopted, the service is smoothly transited to a data center, and a one-key automatic full-service environment rollback scheme is established to prevent abnormity.

The invention relates to the technical field of automatic driving, in particular to an open type automatic driving safety management system and method supporting multi-benefit-party strategy arbitration, and the system comprises an open type safety strategy interface module which is used for defining a standardized strategy description specification; the strategy plug-in management module is used for receiving and loading the strategy plug-ins conforming to the strategy description specifications; the perception and fusion module is used for collecting the vehicle state data, the environment perception data and the service context data and fusing the data to generate a unified context model; the multi-strategy evaluation module is used for screening a candidate strategy set according to a unified context model and a trigger condition when an abnormal event is detected, and evaluating and selecting an optimal strategy; and the strategy execution and monitoring module is used for executing the action template corresponding to the optimal strategy. According to the invention, the automatic driving vehicle can select adaptive degradation strategies according to different operation scenes and business requirements, and the adaptability of the system to diversified business scenes is improved.

The invention relates to the technical field of information retrieval, in particular to an enterprise knowledge base natural language query method based on the RAG technology, and the method comprises the steps: constructing the industry term adaptation degree of a query text during each query based on the double correlation of the query text of each query and the content of an original corpus in business accuracy and intention understanding; each cluster is obtained by clustering the industry term adaptation degree of the historical query text, elements in each cluster are analyzed through a sliding window, and the term offset degree of the historical query text before current query is calculated, so that the adjusted triggering frequency is determined, the field term library updating frequency is adjusted, and the field term library updating efficiency is improved. Constructing a retrieval enhancement generation model through the updated domain term library, and carrying out natural language query; vector representation of specific terms of an enterprise can be ensured to be continuously calibrated along with the evolution of a business context, and the accuracy of a result of performing natural language query on an enterprise knowledge base is improved.

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 is suitable for the technical field of computers, and provides an integrated code generation method which comprises the steps that on a low-code platform, a seed code is generated based on a data model, and the seed code comprises service metadata capable of being recognized by a development environment; analyzing a data model and a business rule on which the seed code is based, and constructing a multi-dimensional meta-knowledge graph; in a code editor, analyzing the business metadata to generate a business context; deducing a service constraint condition from the service context, and converting the service constraint condition into a code generation constraint; on the basis of the code generation constraints, code snippets are generated through an AI programming assistant in combination with the seed codes, and therefore semantic matching is carried out on data relations, service rules and the like in services and constraint conditions needed by code generation through integration of low-platform codes, a code editor and the AI programming assistant, deep semantic understanding is achieved, and the service quality is improved. And generating a corresponding code snippet.

The invention discloses a microservice elastic scaling decision-making method and device based on time sequence data driving, and the method comprises the steps: obtaining operation index data of a plurality of microservices, call chain data among the plurality of microservices, and business context data; based on the operation index data of the target micro-service, determining the operation state of the target micro-service in the plurality of first target time windows; constructing a state sequence of the target micro-service based on the operation state; analyzing a state change feature of the state sequence, and if the state change feature meets a preset requirement, determining an operation scene of the target micro-service; determining an adjustment strategy template of the target micro-service based on the operation scene and a preset strategy template library; analyzing the change trend of the operation index to obtain a change trend label; determining an adjustment parameter of the adjustment strategy template; and generating an adjustment instruction according to the strategy template and the adjustment parameter, and sending the adjustment instruction to the micro-service control platform, so that the micro-service control platform executes the adjustment instruction. The application can improve the utilization rate of network resources.

Robust systems and methods are disclosed for fake account detection on digital platforms, integrating provenance analysis to scrutinize data origins, ownership, and history, thereby unveiling potential sources of fraudulent activities. They leverage dynamic feature generation, using advanced algorithms to assess user behaviors and interactions, ensuring the model stays attuned to the evolving landscape of cyber threats. Incorporating Quantum-assisted optimization, the method employs Quantum algorithms to expedite feature selection, enhancing detection efficiency. Quantum simulation further refines this process, creating sophisticated verification patterns and analytical techniques to distinguish genuine from fake accounts with higher accuracy. A comprehensive analysis amalgamates provenance data, telemetry, and dynamic features, forming a holistic detection approach. This system optimizes features through Quantum simulation, tailoring them to specific business environments, and deploys them via AI-ML DevOps, streamlining orchestration across various operational settings.

