66 results about "Business environment" patented technology

The invention provides a business resource optimization management method and system based on computing power demand prediction, and the method comprises the steps: collecting multi-source heterogeneous data in real time, so as to construct and continuously update a topological graph which represents the dynamic association relationship among a business entity, a logic service entity and a physical resource entity; a time sequence state of a topological graph is input into a prediction model fusing a graph structure and continuous time modeling, multi-dimensional evolution calculation of a node state is carried out by the prediction model, a dynamic resource value signal is generated by a central scheduler under a hierarchical decision framework, a plurality of service unit intelligent agents are guided to carry out distributed collaborative decision, and a multi-dimensional resource value signal is generated by the central scheduler. Iteratively generating an optimal resource allocation scheme; and before the optimal resource allocation scheme is executed, potential risk assessment and credible automatic execution are carried out. Therefore, in a complex dynamic commercial environment, the computing power resource is converted from global perception, accurate prediction and intelligent optimization to risk-controllable closed-loop management and adaptive configuration.

The invention relates to the technical field of artificial intelligence and reinforcement learning, and discloses a cross-border trade intelligent decision optimization method based on reinforcement learning. The method comprises the following steps: constructing a composite state representation fusing tax, logistics, exchange rate, demand and competitive behavior; high-dimensional state compression is realized through an auto-encoder; designing a hierarchical action architecture to decouple a macroscopic strategy and a microscopic operation; a Nash equilibrium guided reward shaping function is introduced, and an equilibrium income deviation is estimated in combination with anti-factual reasoning; carrying out stable training by adopting a double-delay depth deterministic strategy gradient algorithm with state transition consistency constraint; an online fine tuning mechanism is deployed to adapt to a real business environment. According to the method, the strategy convergence speed, the annual profit rate and the responsiveness to policy mutation 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 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 invention belongs to the technical field of load balancing, and discloses a printing equipment dynamic load balancing distribution method based on real-time order flow. The method comprises the following steps: acquiring order submission data streams distributed in a plurality of business areas in real time, and constructing an order traffic space-time heat matrix; obtaining an accumulated running time record, a historical fault maintenance log and a periodic maintenance record of each printing device, and dynamically evaluating a residual health life index; obtaining geographic coordinates of each business area and each printing device to form a time delay cost matrix; constructing an equipment load protection weight vector based on the residual health life index of each printing equipment, and carrying out dynamic fusion operation on the equipment load protection weight vector and the time delay cost matrix to generate a scheduling priority scoring matrix; according to the scheduling priority scoring matrix, a final printing task allocation result table is generated, and task scheduling is carried out; according to the method, the optimal scheduling effect can be always kept in a complex and changeable business environment, so that the printing service quality is improved.

A system is provided.SOLUTION: This system includes a means for automatically collecting customer information from an information source on the Internet, a means for analyzing the collected customer information and grasping the business environment and market trend of a customer company, a means for specifying the proposal need of the customer on the basis of an analysis result, a means for automatically generating a proposal material corresponding to the specified proposal need, and a means for transmitting the generated proposal material to a user terminal.SELECTED DRAWING: Figure 1

This invention discloses a knowledge graph-based intelligent question-and-answer method for audit guidance, relating to the field of digital auditing. Through knowledge graph framework design and ETL data warehouse technology, this invention extracts, cleans, and transforms audit business data relevant to business environment optimization audits, achieving the fusion of various business data to provide data support for intelligent question-and-answer. By constructing an intelligent question-and-answer system, and designing a multi-task-oriented approach based on real-world audit scenarios, it utilizes word segmentation, matching, and query modules to achieve knowledge graph-based intelligent question-and-answer for business environment optimization audit guidance. This reduces the workload of the audit process, freeing auditors from tedious and repetitive tasks, allowing them to focus more on risk identification, problem analysis, and root cause analysis, effectively improving audit efficiency and quality.

This invention relates to the field of artificial intelligence technology and proposes a deep AI-driven method for data anomaly detection and self-healing. The steps include: constructing a data governance and preprocessing platform to preprocess multi-source heterogeneous data and obtain high-quality data sources; employing a multi-algorithm fusion mechanism to detect anomalies in the data sources; constructing a dynamic modeling and self-learning engine to rapidly adapt model parameters based on meta-learning and to automatically search and optimize the model structure based on reinforcement learning to adapt to changes in data distribution and business environment; and providing an intelligent repair and self-healing mechanism for detected abnormal data, automatically correcting, repairing, and performing root cause analysis on data source anomalies, geographical location anomalies, data push failures, and system operation failures. This invention achieves high-precision detection of complex anomalies through a cascaded multi-algorithm fusion detection framework and realizes fully automatic model evolution through a collaborative adaptive engine of meta-learning and reinforcement learning.

