2786 results about "Business process" patented technology

The invention relates to a digital resource sharing method and system based on a pedigree mapping relation, and belongs to the technical field of enterprise digital resource management, and the resource sharing method comprises the steps: obtaining multi-source heterogeneous data from an internal distributed system of an enterprise through a preset interface protocol; performing standardization processing on the multi-source heterogeneous data to generate a structured resource pool; a graph dictionary module is constructed, the resources in the structured resource pool are subjected to association topology analysis, and a resource relation topological graph is output; establishing a pedigree relationship between resources and business scenes based on the three-dimensional mapping model, respectively generating and fusing a business process demand map, a production stage demand map and a product capability matching map, and constructing a visual digital resource capability platform; and outputting the target resource identifier and the associated path in response to a resource calling instruction input by a user. Integration and sharing of internal and external multi-source heterogeneous data of an enterprise can be realized, the problem of data islands is solved, and meanwhile, the intelligent degree of a resource management system is improved.

The invention relates to the technical field of business process compliance inspection systems, in particular to a compliance inspection system and method for an enterprise business process, and aims to solve the problems that in the prior art, an enterprise business process compliance inspection system generally adopts a static risk assessment model and lacks automatic and intelligent data acquisition and processing means, so that the enterprise business process compliance inspection system is inconvenient to use. And the accuracy and timeliness of risk assessment are insufficient. The system comprises a data acquisition module, a risk assessment module, a monitoring and early warning module, a map construction module, an intelligent recommendation module and a report generation module. According to the invention, through automatic data acquisition, preprocessing, risk assessment and monitoring early warning, the efficiency and accuracy of compliance inspection are significantly improved; by utilizing artificial intelligence and big data technologies, the system can quickly identify potential compliance risk points and perform quantitative evaluation, so that errors caused by manual intervention and subjective judgment are reduced, and the efficiency and accuracy of compliance inspection are improved.

The invention discloses a causal atlas-driven enterprise intelligent decision-making method and system, and belongs to the technical field of enterprise management. The method comprises the following steps: constructing an enterprise-level causal atlas by obtaining enterprise business processes, operating indexes and unstructured text data; executing path reasoning based on the to-be-decided data, and determining a multi-hop causal path chain; determining a current association rule and a decision suggestion in combination with an expert rule base; causal maps and rule tags of different industries are introduced, and a migratable causal path and an association rule are identified based on structural similarity and a semantic mapping rule; and finally, current and migration knowledge is fused to generate an enterprise decision execution scheme and a causal reasoning result. According to the scheme, based on comprehensive application of current and migrated knowledge, scientificity, accuracy and interpretability of decision making are effectively enhanced, meanwhile, dynamic updating and inter-industry knowledge sharing are supported, enterprise decision making efficiency and response speed are remarkably improved, and enterprises are assisted to achieve intelligent and fine management.

The invention relates to the technical field of process automation, in particular to an RPA service automation method capable of enabling a large language model MCP. The method comprises the following steps: acquiring user input data, and performing intention understanding through a multi-round intention recognition model according to the user input data to obtain semantic instruction data; performing standard semantic protocol conversion according to the semantic instruction data to obtain standard semantic protocol data; performing flow chart node matching according to the standard semantic protocol data to obtain flow chart node data; and generating a business process according to the flow chart node data to obtain business process data. According to the method, the multi-round intention recognition capability of the large language model and the standard semantic protocol conversion are fused, so that the accurate mapping from the natural language to the business process is realized, and the intelligent level and the user friendliness of the RPA system are improved. Through process node matching and process logic verification, the execution effectiveness of the automatic generation process is ensured, and the labor cost and configuration errors are reduced.

The invention provides a non-performing asset cross-scene question and answer framework based on a knowledge graph, and relates to the field of artificial intelligence and legal science and technology, and the framework comprises a framework, a knowledge fusion layer is provided with a multi-source data access module which is used for obtaining heterogeneous data of a law and regulation database, a judicial case library, an enterprise financial statement system and an asset transaction platform; configuring a legal entity analysis engine, and converting the unstructured legal text into an entity-relationship-attribute triple which can be represented by a knowledge graph based on an ontology mapping technology; setting a financial data standardization component, and converting the financial data in different formats into unified semantic representation through a predefined financial index mapping rule; the knowledge fusion layer of the framework can integrate multi-source heterogeneous data such as laws and regulations, financial data, business processes and the like through a multi-source data access module, a legal entity analysis engine, a financial data standardization component and the like.

