287 results about "Workflow engine" patented technology

The invention relates to a dynamic data pipeline construction method based on artificial intelligence and multi-modal data processing, and belongs to the technical field of data fusion treatment and artificial intelligence. According to the method, a multi-modal deep learning model is adopted, semantic analysis is performed on business documents, user requirements and function specification instructions, and key indexes, data requirements and logic constraints are extracted; constructing a multi-modal knowledge graph, performing unified representation and feature extraction on structured and unstructured data resources, and performing embedded learning by using a graph neural network; based on a deep semantic matching model and a hybrid recommendation system, realizing accurate matching of business requirements and data resources; modeling generation of a data processing flow into a Markov decision process by adopting a reinforcement learning strategy, and automatically generating an optimized data processing workflow; the adaptability of the model to new business requirements is improved by utilizing a meta-learning algorithm; and dynamic scheduling and execution of tasks are realized through a workflow engine. According to the invention, the data processing efficiency can be improved.

The invention provides an intelligent agent automatic generation and scheduling system based on an artificial intelligence large language model. The system comprises an intelligent agent configuration module driven by the large language model, a workflow engine and automation module and a front-end and rear-end integration module. The agent configuration module driven by the large language model realizes automatic configuration of an agent and a workflow based on the large language model, and comprises a large language model interaction layer and an agent configuration module; the workflow engine and automation module is responsible for execution logic of workflow, supports multi-task parallel processing and comprises a workflow engine module and a task scheduler; the front-end and rear-end integration module realizes cooperative work of a JavaScript front end and a Python Django rear end by designing a unified data interface, the front end is responsible for interacting with a user and displaying an execution state of a workflow, and the rear end provides service logic processing and data persistence.

The invention relates to the technical field of artificial intelligence, in particular to an agent dynamic decision network generation method based on reinforcement learning. The method comprises the following steps: receiving information demand data input by a user; performing feature analysis on the information demand data, and extracting a business target, a constraint condition and a key parameter to obtain an information demand analysis result; identifying a task flow corresponding to the information demand analysis result, and matching an API call chain according to the task flow to obtain a task demand technology blueprint; generating an agent workflow according to the task demand technology blueprint by using a preset dynamic workflow engine; performing context analysis on the language demand analysis result to obtain context information; the agent workflow is divided into ultra-long thinking chains based on context information. In conclusion, through the reinforcement learning technology, the intelligent agent can be automatically generated and continuously optimized according to user requirements, and efficient decision making and dynamic adaptation of complex service scenes are supported.

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.

The invention belongs to the technical field of big data analysis, and discloses a supplier price management system and method based on big data analysis, and the method comprises the steps: collecting core data and auxiliary data, and constructing a multi-dimensional data cube; dynamically calculating a grid availability index, forming a dynamic space-time grid reference library, and synchronously generating a supply chain topological graph; receiving and quantifying a business target instruction, and selecting a path through a rule engine; generating purchase strategy suggestions with confidence rating and a dynamic priority supplier list; the authority is dynamically adjusted through an ABAC model, an intelligent contract engine is triggered, and an order or bargaining invitation is generated; parallel approval is carried out through a workflow engine, and a purchase order set and a permission change log are generated; receiving a real-time data stream of the Internet of Things and a historical case library, and triggering a disposal logic; and matching similar historical cases, pushing a historical optimal strategy, synchronously updating a weight rule, a data cube structure and a supplier credit sub-threshold, and forming full-link closed-loop iteration.

The invention discloses a self-adaptive intelligent symbiotic data ecosystem and a cooperative implementation method thereof. The system comprises an intelligent agent ecosystem module, a self-adaptive domain knowledge graph module, a self-adaptive workflow engine module, a CLID configuration system module, a man-machine cooperation interface module, a data processing pipeline module and an innovation trigger module. Through cooperation of diversified intelligent agents, the dynamic knowledge graph, the self-adaptive workflow and CLID multi-dimensional configuration, high-flexibility and self-adaptive data processing and analysis capacity is achieved. The system can automatically construct a complex workflow, perform multi-modal data fusion, and integrate human judgment and AI capabilities at key points, thereby providing an innovative solution. The method is suitable for complex and dynamic big data processing and analysis scenes, and the data processing efficiency and innovation ability are remarkably improved.

