35 results about "Business context" patented technology

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.

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 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.

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.

This application relates to the technical field of information security, and discloses a simulated attack verification method based on a security intelligent agent, including steps such as judgment result access and context enrichment, security intelligent agent PoC harmless verification, dynamic policy orchestration and human-machine collaborative judgment, automated execution and policy distribution, closed-loop feedback and model self-evolution. This application achieves intelligent screening of PoC scripts through vector matching, completing non-destructive risk verification in an isolated sandbox; it constructs a policy weight model based on asset attributes and business context to achieve dynamic policy orchestration and conflict resolution, clarifying the boundaries of human-machine collaborative handling; and it establishes end-to-end data feedback to achieve self-optimization of the risk assessment model. This method accurately distinguishes between real and theoretical risks, reduces the probability of mishandling, ensures the continuity of core government cloud services, achieves continuous self-evolution of the security protection system, and adapts to the stringent requirements of government cloud security protection.

This invention discloses a key data binding method and related equipment based on hierarchical locks and blockchain. The invention employs a hierarchical lock mechanism combining serial number segment pre-ownership and dynamic leases. Workstation clients apply for batch serial number segment locks and periodically renew them. After successfully holding a lock, multi-source data is pulled from the upstream system through a unified data adaptation layer. After local pre-verification, the serial number is written, generating the original data packet. The data packet is encrypted and uploaded to IPFS to obtain a hash address. This hash address, along with a unique fingerprint, lock credential, and business context, forms a notarized transaction and is sent to the blockchain. A finite state machine smart contract verifies the lock's validity, field uniqueness, and state transitions. Upon successful verification, the transaction is notarized on the blockchain. Finally, the lock is released, returning the serial number segment. This forms a complete closed loop from lock application, data pre-aggregation, serial number writing, hierarchical notarization, to lock release, establishing an end-to-end trusted production record for the equipment.

This invention proposes a customer attitude recognition and operational intervention method based on AI semantic analysis, belonging to the field of data processing technology. The method includes: acquiring real-time voice streams of customer interactions and multi-source heterogeneous business context data, and generating interaction feature vectors and business context data sets respectively; performing a feature-level affine transformation on the current interaction feature vector based on the business context vector of the previous moment to generate a modulated feature vector; using a gated recurrent unit to perform temporal modeling on the modulated feature vector to output the customer attitude vector of the current moment; driving an attention mechanism based on the difference between the attitude vectors of the current moment and the previous moment to dynamically calculate the attention weight of each data item in the business context data set, and generating an updated context vector; feeding the updated context vector back to the feature modulation step, and simultaneously comparing the current attitude vector with a preset threshold vector to trigger corresponding operational intervention instructions.

The application discloses an industrial internet high-risk software identification system and method, and particularly relates to the technical field of industrial internet security, and comprises an industrial software gene map automatic construction module, a business context-aware dynamic risk assessment module and a map-associated precise disposal strategy recommendation module; through multi-dimensional feature acquisition and fusion technology, a unique digital identity is generated for software assets, precise identification is realized; the real security risk of software assets is dynamically quantified by comprehensively considering vulnerability severity, business criticality and network exposure; based on asset relationship map and game analysis, the optimal disposal strategy with the least impact on business is generated and recommended for high-risk software. The application solves the problems of inaccurate industrial software identification, risk assessment out of business context and lack of intelligent decision support for disposal measures in the prior art, realizes automatic closed-loop management from identification, evaluation to disposal, and significantly improves the security protection capability of the industrial internet.

This invention relates to the field of workflow automation technology, specifically to a collaborative management and execution system based on artificial intelligence workflows. The system includes a role behavior representation module, a collaboration relationship characterization module, a collaboration evolution identification module, a task matching and adjustment module, and a collaboration deviation correction module. By capturing role behavior characteristics and task execution performance through data, the system can synchronously map changes in behavioral relationships during task flow, continuously mining the intensity and trends of collaboration. Combined with comprehensive analysis of collaboration status and task suitability, it drives intelligent optimization of task and role allocation order, dynamically adjusts task scheduling structure and allocation priorities, and automatically provides feedback correction for scheduling deviations. With data support, the process proactively adapts to changes in the collaboration chain, simultaneously improving collaboration efficiency and task flow response capabilities. The system's scenario adaptability and collaboration accuracy are significantly enhanced in diverse business environments.