The invention relates to the technical field of transaction processing systems, and particularly discloses a trusted monitoring auditing system fusing TEE and a block chain, which comprises the following steps: generating a transaction instantaneous state abstract containing a key information field at a key node of a business process through a trusted execution environment, signing, anchoring a hash value of the transaction instantaneous state abstract to a block chain network, and storing the signature abstract under the chain; and during auditing, by comparing integrity verification of on-chain hash and under-chain abstract and TEE signature verification, dual authentication of transaction authenticity and environment reliability is realized. Through the collaborative architecture of the TEE and the block chain, the transaction core data state and the credible environment proof are deeply fused, so that the audit verification can be independently completed without depending on a centralized database, the problem that post-event tracing depends on the credibility of the log in the traditional audit is fundamentally avoided, and the audit verification efficiency is improved. And a lightweight credible verification basis is provided for a cross-mechanism cooperation scene.

The invention relates to the technical field of data analysis, in particular to a business abnormal data analysis method based on a financial management system, and the method comprises the following steps: S1, obtaining various business data in an enterprise financial management system in real time; s2, processing the data acquired in the step S1; s3, extracting features for reflecting business exception; s4, establishing an anomaly detection model; s5, comprehensively analyzing the source and influence of the abnormal data to form a multi-dimensional abnormal data analysis report; s6, deeply mining reasons behind the abnormal data, and identifying business processes and operations causing the abnormity; and S7, providing a corresponding decision support suggestion based on an analysis result in S6. According to the invention, through integration of data mining and machine learning technologies, the business abnormity identification, analysis and processing capabilities in an enterprise financial management system are effectively improved, the decision support of an enterprise for coping with a complex business environment is significantly enhanced, and the operation and financial risks are reduced.

The invention relates to the technical field of gas station equipment monitoring, in particular to a gas station equipment monitoring system and method based on the Internet of Things. The method specifically comprises the steps of collecting multi-dimensional operation parameters of equipment and business process node dynamics in real time, constructing a bidirectional coupling graph model of the equipment and business, and extracting high-order features of physical association and business logic interaction between the equipment by using a graph convolutional network; predicting equipment fault risks by adopting a dynamic health scoring mechanism, backtracking a risk propagation chain in combination with an attention mechanism, and mapping multi-dimensional data into interpretable fault diagnosis information through an abnormal semantic generation network; a double-layer reinforcement learning game mechanism is introduced, an inner layer coordinates equipment maintenance and a risk suppression strategy, an outer layer overall plans service efficiency and a safety target, optimization actions such as resource scheduling and oil product allocation are generated, and a perception-diagnosis-decision closed loop is formed. According to the method, full-time perception of the equipment state, active fault early warning and cross-domain collaborative optimization are realized, and the operation and maintenance efficiency and safety of the gas station are remarkably improved.

The invention provides an agent strategy generation and online optimization method based on dynamic scene perception, and the method comprises the steps: obtaining a scene demand description of a user, and carrying out the analysis of the scene demand description, so as to generate a target task sequence which can be executed by an agent disposed in a target scene; dynamically sensing the current environment characteristics of the target scene to generate a dynamic semantic topology network and generate a dynamic scene graph according to the dynamic semantic topology network; on the basis of the dynamic scene graph, hierarchical modeling of the incidence relation is carried out on the behavior space corresponding to the intelligent agent, and behavior semantic features containing scene perception are generated; the behavior semantic features containing scene perception are mapped to an intelligent agent strategy representation space, and a behavior feature strategy for controlling an intelligent agent to execute a target task sequence is obtained; and according to the determined scene value representation, decomposing the behavior feature strategy to obtain an advantage estimation value adapted to the scene so as to carry out online optimization on the behavior feature strategy. According to the invention, the intelligent agent strategy can accurately adapt to the requirements in the business process of an enterprise.