The invention provides an entity link-based multi-source information association system and method in an agricultural scene, and belongs to the technical field of agricultural artificial intelligence, and the system comprises a data analysis module which constructs a multi-modal data analysis layer for data analysis, converts the analyzed data into a knowledge unit in a preset format through a Converter component, and stores the knowledge unit in the preset format; retaining an original semantic hierarchical structure and generating knowledge association anchor points; the graph construction module is used for constructing a cross-modal knowledge graph by utilizing knowledge association anchor points and an entity linking technology; the graph retrieval module is used for retrieving the cross-modal knowledge graph on the basis of a GraphRAG hierarchical retrieval technology; the result optimization module is used for optimizing the retrieval result to obtain an optimized retrieval result; and the result processing module constructs a low-code workflow engine based on the optimized retrieval result, further constructs an agricultural knowledge processing assembly line, and executes the agricultural knowledge processing assembly line to obtain an executable working scheme. And the processing efficiency of agricultural knowledge is improved.

The invention discloses a large-model-driven intelligent calculation method and system for a water conservancy mechanism model. According to the method, natural language input, structured conversion and intelligent optimization calculation of a scheduling target are realized by integrating a field-enhanced large language model and a water conservancy professional mechanism model. The method comprises the steps of receiving a calculation target expressed by a user in a natural language, and analyzing and converting the calculation target into a constraint condition and a target function which can be recognized by a water conservancy mechanism model; a hydrological model, a hydraulic model, a hydrodynamic model and other models are called based on a workflow engine, and reverse calculation is carried out by adopting a hybrid optimization strategy of'coarse adjustment-fine adjustment-verification '; synchronously and visually displaying the parameter change and the result convergence state in the calculation process; and outputting a calculation result including parameter adjustment logic, standard conformity analysis and multi-scheme comparison. The system comprises a natural language interaction module, a target conversion module, an intelligent calculation engine module, a visualization module and a result generation module, and supports multiple application scenes such as multi-target scheduling, emergency decision making and ecological guarantee. Compared with a traditional scheme, the method has the advantages that the model use threshold is lowered, the dispatching efficiency and calculation transparency are improved, and the method is suitable for complex hydraulic engineering calculation tasks such as reservoir dispatching, cross-basin water transfer and flood control emergency.

The invention provides an industrial fire-fighting emergency method based on visual edge calculation. The method comprises the following steps: synchronously acquiring visual data, thermal data and gas data at the edge end of a factory; processing and feature extraction are respectively carried out on the collected data to obtain visual features, thermal features and gas features, after the visual features, the thermal features and the gas features are fused to obtain multi-modal fusion features, whether a fire happens or not is predicted, and if yes, a fire prediction picture is generated; presetting a time sequence analysis model, generating a fire emergency task flow according to a result output by the time sequence analysis model, dividing the priority of the fire emergency task flow, and submitting the fire emergency task flow to a workflow engine to distribute the fire emergency task flow to a corresponding execution unit; and on the basis of the steps S11 to S14, collecting multi-modal data of different factories, and pre-training corresponding fire emergency models. According to the invention, by synchronously collecting and fusing the multi-modal data of the treatment plant area, the corresponding industrial fire-fighting emergency scheme of the plant area is generated, and the fire-fighting emergency response time is remarkably shortened.

The invention discloses an intelligent operation and maintenance method for modular data acquisition and analysis sewage treatment equipment, which comprises the following steps: designing a modular data acquisition unit, configuring an acquisition module with a standardized interface, and realizing quick access of multi-source equipment; protocol conversion, data compression and timestamp completion are completed by using an edge computing gateway, and stable data reporting under a complex network is realized by combining an MQTT protocol; a time sequence database is adopted to store and fuse OA / ERP system data, and an information island is broken; remote diagnosis, intelligent early warning and visual decision making are realized by means of a remote fault analysis interface, a user-defined rule engine and a configurable billboard; and operation and maintenance closed-loop management is achieved in cooperation with a workflow engine. The method effectively solves the problems of low data acquisition integration level, unstable communication, weak analysis capability and the like in traditional operation and maintenance, has the characteristics of modularization, intelligence, high efficiency and the like, remarkably improves the operation and maintenance management level of the sewage treatment equipment, reduces the operation and maintenance cost, and has a wide application prospect.