This invention discloses an intelligent financial risk control system based on ABTest technology, belonging to the field of financial risk control technology. It includes a process configuration module, a strategy management module, an ABTest management module, a decision execution engine, a verification and analysis module, a machine learning model integration module, and a real-time monitoring and early warning module. The system allows for the configuration of risk control decision-making processes in a visual manner and supports the flexible integration of various strategy tools and machine learning models. Utilizing ABTest and a champion-challenger (Champion Challenger) mechanism, it conducts parallel testing and scientific evaluation of online strategies and challenger strategies. Based on statistical indicators and model performance indicators, it automatically analyzes and recommends the optimal strategy, achieving intelligent switching and iteration of strategies. This invention features high flexibility, scalability, and intelligence, improving the accuracy, efficiency, and system stability of risk control decisions. It is suitable for the risk management and business optimization needs of financial institutions in rapidly changing business environments.

The application provides a kind of real-time payment authorization method, device, equipment and medium for enterprise fund flow, it is related to payment authorization technical field, comprising: receiving the original payment request initiated by user through associated terminal, and the enhanced payment request is generated in combination with the business context information associated with original payment request;Through shadow account engine, according to the target budget unit of the enhanced payment request to which it belongs, perform budget pre-freezing operation;Generate compliance determination rules matched with enhanced payment request, to use compliance determination rules to determine the compliance of enhanced payment request;When budget pre-freezing operation is successful and compliance determination passes, grant payment authority for enhanced payment request, to release the payment instruction corresponding to enhanced payment request to external fund settlement account.The application realizes the prior forced binding of budget control and payment action, and significantly improves the compliance response speed and the efficiency of industry and finance reconciliation.

This application discloses a method, device, and medium for supply chain security management in rural cooperatives. The method includes: dynamically generating temporary access keys by extracting and quantifying business context factors such as season, logistics, transactions, and land status to achieve fine-grained access control; analyzing cross-module business feature tensors using quantum support vector machines to achieve associated abnormal behavior detection and risk classification; triggering a graded collaborative response mechanism based on risk levels to implement precise protection while ensuring business continuity; and finally achieving full-link verifiable auditing based on quantum-secure blockchain. This application deeply integrates a quantum-secure encryption system with multimodal business data in the supply chain, constructing a three-layer closed-loop security architecture of "quantum control, business anomaly detection, and dynamic resource optimization." This architecture can resist quantum computing attacks, achieve synergistic optimization of security strategies and business efficiency, and provide rural cooperatives with a supply chain management platform that offers forward-looking security and high operational availability.

The application discloses an AI-based Internet of Things card intelligent risk control and dynamic scheduling method, and belongs to the technical field of communication. Through deep fusion of collaborative feature extraction of multi-source heterogeneous data, interpretable causal discovery and quantitative analysis, decoupling and mapping of resource control intention, and collaborative strategy solving, the problems of risk control and resource scheduling fragmentation, limited risk perception dimension, inaccurate diagnosis, response lag, and static and rigid treatment strategy in the management of Internet of Things cards are effectively solved. Thus, the identification, root location and conduction path quantification of potential risks in the Internet of Things card network are realized, and atomized control actions closely bound with risk causal logic and business context can be generated. Through collaborative strategy solving with the balanced target of inhibiting risk diffusion and guaranteeing the quality of key business services, it is ensured that network resources can be intelligently and efficiently scheduled in a complex and dynamic Internet of Things environment.

The application provides a multi-source data processing method, device, equipment, storage medium and program product, and relates to the field of financial technology. The method obtains a feature vector by performing feature processing on business transaction data and business environment data; determines a target risk prediction model based on the feature vector and a risk prediction model set; inputs the feature vector into the target risk prediction model to obtain a risk prediction result; and determines a resource management strategy based on the target risk prediction result obtained through integration processing and a preset resource management rule. The application expands the coverage range of risk prediction data by performing feature processing on the business transaction data and the business environment data to obtain the feature vector; determines multiple risk prediction models based on the feature vector, thereby avoiding the limitations of a single model on a complex risk scenario; and determines the resource management strategy based on the target risk prediction result obtained through integration, thereby not only avoiding the dependence on artificial experience allocation rules, but also improving the accuracy of strategy generation.