The invention relates to a multimodal transport end-to-end supply chain collaborative management method based on container logistics, and belongs to the technical field of supply chain management. The method comprises the following steps: virtually integrating scattered cargo owner demand, carrier transport capacity and transit point operation capability resources through a cloud computing platform to form a shared resource pool, and constructing a dynamic collaborative network; performing intelligent matching and scheduling on the shared resource pool through a contribution degree distribution mechanism to obtain a supply chain full-link state; dynamic routing optimization is carried out according to the full-link state of the supply chain, and optimal path selection is carried out on the transportation mode of container logistics based on the business process of multimodal transportation; a task is automatically decomposed through an intelligent contract and issued to a node, a carrier automatically uploads a voucher through an RFID gate after completing the node task, and the contract verifies the voucher and then triggers a next node task. Seamless connection and efficient collaboration among all nodes of the supply chain are achieved, and the transportation efficiency and reliability of container logistics are improved.

The invention discloses a mobile collaboration platform data management method, and relates to the technical field of data processing, and the management method comprises the following specific steps: S100, data sensitive grading: calculating sensitivity scores of various types of data in a mobile collaboration platform by using a formula. According to the method, the sensitivity scores of various data are calculated through a formula, and the data are divided according to the sensitivity levels, so that different levels of protection measures are taken for the data with different sensitivity degrees, and meanwhile, through a built-in sensor and a built-in behavior monitoring module, the sensitivity of the data is improved. The operation behavior information, the equipment information and the business process information of the user are collected in real time, the current business scene is intelligently judged, a basis is provided for subsequent privacy protection strategy making, the data processing efficiency is improved, the data security is greatly enhanced, and data leakage and abuse are effectively prevented.

The invention relates to the technical field of process management, and discloses a lightweight process management method, which comprises the following steps of: defining a business process and analyzing the business process through a process engine; a graphical process design interface is provided, and a user can dynamically configure business process nodes through dragging and clicking operations; the business process is disassembled into a plurality of task nodes, and each task node is packaged into an independent function module through modularization design; the task nodes are combined according to needs and executed in parallel on different processors, and efficient execution of the task nodes is ensured; the execution state of the business process is monitored in real time, and potential problems are found and processed in time through log recording and an exception processing mechanism. According to the technical scheme of supporting dynamic adjustment of the task nodes and optimizing parameters based on the historical abnormal mode, execution parameters of the task nodes are dynamically corrected, association logic between the nodes is adjusted in real time, and the effects that process management is flexible and adapts to complex service scenes, and operation stability is improved are achieved.

The invention provides a business process construction method combining a workflow engine and a dynamic form, which relates to the technical field of low-code platforms, and comprises the following steps of: establishing a data table according to a business, selecting and importing the data table in a code generation module, generating a basic code package, re-operating a main program compiling code package and loading the code package to a JVM (Java Virtual Machine) operating environment; creating a dynamic form on the basis of the entity class information of the code package through a visual form designer of the dynamic form module, and outputting dynamic form configuration in a JSON (JavaScript Object Notation) format; according to the method, the dynamic form and the process engine are combined, highly flexible business process design can be realized, a user can dynamically design the form and the process model as required to meet different business requirements, and the user-defined component can be designed according to a business data structure and common functions, so that the form can better adapt to a specific business scene, and the user experience is improved. And the user experience and the working efficiency are improved.

A distributed generative artificial intelligence (AI) reasoning and action platform that utilizes a cloud-based computing architecture for neuro-symbolic reasoning. The platform comprises systems for distributed computation, curation, marketplace integration, and context management. A distributed computational graph (DCG) orchestrates complex workflows for building and deploying generative AI models, incorporating expert judgment and external data sources. A context computing system aggregates contextual data, while a curation system provides curated responses from trained models. Marketplaces offer data, algorithms, and expert judgment for purchase or integration. The platform enables enterprises to construct user-defined workflows and incorporate trained models into their business processes, leveraging enterprise-specific knowledge. The platform facilitates flexible and scalable integration of machine learning models into software applications, supported by a dynamic and adaptive DCG architecture.

The invention relates to the technical field of data processing, can be applied to business scenes of financial science and technology, medical treatment and health and the like, and discloses a business process processing method, device, equipment and medium based on multistage tasks. Creating a main task record and a detail task record and establishing an association relationship, updating the state of the detail task record, periodically scanning the main task record and judging whether the detail task record is completed or not, detecting a process triggering request and executing a preset business process or updating the state of the main task record according to a detection result. Whether the preposed verification task is needed or not is judged in a unified mode before the business object is generated, the state of each preposed task is tracked by means of the incidence relation between the main task record and the detailed task record, and the precondition of process execution is ensured to be accurately met in combination with a periodic scanning and process triggering detection mechanism; the accuracy of business process execution is improved.