The invention provides a multi-modal education evaluation system and method based on super-automation and federal cognitive calculation, and the system integrates multi-source heterogeneous data collection, a federal cognitive calculation engine, an automatic evaluation workflow engine and a privacy enhanced data processing architecture. And comprehensive acquisition, efficient processing, accurate evaluation and safety protection of education data are realized. Through the LSTM-Bayesian hybrid model of dynamic parameter adjustment, intelligent feature engineering, interpretability analysis, reinforcement learning strategy optimization and other technologies, the system can generate personalized learning path planning and an adaptive teaching intervention scheme, and the teaching effect and learning efficiency are improved. And meanwhile, the security and privacy of the education data are ensured by adopting privacy protection technologies such as differential privacy and homomorphic encryption.

The invention relates to a large model business logic processing method and system based on workflow engine and domain knowledge fusion, and is suitable for automatic processing of complex multi-node and multi-branch business processes. According to the method, natural language input of a user is analyzed through a large language model, a service intention is recognized, and the service intention is converted into an executable task process through a workflow engine. And the workflow engine dynamically adjusts an execution path according to rules and data in the domain knowledge graph to realize efficient parallel processing of tasks. The system integrates a workflow engine, a domain knowledge graph and a large language model, supports real-time data processing, rule matching, conflict resolution and decision optimization, is suitable for the fields of water conservancy, medical treatment, finance and the like, automatically generates and executes a complex business process, and optimizes a decision process. The system improves the accuracy and execution efficiency of business decision through deep fusion of domain knowledge.

The invention discloses a business and financial integrated management system, which constructs a high-elasticity and easy-to-expand business and financial integrated management system through deep fusion of cloud native and micro-service architecture. The system realizes real-time acquisition and cleaning of business and financial data through a standardized data interface and an ETL technology, and data islands are thoroughly broken; a business process modeling tool cooperates with a workflow engine, financial management and control nodes are embedded into the whole business process, and the real-time performance of budget execution and cost collection is ensured; the intelligent analysis module combines OLAP and machine learning, provides multi-dimensional data insight and risk prediction, and significantly improves decision scientificity. In addition, due to the distributed log and role grading permission design, the safety and auditing performance of the system are enhanced. The system not only reduces the manual operation error rate and IT operation and maintenance cost, but also assists an enterprise in realizing refined operation and agile decision making through an automatic process and an intelligent early warning mechanism.

The invention relates to the technical field of equipment management, in particular to an equipment full-life-cycle digital twinborn decision-making system enabling a large model. Comprising a digital twin modeling unit; a large model enabling analysis unit, wherein the large model enabling analysis unit adopts a hydroelectric equipment multi-modal causal constraint analysis model; a whole-process closed-loop management and control unit; and an intelligent decision output unit. According to the method, the multi-modal causal constraint analysis model adaptive to the working condition of the hydroelectric equipment is constructed, and a causal chain verification backtracking mechanism is introduced, so that the problem that the reasoning result lacks logic verification is effectively solved, the logic consistency of a fault reasoning conclusion is guaranteed, and the reliability of decision output is improved; through a scene adaptation mode of'pre-training + fine tuning 'of a large model, multi-modal feature fusion processing and deep linkage of a workflow engine and a digital twinborn body, full-life-cycle management requirements of equipment are fully covered, and the refinement and intelligence level of hydroelectric equipment management is further improved.

The invention provides a machine vision solution automatic generation method based on a large language model. The module comprises a workflow agent, a parameter agent, an algorithm agent, a workflow engine and a pre-implementation operator library. According to the method, many problems in a traditional workflow-based machine vision solution construction process are solved to a great extent, including that workflow construction highly depends on personal experience of engineers, algorithm parameter performance is sensitive and difficult to adjust, and an application iteration debugging period is long. Besides, the method provided by the invention has multiple flexible deployment modes, the method can be deployed in carriers such as a client field industrial personal computer, a client field private cloud server, a public cloud server and the like, and a machine vision solution automatic generation task is completed. The solution deployment efficiency of a machine vision provider is greatly improved, and the personnel cost of the machine vision provider is reduced; the method is of great significance in improving the efficiency of the last kilometer of a machine vision industry chain.

