100 results about "Business environment" patented technology

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

The invention discloses a white list state synchronous processing method and system combined with database transaction control, and relates to the technical field of database transaction control and data synchronous processing. The method comprises the following steps: recording a white list state change event based on a pre-writing mechanism of a database transaction log; change events in a transaction log are classified and aggregated according to a white list and then are asynchronously written into a specified buffer area, when a transaction is submitted, the sequence of data in the buffer area is controlled through a lightweight lock mechanism to be refreshed to a physical disk, and the priority of a disk IO queue is dynamically adjusted according to the transaction isolation level to reduce concurrent conflicts; according to the white list state synchronization processing method and system combined with database transaction control, the transaction concurrent processing performance and the database response efficiency are remarkably improved, and the expansion capability and stability of the white list synchronization system in a complex business environment are also improved.

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.

The invention discloses a high-interaction deception defense and attack tracing method based on an RASP technology, and the method specifically comprises the following steps: S1, RASP implantation and bait deployment, S2, attack detection and simulation triggering, S3, attack session redirection and high-interaction deception, S4, attack behavior monitoring and tracing data collection, and S5, tracing analysis and threat disposal. The invention relates to the technical field of network and information security. According to the high-interaction deception defense and attack tracing method based on the RASP technology, a deception mechanism is embedded into a real application / system in operation, and high fusion of a real business environment and a false trapping environment is achieved. Different from a traditional'main-auxiliary separation 'mode that a honeypot is placed in an independent network or a host, the method has the advantage that the application can be changed to generate a'trap' during running through the RASP. The seamless embedded spoofing makes attackers difficult to distinguish true and false: the attackers attack a part of a production system initially, and then are introduced into a parallel simulation space unconsciously.

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.

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 embodiment of the invention provides a method, device and equipment for training a workflow to create a model, a storage medium and a program product. The method comprises the steps of obtaining a creation request for creating a workflow for a data object; acquiring data of a workflow corresponding to the creation request, wherein the workflow is created based on the creation request and the information of the data object; based on the data of the workflow, determining first sample data related to a workflow framework of the workflow and second sample data related to configuration information of a plurality of nodes in the workflow framework; and training a workflow creation model based on the first sample data and the second sample data. A workflow creation task is divided into two types of sub-tasks, namely, a framework generation sub-task and a node configuration generation sub-task, so that the learning target of the model in each training stage can be clarified. In this way, the adaptation capability and creation quality of the workflow creation model in a real business environment can be improved.

The invention discloses a business integrated service platform and a data processing method applied by the business integrated service platform, and relates to the technical field of informatization services, and the business integrated service platform comprises a data fusion module which is used for converging cross-department business data of a full life cycle of an industrial project and constructing a unified full-chain electronic archive library; the supervision model module is used for establishing an industrial project digital monitoring model based on a full-chain archive library, monitoring key links of the project and generating a digital intelligent early warning signal; the service cooperation processing module triggers a flattening cooperation service instruction based on the digital intelligent early warning signal, generates a project blocking point problem and dispatches the project blocking point problem to a responsible department terminal; and the visual decision-making module is used for integrating the acquired investment attraction data, project progress data and approval efficiency data to obtain and display industrial layout, project performance and business environment trend analysis results. The invention discloses a high-efficiency industrial project supervision system capable of cracking data fragmentation dilemma and realizing full-period dynamic monitoring.

According to the data linkage display method and system based on natural language interaction, multi-modal natural language understanding, intelligent task matching and a front-end and back-end linkage control mechanism are fused, efficient, intelligent and low-delay interaction control between a mobile terminal and a data large screen is achieved, and the overall expandability and stability of the system are improved. Through a multi-modal natural language input mode, instruction understanding and tool selection are carried out in combination with a large language model, so that a non-professional user can also rapidly complete control operation on a data large screen through a natural language, the use threshold is remarkably reduced, after a rear end completes data processing, a control instruction is actively pushed to the front end of the large screen, and the user experience is improved. A user does not need to initiate a request again, the interface response speed is remarkably improved, meanwhile, operation tools are divided according to function categories, a dynamic loading mechanism is combined, related tool information is only input into a large language model when needed, the situation that context is overlong due to one-time input is avoided, and the adaptive capacity of a system in a complex service environment is improved.

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.

The invention belongs to the field of data security, and discloses a financial business data risk early warning management method and system based on artificial intelligence, and the method comprises the steps: 1, obtaining a time sequence of an actual execution amount, and decomposing the time sequence of the actual execution amount to obtain an abnormal score of a budget item; step 2, acquiring an abnormal confidence score of the budget item based on the abnormal score, and judging whether the budget item is an abnormal budget item based on the abnormal confidence score; step 3, constructing a directed graph based on the business event data and the abnormal budget item; step 4, obtaining all paths which take the nodes with the abnormal budget as the end points and meet the path constraint in the directed graph, and obtaining a path set; respectively calculating a path score of each path; and 5, calculating a priority score of each path based on the path scores, obtaining a main path based on the priority scores, and generating a response suggestion based on the main path. According to the invention, the interpretation capability and the treatment capability of the financial risk early warning system in a complex business environment are improved.