The invention relates to the technical field of data analysis, can be applied to business scenes of financial science and technology, medical treatment and health and the like, and discloses a business process visualization early warning and strategy response method, which comprises the following steps: setting a physical interrupt node to collect interrupt operation track data, generating multilevel logic interrupt type structured data based on the operation track data, the method comprises the following steps: acquiring a preset business strategy, mapping the preset business strategy into a visual topological graph, identifying a preset business strategy verification failure type, counting interruption frequency, triggering a grading early warning instruction, and generating a page element weight adjustment parameter and a fault-tolerant threshold set. According to the method, the visual topological graph is constructed and the high-frequency interruption behavior is identified, so that real-time identification and graded early warning of business strategy verification failure are realized, and adaptive adjustment of page elements and strategy thresholds is further driven, so that the user insurance conversion rate is effectively improved and the front-end operation loss risk is reduced.

A system for improving employee engagement and automating the integration of business processes. The system includes: a user interaction module configured to receive multimodal inputs, including natural language text and voice requests from employees via enterprise portals, mobile applications, voice-activated devices, and collaboration platforms, and to generate real-time responses via conversational output channels; a context processor that is operationally coupled with the user interaction module, wherein the context processor stores the interaction history, employee preferences, organizational role metadata, and task results in both short-term and long-term memory and dynamically derives context for controlling responses and initiating workflows; A natural language and intent processor that is communicatively linked to the context processor. The intent engine consists of large language models trained on company-specific lexicons to perform intent detection, entity extraction, ambiguity resolution, and sentiment or urgency classification from employee queries; a workflow orchestration layer in communication with the intent engine and the context processor, wherein the workflow orchestration layer includes a rule-based and AI-powered process execution engine configured to automatically initiate, route, and complete cross-functional business tasks, including approvals, escalations, and compliance checks, with workflows defined using modular templates that include conditional logic and time-based triggers; Webhooks and authentication protocols provide secure interoperability between the workflow orchestration layer and external enterprise platforms; a feedback and learning module that is operationally coupled with the workflow orchestration layer and the natural language understanding engine, wherein the feedback and learning module is configured to analyze task completion rates, response accuracy, latency metrics, and user feedback signals, and to retrain underlying language and workflow models for adaptive improvement in real time; and an administration console with role-based access controls, compliance dashboards, audit trails and interfaces for workflow configuration, whereby the administration console enables authorized personnel to monitor system operations, adjust interaction rules and enforce data protection restrictions across departments.

The invention relates to the technical field of business data insight, and discloses a method for disassembling insight business data through indexes. The method comprises the following steps: firstly, constructing a multi-dimensional index system, determining key business indexes and associated dimensions, and outputting an index disassembling framework; the method comprises the following steps: collecting multi-source data in a business process, performing cleaning and standardization processing, extracting index characteristics and dimension attributes, and constructing a business data association graph; introducing an adaptive weight distribution mechanism and a hierarchical iterative algorithm to optimize a preset attribution model, and generating a preliminary index contribution degree sequence based on an index disassembly framework; setting a monitoring threshold value of each dimension index, and tracking service data in real time; dynamically adjusting an index weight and a model parameter according to a tracking result, and recalculating and updating an index contribution degree sequence; and evaluating the service health degree, identifying abnormal index nodes, and starting a root cause analysis process for the abnormal index nodes. According to the method, multi-dimensional and dynamic insight of the business data can be realized.

The present application relates to the field of workflow management. Disclosed are a workflow automation rule library construction method and device, and a medium. The method comprises: acquiring historical business data respectively corresponding to multiple fields and, on the basis of the historical business data, acquiring corresponding ERP modeling data, ERP operational data and ERP business specifications; on the basis of a knowledge graph technology, generating corresponding check items; determining docking interfaces for a rule library and each check item in an ERP knowledge graph; classifying business workflow automation rules; and, on the basis of an operation of a user in the rule library, defining in the rule library a business workflow automation rule by means of the check items. Compared with a mode in which an expression is created by a human, the present application increases the degree of automation and reduces situations of rule omission. Compared with a mode in which a checking and verifying component is used, the present application can also define rules of complex business scenarios by means of relationships in knowledge graphs, so as to ensure the degree of automation and rule definition efficiency.