The invention relates to a field information analysis method and system based on RAG and a knowledge graph, and belongs to the technical field of artificial intelligence, and the system comprises a multi-source heterogeneous data collection module which is used for collecting field-related original data; the intelligent document processing engine is used for carrying out cleaning and structured processing on the data and extracting entities, relationships and events; the multi-source knowledge graph construction and management module is used for constructing and fusing multi-sub-field knowledge graphs to form a unified knowledge graph; the RAG retrieval enhancement generation dynamic fusion sequencing module is used for receiving user query, performing multi-channel information retrieval by querying a text vector index and a knowledge graph, performing fusion resequencing on a result, and generating an analysis result by utilizing a large language model; and the agent workflow engine is used for decomposing query into subtasks and scheduling professional agents to cooperatively complete analysis and report generation. Deep semantic understanding, intelligent retrieval and automatic analysis of domain information are realized, and the accuracy and efficiency of information processing are improved.

The invention provides an intelligent contract driven workflow engine automatic execution method and system based on a block chain. The method comprises the following steps: constructing a knowledge graph through a historical workflow task, and identifying an association rule between a task type and a resource demand; deploying a monitoring agent at an edge computing node, collecting node load data, and interacting with the block chain smart contract through an encrypted data channel; when a workflow trigger event is detected, a resource pre-allocation strategy is generated in combination with the knowledge graph and the node load data; the block chain intelligent contract verifies the matching degree of the strategy and the current node resource, and after verification is passed, an encryption allocation voucher is generated and written into a block chain account book; the intelligent contract triggers the workflow engine to start the container instance group at the target node according to the voucher, and the task is automatically executed. According to the technical scheme, efficient and safe edge computing resource allocation and workflow automatic execution are achieved, and the resource utilization rate and the task execution efficiency are improved.

The invention discloses a scheduling and approval process control method and device for digital product online leasing, and the method specifically comprises the steps: carrying out the cloud native operation environment preparation, building a process control system based on a workflow engine capable of being arranged, carrying out the system initialization, and carrying out the operation of a cloud server; comprising the steps of initializing a lease process engine, an equipment resource scheduling module, an approval strategy and credit engine and a performance monitoring and state management system; and then a lease application and process control method, an equipment scheduling and conflict detection method, an examination and approval execution and process driving method and a performance monitoring and state write-back method are designed in sequence. After a user submits a leasing request, generating an approval process and distributing the approval process to an approval node; executing resource allocation and locking after the approval is passed; an event-driven mechanism is adopted to manage state circulation of all stages of leasing, and reverse-driven scheduling adjustment of approval results is supported. According to the invention, the approval efficiency of online renting of digital products is improved, the manual intervention cost is reduced, and the rationality and traceability of equipment resource configuration are guaranteed.

The invention relates to the technical field of artificial intelligence and decision support, and discloses a large model agent-based strategic decision generation method, which comprises the following steps of: performing semantic analysis and multi-target game balance on a strategic instruction by a main control agent, and decomposing to generate subtasks; a corresponding domain agent calls an RAG enhancement module to retrieve related multi-modal data, and a preliminary strategy of embedded compliance verification is generated based on the data; and finally, dynamically arranging and coordinating the preliminary strategy by a hierarchical workflow engine, and generating a comprehensive decision report after receiving strategy correction information of a commander. According to the invention, through multi-agent cooperation and multi-target game balance, the decision-making globality and strategic depth are realized; by fusing dynamic multi-modal data, the adaptive capacity of the decision to the external environment is improved; through workflow arrangement and man-machine cooperation, logic self-consistency, execution feasibility and safety of a final scheme are ensured.

The invention provides a hypercomputing cluster-oriented workflow engine system and task scheduling method, and the system comprises a drag-and-drop arrangement design module which is used for providing a visual workflow arrangement environment so as to respond to a drag-and-drop arrangement operation of a user to obtain a target workflow; the target workflow comprises a plurality of tasks and a dependency relationship among the plurality of tasks; the virtualization task packaging and adaptation module is used for packaging a plurality of tasks in the target workflow by utilizing a unified packaging mechanism to obtain a plurality of target containers; and the workflow automatic execution module is used for managing and distributing hardware resources in the super computing cluster so as to schedule and execute tasks in the target containers according to the dependency relationship. Through the system provided by the invention, the complex workflow can be efficiently executed and managed in a super computing cluster environment.