The invention discloses a method for determining an optimal read-write strategy of an RAID card and a related product. The method comprises the following steps: acquiring an iostat echo field of the RAID card; performing feature extraction on the iostat echo field to obtain feature data; and performing feature matching on the feature data and an IO model library, and determining an optimal read-write strategy of the RAID card. According to the method, the extracted feature data and the IO model library which is pre-constructed and continuously optimized are subjected to matching analysis. And automatically recommending the most matched read-write strategy combination. According to the method, subjective errors caused by manual intervention are reduced, and the configuration accuracy and the adaptive capacity of the RAID card in a complex and changeable service environment are remarkably improved, so that the performance and the stability of a whole storage system are effectively 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.

The invention provides a network security monitoring method, which comprises the following steps that: a dynamic camouflage network is configured in a bypass of a real service network, the dynamic camouflage network is dynamically composed of a plurality of honeypots, a dynamically changing honey file is configured in the real service network, each honey file is used for guiding a visitor to be connected to the honeypot, and a bait file is configured in the honeypot; when the visitor accesses the honey file in the real service network, guiding the visitor to connect to the honey pot corresponding to the honey file through the honey file; collecting operation behavior data of the visitor on the bait file in the honey pot corresponding to the honey file; and determining whether the visitor has an attack behavior based on the operation behavior data. A disguised network dynamically composed of a plurality of honeypots is deployed in a real service network bypass, and a honeypot file is dynamically arranged in a real service environment, so that a potential attacker is effectively guided to access a honeypot system, the concealment and authenticity of the honeypot system can be enhanced, and more active, adaptive and efficient network security monitoring is realized.

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 application provides a dynamic air conditioner adjusting method and system based on IT load prediction. The system uses operation terminal performance parameters, computing power scheduling index characteristics, equipment comprehensive performance data, business environment correlation characteristics and corresponding IT load conditions to construct an IT load prediction model. The constructed IT load prediction model predicts real-time IT load conditions. An air conditioner dynamic adjustment control model is constructed based on the IT load conditions, air conditioner dynamic adjustment indexes and the dynamic air conditioner adjusting method. The final dynamic air conditioner adjusting method is obtained by processing the real-time IT load conditions and corresponding air conditioner dynamic adjustment indexes based on the air conditioner dynamic adjustment control model. The air conditioner state is adjusted based on the final dynamic air conditioner adjusting method. The temperature difference control range of the machine room is within 0.8 DEG C by combining double machine learning models. The two constructed models realize real-time control of IT load prediction and air conditioner dynamic adjustment, and improve the high-precision control efficiency and high-efficiency energy-saving performance of the air conditioner system.

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.

According to the deep learning-based oversale strategy optimization method and system, abnormal fluctuation of a business feature combination is monitored in real time, a wartime commander system is activated when an extreme threshold is triggered, a cross-domain extreme case think tank and a rule-data double-engine drive are utilized to carry out resource allocation decision, and the method and system are suitable for large-scale popularization and application. A three-dimensional interpretable architecture is constructed to realize man-machine transparent cooperation, strategy conflicts are processed through a conflict resolution protocol stack, a lightweight simulation engine mode is switched according to a service scene, cross-domain migration simulation is performed on an unknown scene, a closed-loop evolution system is established, bidirectional model enhancement is realized through battlefield data backflow, and a sustainable defense system is constructed. And self-adaptive threshold calibration and man-machine symbiosis training are completed. According to the scheme, the limitation of a traditional single optimization model is broken through, the problem of response failure in an extreme scene in the prior art is effectively solved, and the stability and reliability of a system in a complex service environment are remarkably improved.

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.

The invention provides a multi-modal data intelligent filing management system, and relates to the technical field of multi-modal data processing, and the system comprises a data mapping module which is used for extracting multi-modal data from a native business environment, generating a data unit set, constructing a virtual reference plane, establishing a reference rectangular grid topological structure in the plane, and dividing a dynamic coordinate axis, mapping the data unit set to grid coordinates, and outputting a grid coordinate mapping data unit set; and the attribute tree construction module is used for constructing a multi-dimensional attribute tree according to the density distribution in the grid coordinate mapping data unit set, generating dynamic evaluation nodes, calculating a weight parameter beta value of each node, and outputting an attribute tree structure with the beta value. According to the method, grid coordinate mapping, attribute tree weight evaluation, differentiation processing, block chain evidence storage, hierarchical verification storage and user behavior driven dynamic optimization of the multi-modal data are realized, so that the data independence, security and utilization efficiency are improved, and the management cost is optimized.