The invention provides a service scene collaboration-oriented cross-service system micro-service processing method and system, and the method comprises the steps: firstly obtaining an initial interaction data set with a service scene label of a cross-power service system in a preset time period, and then carrying out the scene feature analysis of the initial interaction data set; obtaining electric power business scene features and interactive behavior features corresponding to each interactive data unit, then calling a pre-constructed business collaborative analysis model to perform collaborative correlation analysis on the features, and generating a collaborative processing strategy containing micro-service calling priorities and data interactive format specifications; and determining a business process adjustment scheme of each business system in the target power business scene according to the cooperative processing strategy, and finally generating a cooperative execution instruction containing a micro-service calling instruction sequence based on the business process adjustment scheme, and sending the cooperative execution instruction to the target business system to trigger a cross-system cooperative processing operation. Therefore, barriers among power business systems can be effectively broken, the cooperative processing efficiency and flexibility are improved, and the reliability of power supply is guaranteed.

Disclosed in the present invention is a fully automated log analysis and fault handling system and method based on an NLP large model. The method comprises: collecting a service state, an operating environment, and log files; extracting and generating log summaries from the log files, extracting key information from the log summaries to obtain state information of a service process; if an error is fed back, the NLP large model performing fault analysis on the error by using the service state and the operating environment as prior knowledge, and recording upstream and downstream key information involved in the error; on the basis of the upstream and downstream key information involved in the error, finding a fault cause and providing a fault handling action; and then executing a plan on the basis of the fault handling action. Log collection of the present invention is not limited to a prescribed format, and an NLP large model using a service state and an operating environment as prior knowledge is introduced for fault analysis.

The invention relates to a business process processing method and device, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: receiving a business process processing request; the business process processing request carries a business variable and an operation instruction; according to the operation instruction, executing a node task in a to-be-executed business instance corresponding to the business process processing request; the to-be-executed service instance is generated after an initial service instance executes at least one node task; the initial service instance is created through a pre-configured process engine; obtaining an execution path of the to-be-executed service instance according to the service variable; the execution path is used for determining the circulation trend of the to-be-executed service instance. By adopting the method, the execution efficiency can be improved.

The invention provides a digital asset-oriented network security management method and system. The method comprises the following steps: acquiring a digital asset list and a business process document, identifying keywords in the document by using a natural language processing technology, constructing an association relationship graph of digital assets and business processes, and discovering potential association; directly connected business process nodes of each digital asset are extracted from the incidence relation graph, an asset business influence range report is generated, and a many-to-many mapping relation between the assets and the businesses is displayed in a matrix form; in the review process, association and quantitative indexes, including a risk thermodynamic diagram, an influence degree scatter diagram and the like, among assets, risks and business processes are dynamically presented through a visualization technology, and a visual information transmission channel is provided for all parties; and after meeting review is consistent, qualitative influence analysis of the business department and quantitative loss estimation of the technical department are integrated into a business influence analysis result by adopting a fuzzy comprehensive evaluation method, and a comprehensive business influence level is obtained.

The embodiment of the invention provides a big language model training method and device, and aims to enable a big language model to have the capability of processing complex services and train the reasoning capability of the big language model. Training is carried out in two stages. In the first stage, a thinking chain is used as supervision fine tuning of a supervision signal, and in the process, the thinking chain can be determined by adopting generation, evaluation and correction modes of a fine-grained single action, so that the flexibility and depth of a reasoning path are improved. The second stage is a reinforcement learning stage, model rewards in the reinforcement learning process comprise correctness rewards and length rewards, the big language model is encouraged to generate a longer and reliable reasoning path, and reward abuse is avoided. According to the scheme, the processing reliability and accuracy of the large language model suitable for complex services can be improved.

The invention relates to the technical field of graph analysis, in particular to an intelligent counter semantic interaction system based on a knowledge graph, which comprises a subgraph generation and matrix analysis module, a semantic analysis module and a node mapping module, and the dynamic adjustment factor dynamically adjusts the activation priority of the service node based on the historical jump path frequency, and generates a service flow time sequence priority. According to the method, the dynamic regulation factors and the real-time session features are coupled, so that triple constraint collaborative optimization of association degree scoring, time sequence compliance and resource scheduling is innovatively realized; dynamically cutting the knowledge graph based on the session state vector, and eliminating redundant node interference; the service flow time sequence matrix is combined with historical path attenuation statistics and real-time semantic features, and it is ensured that the node activation priority accurately adapts to service scene changes.