The application provides a logistics in-transit risk control method and system based on an AI large model, and relates to the technical field of in-transit early warning. The method comprises the following steps: first, receiving a vehicle-mounted Internet of Things sensing end original alarm message and analyzing the original alarm message into structured alarm data containing an alarm level; then, sending the structured alarm data into a multi-layer analysis of an intention recognition module to analyze out dangerous alarm data containing a dangerous level category and an alarm event subcategory. According to the alarm event subcategory, it is determined whether there is a business attribute requirement, and if there is, data collision is performed with a bulk logistics business system database to retrieve associated business context information. In combination with the dangerous alarm data and the business context, a special prompt word suitable for an alarm scene is matched and generated, the special prompt word and the structured alarm data are input into an instruction generation AI large model to generate a decision instruction, and the decision instruction is sent to a logistics dispatch terminal to complete early warning disposal. The method improves the accuracy and automation level of risk control early warning.

This application provides an intelligent access control management system for network boundary protection, relating to the field of access control management technology. By connecting a threat intelligence and behavior analysis module, this application no longer relies on static rules but dynamically adjusts access control policies based on real-time threat intelligence and traffic anomaly detection results. This enhances the protection capabilities against unknown threats, zero-day vulnerabilities, and internal abnormal behavior, and shortens threat response time. Furthermore, by introducing a business context-aware module, this application can deeply understand the business attributes of network traffic, allowing policy formulation and execution to extend beyond the network layer to the business layer, reducing false positives and false negatives, and ensuring that core business operations are not affected while maintaining security.

The application is suitable for the technical field of intelligent terminals, and provides a dynamic risk control defense method and device, a terminal device and a storage medium. The method comprises the following steps: in response to an update trigger condition, collecting key behavior index data in a business risk control scene; performing cluster analysis on the key behavior index data to obtain cluster centers of various categories; determining a target defense threshold according to the cluster centers and a preset business constraint condition; and performing risk control defense based on the target defense threshold. The application can automatically adapt to changes in business scale and evolution of risk situations, improve the accuracy of risk control defense in complex and changeable business environments, and reduce the rate of false positives.

The application relates to the technical field of business intelligent agent dynamic construction, and discloses an event-driven business intelligent agent dynamic construction method. The method comprises the following steps: receiving and analyzing an original event stream, extracting entity attributes and event intentions; searching historical interaction records based on the same, and inducing task targets and resource constraints; using a machine learning model to perform pattern recognition on the task targets and the resource constraints, automatically generating an intelligent agent construction framework which defines a plurality of seed intelligent agents and their preliminary dependency relationships; according to the resource constraints and the dependency relationships, assigning virtualized resources to each sub-intelligent agent and loading a basic function image, to form an initialized business intelligent agent set; and finally, through iterative adjustment of the set and in combination with a real business environment, completing the dynamic construction of the business intelligent agent. The application realizes the full-process automation and self-adaptation of an intelligent agent system from structure definition to resource deployment, and can efficiently respond to complex and changeable event streams.

The application discloses a power generation side industrial control system network security risk assessment and vulnerability repair linkage method, comprising: based on the vulnerability information of assets in the power generation side industrial control system and the business context information of the assets, a business risk value for representing the actual threat degree of the vulnerability to power generation business is calculated; according to the business risk value and the real-time operation condition and plan information of the power plant production management system, a low business risk period for executing repair operation is obtained as a repair time window, and a corresponding repair plan is matched for the assets; and the repair plan is executed in the repair time window. Through the dynamic linkage of the risk assessment based on the business context, the repair time window decision combining the real-time operation condition and the repair plan execution, the problem that the network security risk is disconnected with the actual production business in the prior art and the repair operation may introduce secondary risk is solved, and the security vulnerability is accurately and efficiently disposed under the premise of guaranteeing the continuity of power generation.