The invention discloses an agent building method based on dynamic workflow, and relates to the field of artificial intelligence and automatic process management. Comprising the steps of 1, creating an agent building system of the dynamic workflow, 2, creating a workflow node through an agent cooperation module, and enabling the workflow node to achieve collaborative decision through a standardized communication protocol, and 3, recording context information through a real-time state sensing function module by means of a shared memory, the method comprises the following steps: step 1, sending out a command for adjusting an execution sequence of workflow nodes and sub-flows of some workflow nodes according to context information, and realizing cross-workflow node data flow by adopting a standardized message format, and step 4, carrying out insertion / deletion operation on the workflow nodes through a dynamic workflow engine module when the workflow nodes run according to the command, so as to realize cross-workflow node data flow. And step 5, integrating reinforcement learning and a rule engine through a dynamic strategy controller module, formulating an abnormity self-healing strategy, presetting a threshold rule, and automatically injecting a compensation workflow node or switching a standby sub-process in combination with a time sequence prediction model.

The invention discloses a task scheduling method based on dynamic weight, and belongs to the technical field of workflow engines and distributed computing. The method comprises the steps of establishing a to-be-allocated task set; calculating a dynamic priority weight; establishing a task queue; sequentially processing the tasks according to a task queue sequence; establishing a calculation node set of all available processing tasks, and screening out an optimal calculation node corresponding to a to-be-processed task in the task queue; judging whether the to-be-processed task is partitioned or not; establishing a task preemption condition until the to-be-processed task is processed; and repeating the steps until all tasks are processed. The method can be applied to cloud computing, intelligent manufacturing and distributed system task scheduling scenes, and the resource utilization rate and task completion timeliness are remarkably improved.

The invention discloses a dynamic coupling modeling method and system for an electric power information physical system, and belongs to the technical field of electric power dispatching automation, and the method comprises the steps: analyzing an AGC control process business data flow, and constructing a workflow engine; performing node classification, generating an information node model and an information branch model of the information system, and constructing a rule engine; constructing an information system network matrix model based on the rule engine, and establishing a preset service strategy based on the information system network matrix model; constructing an information physical coupling model of the AGC control process; and according to the AGC actual service, loading a preset service strategy established by the information system network matrix model based on the synergistic effect of the workflow engine and the rule engine, and updating the information physical coupling model. According to the method, a basic model is provided for cross-level closed-loop simulation verification in the AGC process, the problems of unclear information physical dynamic coupling mechanism, insufficient service logic adaptive mapping and model iteration self-updating are solved, and a guarantee is provided for safe and stable operation of a large power grid.

The invention relates to the technical field of data recovery, and discloses a data recovery method and system based on a multi-cloud platform, and the method comprises the following steps: S1, constructing an intelligent data acquisition layer: carrying out the dynamic adaption of four data collection channels based on a multi-modal data recognition engine; s2, constructing an adaptive transmission protocol stack: dynamically selecting based on a network quality real-time evaluation model; s3, heterogeneous system architecture recovery: realizing architecture recovery through a virtual environment abstraction layer; s4, establishing a space-time dimension data management model, which is realized based on a cloud native storage architecture; and S5, automatically recovering an arrangement engine through a workflow engine. According to the method, the reliability and the stability of the system are improved while the labor cost is saved, high-frequency copy synchronization is supported, the resource utilization rate is improved, and the data reading efficiency can be improved through block-level data synchronization.

The invention relates to the technical field of information management systems, in particular to a full-process intelligent office system which comprises a data layer, an AI engine layer, a service engine layer, an application layer, an interface layer, a central data bus, a unified identity authentication center, a safety protection center, a system monitoring center, an intelligent workflow engine (1000), a real-time notification pushing mechanism and a multi-terminal adaptation framework. Data barriers of all departments of an enterprise are broken through through the AI technology, full-process data intercommunication is achieved, enterprise management forms a complete closed loop, and the management complexity and the information island problem are reduced; all functional modules are coordinated through a central AI agent, and data consistency and business process continuity are ensured. By realizing the full-process digital management of the enterprise, the system obviously improves the office efficiency of the enterprise, reduces the management cost, enhances the competitiveness of the enterprise, and is particularly suitable for the intelligent operation and management requirements of small and medium-sized enterprises and entrepreneurship teams.