The invention relates to a financial risk control dynamic decision-making method, system and device and a medium. The method comprises the following steps: acquiring multi-source real-time risk characteristics and business environment indexes during customer transaction, and constructing an initial node structure associated with the characteristics through a graph neural network; effective connection is screened based on connection strength, and a dynamic routing scheme is generated by combining a high-risk scene optimization path priority; and utilizing reinforcement learning to iterate abnormal data flow analysis, and updating decision network configuration. The method comprises the following steps: extracting service demand indexes from configuration, generating an initial priority sequence through a graph neural network, harmonizing into a multi-target sequence by combining historical balance data, propagating risk characteristics in sequence to generate decision vectors, and verifying a matching degree to form a dynamic decision path. By adopting the method, the problem of insufficient scene identification of a traditional static model can be solved, the decision real-time performance is improved, the risk interception and business efficiency are balanced, a full-process dynamic risk control system is constructed, and efficient and adaptive risk decision support is provided for financial business.

The application relates to an observable system based on service context enhancement, which is built based on an observable architecture and comprises a data acquisition layer, a data processing layer, a data storage layer and a data display layer. The data acquisition layer is used for acquiring metadata of service requirements and technical requirements, and generating technical monitoring data under a service context. The data processing layer is used for associating enhanced data including external service data and software life cycle data with the technical monitoring data under the service context to generate derivative data. The data processing layer is also used for performing semantic analysis on the derivative data to generate pragmatic data, wherein the pragmatic data includes service level operation steps in a natural language form and service optimization suggestions. The data storage layer is used for storing the derivative data and the pragmatic data. The data display layer is used for displaying the derivative data and the pragmatic data. Compared with the prior art, the application can realize deep integration and comprehensive observability of technical data and service processes, and reduce communication barriers between technical teams and service teams.

The present application relates to the technical field of log analysis, and particularly relates to a kind of audit system operation and maintenance log automatic generation method and equipment.The method parses log stream to distinguish business log entries from unassociated abnormal entries, constructs thread execution sequence and calculates the idle time slice between adjacent business log entries in thread execution sequence;Load the resource constraint mapping table defining hardware resource type and minimum duration;Get the candidate business entries of unassociated abnormal entries, and perform mutual exclusivity and capacity physical verification using the resource constraint mapping table to filter out effective candidate business entries;According to the time difference between unassociated abnormal entries and its effective candidate business entries, filter out the best matching business entries, and then backtrack the business context, inject the exception stack and correct the state to generate structured audit records.The present application realizes non-invasive and high-precision exception attribution based on the rules of runtime resource constraints, and improves the integrity and accuracy of audit data.

This invention provides a self-healing scheduling method for RPA clusters based on microtask state reconstruction, relating to the field of mobile terminal RPA scheduling technology. The method includes deploying a daemon process on the mobile terminal side and establishing a direct communication link with the scheduling center; the scheduling center detects the running status of the mobile terminal application using command probes and image probes, calculates the soft fault fusion probability based on weighted fusion, and triggers self-healing when the probability exceeds a threshold; the business process is decomposed into microtasks with dependencies and a dependency graph is constructed; during execution, the execution state and business context are synchronously stored in the central storage; after a soft fault occurs, the breakpoint is determined and a backup mobile terminal is selected from the session pool to take over; the backup mobile terminal obtains the breakpoint task data and reconstructs the execution environment; before injecting business data, an environment consistency check is performed; if inconsistency occurs, a compensation operation is performed before continuing to execute subsequent microtasks, thereby improving the success rate and execution stability in cluster scenarios.

The invention discloses a large model question-answering system implementation method based on enterprise knowledge and a business system, which comprises the following steps of: performing business intention analysis on a natural language question input by a user to generate business context information; based on the service context information, performing multi-source retrieval in a pre-constructed enterprise knowledge base and a butted service system to obtain a plurality of knowledge fragments; based on the natural language problem, the knowledge fragment and the service context information, according to the entity relationship in the enterprise knowledge base, fusing to generate a structured evidence chain; and providing the evidence chain and a preset business rule to the large language model as constraint conditions, constraining the reasoning process, and generating a final answer consistent with the evidence. Relates to the technical field of enterprise intelligent question answering and knowledge management. The method avoids the defects that output content contains factual errors, outdated information or imaginary conclusions and the like. Efficient retrieval and credible generation of enterprise knowledge are realized, and the accuracy, interpretability and business closed-loop capability of the question-answering system are improved.