The application discloses a kind of based on formal verification and multi-agent collaborative commercial autonomous evolution AGI system and control method, belong to artificial intelligence technical field.System includes AGI core control module, self-perception evaluation module, formal safety verification module, multi-agent collaborative evolution module, business environment feedback injection module, recursive self-improvement execution module, evolution verification rollback module and data storage backup module.Control method includes self-perception, evolution scheme autonomous design, safety formal verification, multi-agent collaborative decision-making, recursive self-improvement execution, evolution effect autonomous verification, invalid rollback and breakthrough emergence phase, form complete autonomous evolution closed loop.The application guarantees evolution safety by safety lock and formal verification, improves evolution rationality by multi-agent collaboration, makes evolution fit commercial value by business environment feedback injection, and has autonomous verification, automatic rollback and capability emergence characteristics, can realize safe, controllable, scalable autonomous evolution without continuous intervention of artificial, applicable to enterprise-level commercial AGI autonomous iteration and deployment scene.

Computerized systems and methods are described for enabling resellers to manage their end-user business within their own business environment on a distribution platform. The system includes a server configured to provide a Single Pane of Glass User Interface (SPoG UI) and a Real-Time Data Mesh (RTDM) module for ingesting and standardizing data from multiple sources. Advanced Analytics and Machine Learning (AAML) models analyze the data to provide predictive analytics, anomaly detection, and personalized recommendations. The SPoG UI presents real-time data and insights through interactive visualizations, enabling resellers to perform actions such as creating quotes, placing orders, and managing customer accounts. The system supports real-time negotiation of pricing, compliance management, and integration with external systems via APIs. The method and system generates a real-time, end-to-end view of both supply and end-user customer interactions.

The application discloses a large file cross-unit transmission method based on a business cooperation scene, relates to the technical field of cross-organization data transmission and resource scheduling, and takes a business context as a core to map an event type, an emergency level and an SLA deadline time into a calculable priority, and is linked with real-time network measurement results, so that scheduling no longer stays in static file attributes, but can instantly improve the service share of a key request according to events such as out-of-stock early warning and promotion start, thereby reducing the probability that a high-emergency task is blocked by regular transmission. By introducing unit-level quotas, admission and interruptible preemption on the side of a bottleneck link, long-term occupation caused by first-come-first-served is avoided, basic fairness between different units is ensured, and bandwidth can be quickly transferred to more urgent flows at a business peak moment. Weighted round-robin weights are dynamically updated according to the emergency degree, the SLA urgency degree and the quota surplus degree, hysteresis and minimum interval control are superimposed, and the agility of scheduling is improved.

The application discloses an API security vulnerability dynamic monitoring method based on abnormal monitoring, and belongs to the technical field of computer security protection, which comprises the following steps: obtaining request layer data and response layer data of a target API, and generating a traffic data set; extracting behavior features from the traffic data set, and generating a behavior feature matrix; performing context awareness analysis on the behavior feature matrix, combining a business object mapping relationship to generate a vulnerability chain prediction result; analyzing vulnerability path nodes in the vulnerability chain prediction result, and generating temporary detection rules; using the temporary detection rules to perform real-time detection on subsequent API requests, and outputting a security detection result. The application adopts a traffic feature extraction, vulnerability chain prediction and self-adaptive rule generation mechanism, and can realize real-time threat interception and continuous optimization of security protection closed loop associated with business context.

The present disclosure provides a business management method and device, equipment and medium, relates to the technical field of artificial intelligence, and can be applied to various scenes such as cloud technology, artificial intelligence, intelligent transportation and auxiliary driving. The method comprises the following steps: querying at least one target container image information and at least one target test instance information matched with running sub-environment information corresponding to a target business from a preset relationship table; obtaining detection index information corresponding to the target container image information; and calling a general business test program, so that the general business test program detects at least one running sub-environment based on the detection index information and the corresponding target test instance information. In this way, specific business environment detection scripts do not need to be written for each business service device, the workload of business environment testing is reduced, and the environment detection response rate is improved.

