138 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.

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 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.

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 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 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.

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.

The invention relates to a natural language to SQL query method and system based on a large model, and relates to the technical field of artificial intelligence and business intelligence. The method comprises the following steps: constructing a vectorization knowledge base containing a business context; based on the vectorization knowledge base, receiving and analyzing a natural language problem of the user, generating and executing an SQL query, and returning a query result to the user; based on the query result, generating a parameterized index SQL template from the query result, and storing the parameterized index SQL template in an index template library; and providing a configuration interface for a user to create a report template, binding at least one index SQL template from the index template library in a paragraph of the report template, and configuring a copywriting generation rule for each paragraph. The method has the effect of solving the problem that a large model is understood insufficiently in a complex service scene as much as possible.

The invention discloses an airport multi-level screen intelligent grading information display method and system, and belongs to the technical field of data processing and business management, and the method comprises the steps: obtaining static attribute information, real-time state information and business context information of each screen in an airport, and constructing a corresponding digital twinborn model; obtaining a real-time service data flow to dynamically group the digital twinborn model, and generating a dynamic virtual grouping strategy; equipment operation parameter data streams and playing picture data streams of all screens are collected, fusion and correlation analysis are carried out on the equipment operation parameter data streams and the playing picture data streams and real-time service data streams, a same control instruction set is generated and issued to a target screen control terminal in combination with a dynamic virtual grouping strategy, and information display control is executed. By means of constructing a screen digital twinborn model, performing dynamic grouping and priority analysis based on real-time service data flow and generating a cooperative control instruction set, dynamic and accurate display of airport information release, intelligent sensing and self-healing recovery of faults and continuous optimization of system decision can be realized.

This invention relates to the field of data analysis technology, and in particular to a query analysis method, apparatus, device, and storage medium based on a large language model. The method provides comprehensive and effective data support for enterprise decision-making. It achieves semantic fusion of multi-source heterogeneous data through a unified knowledge graph, breaking the limitations of traditional data silos and providing a comprehensive and accurate business knowledge foundation for the large language model, reducing model illusions from the source. It achieves differentiated analysis processing through task type classification, automatically scheduling different capability modules of the large language model according to user needs, balancing the efficiency of basic queries with the accuracy of in-depth analysis. It obtains the full business context through entity links, limiting the reasoning process of the large language model to the enterprise's real business scenarios, significantly improving the business relevance of the analysis results. It achieves in-depth diagnostic analysis through causal attribution, fully leveraging the logical reasoning capabilities of the large language model and overcoming the limitations of descriptive analysis.

The application discloses a test case generation method and system based on log traceability and interface call chain, belongs to the cross technical field of software test automation and log intelligent analysis, and comprises the following steps: step 1, collecting multi-source running logs of full-link components of a target system; step 2, pre-processing the multi-source running logs to form a full-link log set by correlating the logs with TraceID as an index; step 3, reconstructing a cross-service API call chain and identifying hidden call relations based on the full-link log set and a graph theory algorithm; and step 4, constructing an API dynamic parameter model based on the full-link log set and extracting parameter constraint rules. The application is different from the passive recording and troubleshooting purposes of logs, actively mines the interface call rules, parameter characteristics and business context in the logs, constructs a reusable API knowledge graph, and realizes the essential upgrading of the value of logs.

The application belongs to the technical field of informatization and industrial data integration of nuclear power plants, and provides a maintenance procedure and information automatic association method and system based on equipment coding, the method comprising a receiving request step, a concurrent query step, a data aggregation step and an active push step, and the system comprising an equipment coding unified service module, a data interface adapter group module, an information aggregation and association engine and an active information service module, the application taking equipment coding as core master data, automatically aggregating maintenance related multi-source heterogeneous information through a service architecture and intelligent association technology, and actively pushing in a business context to form a method and system of an equipment holographic view.

The invention relates to the field of information security and block chains, and discloses an agent identity information automatic detection and verification method and system. The method comprises the following steps: S1, receiving a verification request containing a decentralized identity identifier of a broker and a service context; s2, carrying out cross-chain aggregation on static and dynamic attributes on a plurality of parallel chains; s3, generating a self-adaptive challenge according to the aggregation attribute and the service context; s4, receiving a dynamic presence proof containing a response and a signature; and S5, aggregating and verifying the proof, and judging whether the verification is passed or not. The system comprises a request receiving module, a state aggregation module, a challenge generation module, a proof receiving module and an aggregation verification module. Through cross-chain aggregation of multi-dimensional dynamic attributes and combination of dynamic on-site certification, automatic detection and verification of agent identity information are more accurate and safer, and the risk of identity fraud can be effectively prevented.

This invention discloses a business model orchestration and collaborative execution method for diagnosing complex business logic. The method comprises six parts: First, business model abstraction and layered construction, including component-based business modeling, domain-driven design-based business context partitioning, and aggregation of related services. Second, intelligent orchestration engine design, including dynamic process assembly, execution strategy configuration, and diagnostic probe implantation. Third, a distributed collaborative execution framework, including event-driven architecture, distributed transaction control, and resource scheduling optimization. Fourth, real-time diagnosis and root cause analysis, including multi-dimensional monitoring, dynamic topology analysis, intelligent diagnostic engine, and root cause localization. Fifth, collaborative governance and optimization closed loop, including knowledge accumulation, automatic repair, and process optimization suggestions. Sixth, collaborative task execution, allocating resources according to business type and collaboratively executing concurrent tasks. Finally, the results of business model orchestration and collaborative execution are output. This invention achieves efficient collaborative execution and automatically diagnoses problems during the execution process.

The invention provides a metadata-driven intelligent log analysis system and method, and the system comprises a dialogue interaction module which is used for extracting a technical problem proposed by a user; the keyword extraction and query module is used for carrying out keyword extraction, service attribute labeling and query on the technical problem based on the service metadata pre-identifier; the query statement dynamic construction module dynamically constructs a query statement and performs log data query; the time conversion module performs context conversion on the keyword query result and the log data query result based on a time sequence, and outputs a final log query result; and the hierarchical structured RAG module performs context structured processing on the query result and the technical problem, and outputs a final solution through large model analysis. According to the method, natural language question input is supported, the use threshold is remarkably reduced, accurate retrieval of operation and maintenance data and business context enhancement are achieved through collaborative association of keywords and metadata, and the accuracy of question analysis is improved.

Disclosed in the present application is an application management method. The method comprises: an interaction apparatus receiving a user query; an agent routing and orchestration apparatus identifying, on the basis of the query, an agent requested by a user in the query, and when no agent is requested in the query, retrieving a target agent matching the query, assigning tasks to the target agent, and determining an execution order or a dependency relationship of the tasks; the agent routing and orchestration apparatus generating a standard operating procedure (SOP) by means of a language model on the basis of the execution order or dependency relationship of the tasks and the target agent; and an agent execution apparatus loading the target agent on the basis of the SOP, and executing an operation node in the target agent to obtain a response. By means of the method, intent recognition can be performed on a user query, a target agent matching the query is retrieved, tasks are assigned to the target agent, and static or dynamic SOPs can be generated by means of task orchestration, thereby meeting diverse business requirements and complex business environments, and thus the method has higher flexibility and adaptability.