This application provides a traffic prediction method and related apparatus, relating to the field of software technology. The method includes: acquiring the temporal traffic characteristics and interface spatial characteristics of a target interface; retrieving a trained traffic prediction model, wherein the traffic prediction model is obtained by iteratively training a basic model using the temporal traffic characteristics and interface spatial characteristics of a calibrated interface as training samples, with the goal of the predicted traffic of the training samples approximating its actual traffic; and inputting the temporal traffic characteristics and interface spatial characteristics of the target interface into the traffic prediction model to obtain the predicted traffic of the target interface. This application achieves high-precision prediction and reliability assessment of interface traffic in complex business environments, providing data support for resource scheduling and risk prevention.

The application discloses an intelligent WAF attack flow discrimination method, system, device and medium based on a large model MoE, belongs to the technical field of network security and artificial intelligence, and aims to solve the technical problem that an existing Web Application Firewall mainly relies on rule matching or a single model mode in an attack flow discrimination process, and it is difficult to effectively cope with diversified attack types, significant attack feature differences and dynamic business environment changes. The technical scheme is as follows: collecting Web access flow: collecting access request flow entering a Web application from a Web Application Firewall or a network environment where the Web Application Firewall is located through flow mirroring, bypass listening, log collection or interface calling; analyzing and preprocessing the access flow: analyzing and preprocessing the access request flow to generate structured request data; constructing a flow feature vector; discriminating attacks based on a large model of an MOE architecture; outputting an attack flow discrimination result; and executing a WAF protection strategy.

The application belongs to the technical field of load balancing, and discloses a printing equipment dynamic load balancing distribution method based on real-time order flow; the method comprises the following steps: collecting order submission data streams distributed in multiple business areas in real time, and constructing an order flow space-time heat matrix; obtaining cumulative running time records, historical fault maintenance logs and periodic maintenance records of each printing equipment, and dynamically evaluating a residual health life index; obtaining geographic coordinates of each business area and each printing equipment, and forming a time delay cost matrix; constructing an equipment load protection weight vector based on the residual health life index of each printing equipment, and performing dynamic fusion operation with the time delay cost matrix to generate a scheduling priority score matrix; generating a final printing task distribution result table according to the scheduling priority score matrix and performing task scheduling; and the application can guarantee optimal scheduling effect under complex and changeable business environment, thereby improving printing service quality.

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.

Systems and methods are described that enable dealers to manage their end user traffic in an own traffic environment on a distribution platform. The system includes a server configured to provide a single window user interface (SPoG UI) and a real-time data grid (RTDM) module for ingestion and standardization of data from multiple sources. An advanced analysis and machine learning (AAML) model analyzes the data to provide predictive analysis, anomaly detection, and personalized recommendations. The SPoG UI presents real-time data and insights through interactive visualization, enabling dealers to perform operations such as creating offers, issuing orders, and managing customer accounts. The system supports real-time pricing negotiation, compliance management, and integration with external systems via an API. The method and system generates an end-to-end real-time view of the provider's interaction with the end-customer.

The utility model discloses an auxiliary device for fixing a label box, which relates to the technical field of label boxes, and specifically comprises a bottom plate, a plurality of rows of protrusions and a plurality of fixing plates, the inclined block is provided with an inclined surface and a mounting surface used for being connected with the label box, and the inclined surface is provided with a plurality of grooves connected with the protrusions in an inserted mode; the end part of the inclined block is convexly provided with a dismounting convex block; the bottom plate with the multiple rows of protrusions and the detachable inclined block are arranged, the label boxes are fixed to the inclined block, and the label boxes can be distributed on the bottom plate in a stepped mode, so that the displacement rate and the falling frequency of the label boxes are effectively reduced, accurate display of price information is guaranteed, and the label box is suitable for cigarette cabinets of different sizes and multi-scene adaptation. And meanwhile, the display efficiency of retailers is improved through the modular design. In addition, the device gives consideration to low cost and high adaptability, helps small and medium-sized retailer households to improve the operating environment with low investment, and promotes specialization of commodity display.