The application provides a large model skill routing method and system for urban and rural water supply intelligence, belonging to the technical field of intelligent water affairs, the method comprising: obtaining historical calling information and business data of urban and rural water supply projects and preprocessing to construct a structured data set; constructing a heterogeneous graph through the structured data set; generating initial semantic features of each node type through a large language model; training a heterogeneous graph neural network model according to the heterogeneous graph and the initial semantic features of each node type; performing intention reasoning on the intention text input by a user through the trained heterogeneous graph neural network model, and outputting vector representations of each node; scoring the intention-skill matching degree according to the vector representations of each node, and feeding back the K skills with the highest matching degree score to the user. Through dual cognition of semantics and structure, the application realizes dynamic routing of business context awareness, and significantly improves the accuracy and generalization ability in complex water supply scenarios.

The application provides a marketing resource allocation method and device, which can be used in the field of artificial intelligence technology, and the method comprises the following steps: through a conversion estimation model, the core feature set, the stored product basic attribute, the marketing binary classification label and the purchase binary classification label are subjected to probability prediction to generate a basic purchase probability and a marketing intervention purchase probability, and the marketing sensitivity level of a user is determined; based on the marketing sensitivity level of the user, a marketing resource allocation scheme is constructed, precise calculation and grading based on individual causal incremental effect are realized in the marketing resource allocation, the sensitivity of the user to the marketing intervention is accurately quantified, and effective stratification of the user group is realized; meanwhile, the built-in dynamic features and model iteration mechanism guarantee the robustness and adaptability of the algorithm in the continuously changing business environment from the system level, form an intelligent decision-making closed loop that can be self-optimized, ensure the dynamic adaptability and long-term effectiveness of the marketing strategy, and optimize the user experience while improving the conversion effect.

This application discloses a method for building a multi-scenario intelligent customer service system in the energy industry, belonging to the field of computer technology. By constructing a digital twin of the scenario process and a multi-source business data model, and performing dynamic coupling and automated simulation dialogue in a dynamic business sandbox, a comprehensive pre-playback of the intelligent customer service process in the energy industry is achieved. Through joint analysis of the pre-playback logs and the business data model, conflicts caused by dynamic mismatches in business data or differences in user expression can be identified, and their root causes and scope of impact can be analyzed. Based on this, a structured optimization strategy can be generated and iteratively verified, thereby solving the problems of insufficient coupling, delayed conflict identification, and optimization dependence on trial and error in traditional customer service systems under complex and dynamic business environments, and improving the robustness and deployment efficiency of the intelligent customer service system.

This application relates to the technical field of data analysis, and discloses a method, system, and storage medium for multi-dimensional attack surface identification based on asset changes. The method includes: receiving standardized change events and parsing key attributes from the standardized change events; obtaining business context tags associated with multi-dimensional data assets from an asset knowledge graph; matching control items from a compliance rule set according to the change dimension and change type, and loading the weight values ​​and judgment logic scripts of the control items; obtaining intelligence text associated with the change content snapshot and converting the intelligence text into an intelligence vector; constructing a fusion vector based on the change content snapshot and the intelligence vector; obtaining a compliance status quantification algorithm, constructing a weighted mask vector, and performing a Hadamard product operation on the fusion vector and the weighted mask vector to generate a weighted fusion vector; constructing a structured prompt word template, calling a large language model processing instruction, processing the fusion vector, and outputting the attack surface judgment result, thereby improving the accuracy of risk identification.

This specification provides a knowledge graph-based method and system for analyzing the risk correlation of technology data leakage. The method includes the following steps: S1: Extracting entity data streams related to technology data from the R&D and operation environment of technology projects, and identifying the entity data streams using a predefined rule model to construct an entity dataset; parsing the entity dataset to determine the static structural relationships and dynamic behavioral relationships between entities; S2: Using the entity dataset as a set of nodes, and combining it with an edge set of static structural relationships and dynamic behavioral relationships to construct a knowledge graph, while simultaneously calculating the associativity weight and risk weight of the edge set. This invention constructs a knowledge graph that integrates business context by fusing four types of entities—personnel, projects, assets, and terminals—and their static structural and dynamic behavioral relationships, transforming the analysis perspective from single-point anomalies to a relational graph, and facilitating the identification of key links in the transmission of critical risks.