The invention discloses a customer service robot system integrating knowledge base question and answer retrieval and work order processing, which comprises a natural language understanding and generating module, a knowledge retrieval module, a business system integration module, a dialogue management and decision module and a cue word management module, and relates to the technical field of artificial intelligence. Through cooperation of a large language model and a dialogue management module, the system realizes multiple rounds of deep understanding of a natural language, and can actively guide a user to clarify a demand, such as gradually inquiring an equipment model and fault details during repair, memorizing a dialogue context and avoiding information omission. Compared with traditional regular customer service, the method has the advantages that the accuracy of understanding complex problems is remarkably improved, open dialogue scenes such as technical consultation and fault diagnosis are supported, user interaction experience is closer to manual service seats, and the problems that multi-round dialogue understanding is limited, progress data cannot be obtained in real time, multiple knowledge bases are fused, and the automation degree of the business process is low are solved.

The invention discloses a power marketing knowledge graph construction method, system and device and a storage medium, and relates to the technical field of power system data management. At present, integration and application of multi-source heterogeneous data are difficult, data anomaly detection and treatment efficiency is low, and data circulation is unstable. The method comprises the following steps: collecting data of a plurality of sources, and carrying out data cleaning and standardization processing; classifying the data according to structuralization, semi-structuralization and non-structuralization, and forming feature vectors through a feature extraction method; extracting knowledge units from the feature vectors by using natural language processing and machine learning technologies, fusing knowledge from different sources through rules or models, and constructing a unified knowledge base; generating a knowledge graph in a graph database by using a knowledge base, and constructing a graph structure of nodes and edges; and carrying out real-time monitoring on data flow in the knowledge graph, and analyzing path efficiency. According to the technical scheme, the data management efficiency is improved, data circulation is monitored in real time, and the business process is optimized.

The invention discloses a task processing method and system applying robot process automation, and relates to the technical field of robot process automation, and the method comprises the steps: S1, carrying out the priority evaluation of tasks in a task queue based on a preset rule, S2, sorting the evaluation results to generate a priority task queue, s3, allocating execution resources to the tasks according to the priority task queues; S4, monitoring task execution states and dynamically adjusting the priorities of the task queues; according to the task processing method and system applying robot process automation, task scheduling requirements in complex and changeable business scenes are met, efficient operation of the system in a dynamic environment is ensured, key tasks are ensured to be completed on time, continuity and stability of a business process are improved, business risks are reduced, and the task processing efficiency is improved. The problem that how to regulate and control the priority of the task queue to solve the service influence caused by the fact that key tasks are preempted by low-priority tasks is solved.

The invention discloses a zero-code development platform and method, electronic equipment and a medium, and belongs to the field of software development. The zero code development platform comprises a data protocol layer, an equipment layer, an operator and module library, a business logic layer and an application layer, the data protocol layer manages an industrial control protocol and a database system; the equipment layer accesses and manages hardware resources; the operator and module library provides a function operator, a resource component operator and an HMI module for calling; the business logic layer calls operators and modules in a module library in a graphical mode through a designer to construct a business task process; and the application layer provides a running environment for the business process and executes the process. According to the zero-code development platform, the development method, the electronic equipment and the medium provided by the invention, efficient development and management of complex industrial automation applications can be realized through a multi-layer architecture design, the overall performance of a system and the development experience of a user are improved, the development process is simplified, and the development period is shortened.

The invention provides a business arrangement scheduling method based on event driving, which comprises the following steps of: constructing a unified business arrangement framework, describing a business process by using a unified process definition language, and defining a process metadata standard; adding a general process engine with a plug-in mechanism in the business arrangement framework; establishing a real-time event processing architecture by taking an event as a core, and adding an event-driven business process engine; constructing a monitoring platform on the basis of the real-time event processing architecture, performing real-time monitoring on an event stream and event processing, and performing visual display; a unified retry mechanism is introduced, and a retry strategy and retry management are defined; according to the method, a unified, efficient and easy-to-monitor and easy-to-maintain business arrangement framework is established, so that the scheduling process is conveniently arranged through event-driven business, the endpoint retry and business flow monitoring capability is provided, the realization difficulty of monitorable and retry in the business is simplified, and the robustness of the business is improved.