The invention discloses an application management method. The method comprises the steps that an interaction device receives a query of a user; the agent routing arrangement device identifies an agent requested to be queried by a user according to the query, retrieves a target agent matched with the query when querying an unrequested agent, then allocates a task for the target agent, and determines an execution sequence or a dependency relationship of the task; the agent routing arrangement device generates a standard operation program (SOP) through a language model according to the execution sequence or dependency relationship of the tasks and the target agent; and the agent execution device loads the target agent according to the standard operation program, executes the operation node in the target agent, and obtains an answer. According to the method, intention recognition can be carried out on the query of the user, the target agent matched with the query can be retrieved, the task can be allocated to the target agent, a static or dynamic SOP can be generated through task arrangement, diversified service requirements and a complex service environment can be met, and high flexibility and adaptability are achieved.

The application particularly relates to an adaptive alarm implementation method. The adaptive alarm implementation method adopts a statistical analysis algorithm to analyze and calculate mass data, and obtains a threshold interval; if an index triggers an alarm, data is re-collected for statistical analysis, a new threshold interval is obtained, and dynamic adjustment of the threshold is realized; the size period of a window is dynamically divided according to a time level and alarm flow, so that dynamic extraction and analysis of alarm events are realized; alarm event information is preprocessed, and whether the alarm event is of the same type is judged by matching similarity; and a unified template is used to output the alarm event of the same type. The adaptive alarm implementation method can not only adapt to a business environment in a process of continuous service change, better reflect changes of alarm thresholds, but also optimize and improve alarm quality according to different business scales, improve the accuracy of the alarm and the efficiency of fault management, and reduce the cost related to the alarm.

A data disaster backup and recovery drill method and system, wherein the method comprises: acquiring service node business traffic data, a plurality of calling service node calling relationships and data flow transfer records; constructing a service dependency directed graph, generating a calling service node hierarchical table; completing environment initialization in a preset drill environment; establishing a traffic mirroring channel in a target business environment, copying read-only business traffic in the target business environment to the preset drill environment in real time through the traffic mirroring channel; acquiring a first data change snapshot of the target business environment and a second data change snapshot in the preset drill environment; generating a data consistency verification baseline table; analyzing the data consistency verification baseline table to determine a data inconsistency event, updating the service dependency directed graph based on the data inconsistency event, and generating a comprehensive drill report. The application can improve the accuracy of data disaster backup and recovery drills.

The invention relates to a cloud edge collaborative streaming analysis method, system and device for business big data, and the method comprises the steps: receiving streaming data packets from a plurality of edge nodes, extracting business entity identifiers of the streaming data packets, carrying out the fusion calculation of the business entity identifiers and feature vectors, and generating a global state view; performing traversal matching on the global state view and rule conditions in a preset first business rule set, identifying abnormal state features, triggering a rule updating process, extracting a historical state view from a historical database and performing business iteration on the historical state view and the abnormal state features, and generating a candidate replacement rule set; and packaging the replacement service rule into a rule updating instruction according to the sorting issuing queue, and distributing the rule updating instruction to the corresponding edge node to complete rule updating. According to the invention, the real-time accurate updating of the business rule can be realized, so that the response speed and decision accuracy of the dynamic business environment are obviously improved.

The invention discloses a data classification method and device based on machine learning, equipment and a storage medium, and relates to the field of machine learning. Constructing and training a classification model by taking the extracted bag-of-words vector as model input and the classification identifier as target output, and constructing a structured knowledge graph by taking the keywords extracted from the model, the classification and the weight coefficient as knowledge entries; to-be-classified field information is obtained, keyword matching is carried out based on the knowledge graph, graph keywords contained in the to-be-classified field information are extracted, and a graph keyword set is constructed; and extracting graph keywords, matching candidate classifications by matching knowledge entries, and screening target classifications based on weight coefficient scores. According to the scheme, explicit expression and dynamic fusion of machine learning are achieved by constructing the knowledge graph, the contradiction that classification decision cannot be explained and knowledge is difficult to update is effectively solved, the defects that a rule method is rigid and a machine learning method is black in a traditional technology are overcome, and the adaptability and reliability of a system in a dynamic service environment are improved.

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 present application relates to resource management technical field, especially to a kind of business environment data resource management platform.The present application includes application security module, data security module, host security module, network security module and boundary security module;The application security module is responsible for the security protection of audit log and system application;The data security module realizes data backup security by timing increment and full data backup;The host security module provides security protection for server equipment through various protection measures;The network security module ensures the safety of network channel environment, and realizes the monitoring of network channel security protection;The boundary security module ensures the security of boundary and audits the tracking of intrusion detection event.The purpose of the present application is to provide a kind of business environment data resource management platform, to protect the safety of network channel, network channel environment, and handle intrusion event while protecting data security in time.