The invention discloses an electronic medical record intelligent coding method, device and system, and a storage medium. The method comprises the following steps: respectively carrying out entity extraction, feature coding and homomorphic encryption transmission on an electronic medical record text, a medical image and structured inspection data; according to the preprocessed data, performing multi-modal feature alignment, ICD semantic mapping and real-time medical insurance rule injection through a dynamic knowledge fusion coding engine; generating a three-dimensional visual evidence chain of the text evidence, the image evidence and the policy evidence through the interpretable traceability matrix; through a multi-role cooperative workflow engine, generating a code according to a confidence threshold routing AI, and auditing the code to a clinician or a professional coder; rare disease data are synthesized through an adversarial training enhancement module, and policy mutation is simulated for model adaptability training. By adopting the technical scheme of the invention, the problems of multi-modal data splitting, policy lag and non-traceability in the prior art are solved.

The invention discloses a cross-system data collaborative driving workflow full-link approval method and system, belongs to the technical field of workflow management, and aims to solve the technical problem of how to realize cross-system data collaborative driving and cover a workflow full-link approval method and process. Comprising the steps that a system architecture composed of a unified workflow engine, data collaboration middleware and a plurality of service systems is built; drawing a cross-system workflow full-link approval flow chart in the unified workflow engine; by means of data collaboration middleware, a collaboration mapping relation of all service systems is established, and data format conversion, extraction and integration operations are automatically executed in process execution; and the unified workflow engine generates a process instance, notifies a service system of a subsequent approval node through the data collaboration middleware, sends an approval task to the corresponding approval node, transmits related data through the data collaboration middleware, notifies related personnel, and records various information in a full-link approval process execution process in real time.

A ML-based method and system for automatically generating responses to inputs using a multi-tier technique, is disclosed. Initially, first inputs are obtained from electronic devices of first users. An information is retrieved from external sources based on first inputs, using RAG engine. The responses are generated based on optimized information, using ML model. The ML-based method determines whether queries of first users within first inputs are resolved through the optimized responses. Issues are categorized using ML-based issue classification engine when the queries are not resolved. Dynamic forms are generated with fields based on categorization of issues, using a dynamic workflow engine. Second inputs are obtained and processed to determine whether queries are resolved using a rule-based logic engine. The first users are adapted to interact with second users to resolve the issues through communication channels. The responses are provided as output upon resolving the issues, to first users.

The invention belongs to the technical field of product production cycle management, and particularly relates to an electronic image-text file remote multi-bin collaborative storage method based on a PLM. Comprising the steps that a 1 + N hybrid storage architecture is constructed, one center bin is deployed in an enterprise headquarter data center and stores full document metadata and latest version files, and N area bins are deployed according to geographic areas and store cache copies and incremental update data of local high-frequency access documents; the remote multi-warehouse document increment synchronization is realized through an intelligent synchronization engine, and the intelligent synchronization engine adopts a content awareness increment synchronization algorithm and a dynamic synchronization strategy based on network quality; version management is realized through a version cooperative control module, and the version cooperative control module is used for establishing a global version number mapping mechanism and a conflict resolution decision tree; cross-warehouse process automation is realized through a cross-warehouse workflow engine, and the cross-warehouse workflow engine integrates a BPM process engine and a message queue; the method can adapt to a remote network environment, and the large file synchronization efficiency is improved.

The invention relates to the technical field of informatization, in particular to a workflow engine implementation method based on event traceability and decentralized monitoring, which is based on the design of recording node operation and deducing a process state on the basis of an immutable event sequence, thoroughly relieves the constraint of traditional state table storage on dynamic adjustment, and improves the working efficiency. Operation such as node insertion, skipping or rollback can take effect in real time through runtime metadata reconstruction, meanwhile, a completely stored operation event chain naturally supports full-process audit tracing, and a log system does not need to be additionally developed; a power proportion algorithm decomposes distributed node decisions into three layers of logics of role allocation, predefined result verification and secondary monitoring triggering, cross-service consistency is guaranteed through a post weight adjustment and historical behavior voting dual mechanism, and an online voting mechanism dynamically calculates and processes human weights by using event logs, so that the decision accuracy is improved. Therefore, the technical bottlenecks of rigidity, redundancy and weak consistency of the traditional scheme are overcome.