The described system provides a novel, efficient, and scalable approach to using LLMs for domain-specific applications. By combining a pre-trained LLM model with a semantic search engine and a range of specialized modules, the system overcomes the limitations of traditional LLM-based systems. It offers accurate, context-aware responses to domain-specific queries without the need for costly retraining, making it an ideal solution for dynamic and complex business environments.

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.

A method and apparatus for distributed voice interaction in offline AI-driven operations are disclosed. The method includes: acquiring voice data from offline operational scenarios and extracting several candidate voices from the voice data through voiceprint recognition; performing order-specific keyword recognition on the candidate voices to determine the target interactive voice and generating a voiceprint mask based on the target interactive voice; performing noise filtering processing on the target interactive voice using the voiceprint mask to obtain standard voice data; generating response text or business instructions based on the standard voice data and the business model, and sending the response text or business instructions to the corresponding target terminal. The method demonstrates high accuracy and good adaptability in voice instruction recognition for order-taking scenarios in noisy offline commercial environments. Furthermore, by combining cloud-based business models and role-based permission mapping, it achieves accurate distribution and response of instructions, meeting the AI-driven operational interaction needs of offline merchants across multiple roles and scenarios.

The invention discloses a business environment assessment method, device and equipment, a medium and a product. The method comprises the following steps: when a business environment evaluation request for a plurality of to-be-evaluated areas is received, obtaining original data of each to-be-evaluated area under a plurality of feature dimensions under at least one time slice within a preset duration based on the business environment evaluation request, and determining an original input matrix; performing data fusion processing on the original data in the original input matrix based on a data fusion model to obtain a to-be-fused matrix corresponding to each to-be-evaluated area and a to-be-processed matrix corresponding to a plurality of to-be-fused matrixes; processing the to-be-processed matrix based on a pre-trained business environment evaluation model to obtain an area evaluation result corresponding to each to-be-evaluated area; and based on the region evaluation results corresponding to the plurality of to-be-evaluated regions, determining a business environment evaluation result and feeding back the business environment evaluation result. According to the invention, the business environment evaluation processing of the plurality of to-be-evaluated areas is realized, and the accuracy of the evaluation result is improved.

The application relates to a container state recovery method and device, computer equipment, a storage medium and a software robot. The method comprises the following steps: detecting, in real time, a container state of a target container running in a business environment and resource usage of the business environment; when the container state is abnormal, accessing a pre-configured self-recovery environment in the business environment, and performing state self-recovery on the target container according to the resource usage; using state data of the target container to pull up a standby container corresponding to the target container in the self-recovery environment, and transferring traffic of the target container to the standby container for processing; continuously detecting the container state of the target container, and when the container state returns to normal, re-registering the target container in the business environment, and distributing traffic to the target container for processing. The method can intelligently realize self-recovery of a fault container while ensuring continuous traffic service, thereby reducing operation and maintenance labor costs and improving container state recovery efficiency.

The invention discloses a bank industry vulnerability dynamic grading method and system and computer equipment. The method comprises the steps that an application system design document is analyzed based on a pre-trained application security big language model and an RAG technology, and a function point and an operation scene are recognized; based on the function point and the operation scene, calculating an application system risk level through attack surface quantification and an operation environment factor; performing security testing on the application system, and obtaining a vulnerability technical level based on a preset vulnerability detection tool; and in combination with the vulnerability technical level and the application system risk level, outputting a vulnerability comprehensive rating through a preset comprehensive rating table. According to the method, the problem that the business environment risk is ignored in a traditional vulnerability grading method is solved, dynamic, scenarized and automatic vulnerability grading is realized, and the accuracy and efficiency of bank vulnerability management are remarkably improved.

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 a business environment variability prediction system and method, wherein the system comprises: in a quantitative prediction model, a variational causal decoupling framework is constructed to decompose data in a collected heterogeneous data set into causal invariant features and environmental noise; the causal invariant features are mapped to a non-Euclidean hyperbolic space, a hyperbolic convolution operator is used to construct a dynamic topology correlation graph for representing hierarchical correlations between entity nodes in a business environment; and on the basis of performing adversarial occlusion and self-supervised reconstruction on the dynamic topology correlation graph, the variability trajectory of the business environment is quantitatively predicted by a neural ordinary differential equation. Through the application, the causal features and noise in the heterogeneous data are decoupled, the generalization capability of the model to sudden factor changes is improved, a topology graph capable of accurately representing the hierarchical correlations between nodes in the business environment is constructed in a non-Euclidean space, and then the continuous variability trajectory curve of the business environment is accurately captured by using a neural ordinary differential equation.