2092 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 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 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.

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 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 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.

The invention relates to a business travel data processing system and method based on a large model and an intelligent agent and a medium, and the system comprises a natural language interaction and structured data conversion module which is used for receiving unstructured business travel text information inputted by a user, and carrying out semantic analysis, entity extraction and context association through a multi-modal intention recognition engine, converting the data into structured travel data; the agent-driven dynamic process adaptation module is used for dynamically adapting the approval process and the system operation logic according to the request input by the user and the business process state; the multi-dimensional intelligent compliance verification module is used for verifying the structured travel data; the cross-system collaboration and data communication module is used for realizing automatic synchronization and data consistency guarantee of the travel data and the heterogeneous system; and the system architecture and key technology integration module is used for providing layered architecture design, large-model lightweight deployment and security and privacy protection of the system. The method has the advantages of being high in efficiency, high in adaptability, low in compliance risk, good in collaboration and the like.

Orchestration solutions for agentic automation for artificial intelligence (AI) agents, robotic process automation (RPA) robots, agentic orchestration processes (AOPs), and humans-in-the-loop with self-healing and capabilities to increase autonomy. Agentic orchestration, via a conductor application, enables the automation, modeling, and monitoring of complex business processes from start to finish. A user or an initiating software process requests that a given AOP be run. The AOP defines a business process (e.g., in Business Process Model and Notation (BPMN)), and may utilize one or more RPA robots, one or more AI agents, and / or one or more humans to assist with accomplishing this process, such as resolving invoice discrepancies, onboarding an employee, performing compliance checks, synchronizing order data among multiple backend systems, etc.

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 embodiment of the invention provides an RPA process generation method and device based on a large model, a medium and a product. The method comprises the following steps: receiving business demand information in a natural language form input by a user; based on a preset large model and a business capability knowledge base, analyzing the business demand information, and determining a business operation sequence matched with the business scene; based on a robot process automation (RPA) component knowledge base and a preset multi-modal visual understanding model, analyzing the service operation sequence into a single-step executable atomic operation sequence; and according to the atomic operation sequence and the large model, generating an executable code of the target RPA component. According to the scheme, a user is supported to input business process requirements in a natural language form, the requirements are automatically converted into the business process operation sequence based on a large model, and the corresponding RPA code is generated and executed, so that the understanding cost of the user is greatly reduced, the execution efficiency of the business process is improved, and the labor cost is reduced.

The invention relates to a standardized project management and intelligent professional scheduling system, and belongs to the technical field of project management and human resource intelligent scheduling. The project standardization module is disassembled into standardization task steps through a business process association rule mining algorithm, and project complexity and resource tensity are adapted by using a task attribute dynamic weight algorithm; the archive matching module constructs professional archives, extracts feature vectors through a multi-criterion decision-weighted bipartite graph matching algorithm, and generates optimal matching pairs in combination with adaptive weight adjustment and an integer linear programming model; the dynamic scheduling module plans a task execution scheme according to a resource constraint scheduling mechanism, and realizes efficient scheduling in combination with a skill supply and demand prediction algorithm and calendar integration; and the quality optimization module adopts a deliverable anomaly detection algorithm to monitor compliance, feeds back an iterative matching and scheduling strategy through a time sequence prediction optimization algorithm, and perfects a skill map based on a map increment updating algorithm. The system realizes a project full-process closed loop.

The invention discloses an enterprise production management method based on digital twinning and workflow simulation, and particularly relates to the technical field of industrial internet and intelligent manufacturing, and the method comprises the steps: constructing a physical production system digital twinning body and business process workflow model, and carrying out the dynamic association through a model fusion engine to form an integrated digital twinning model; real-time event driving is used for deducing and simulating a future production process, and bottleneck and conflict prediction is output; based on the prediction result, utilizing a multi-objective optimization engine to generate a plurality of alternative scheduling schemes; performing parallel simulation quantitative evaluation on the KPI of each scheme, selecting an optimal scheme, analyzing the optimal scheme into a control instruction, and issuing and executing the control instruction; and model self-correction and closed-loop optimization are realized through real-time monitoring and feedback. According to the method, the problem of service and physical state disjunction caused by digital twinning and workflow independence is solved, the whole-process closed-loop management from prediction to execution is realized, and the production self-adaption and intelligent level is improved.

The invention discloses a Web business process intelligent automatic execution system based on deep learning, and relates to the technical field of Web automation, and the system comprises a data acquisition and semantic analysis module which is used for acquiring business process description and Web page multi-modal data and generating structured business process representation; the deep learning feature extraction module is used for performing deep learning feature extraction on the multi-modal data to obtain corresponding structural feature vectors, visual feature vectors and text feature vectors; the cross-modal association fusion module is used for generating unified page feature representation; the page element intelligent identification module is used for identifying and positioning the target element based on the unified feature representation; the instruction generation module is used for generating an operation instruction sequence; and the execution control module is used for executing the instruction and dynamically adjusting the strategy. According to the method, a complete automatic link from business description to technology execution is established, conditional branch processing and complex dependency relationship management are supported, and the workload of manual programming and maintenance is reduced.

The invention relates to the technical field of data governance, and discloses a data governance method based on an industrial big data platform, which comprises the following steps: processing an organizational structure document and a business process definition through a text semantic analysis technology, and generating a responsibility allocation matrix; analyzing the historical data access behavior of the user, and depicting the data use characteristics of the user; according to the user model and the responsibility matrix, in combination with data sensitivity scoring, scene feature extraction and multi-dimensional risk calculation, dynamic permission decision is realized; for the decision-making mechanism, verifying the security influence of the authorization strategy through a simulation test; performing adaptive adjustment on model parameters according to a test result, and dynamically updating the initial responsibility matrix to form closed-loop management; through organizational structure intelligent analysis, user behavior modeling, risk awareness permission decision, digital twin verification and adaptive optimization, accurate division and dynamic adjustment of data governance responsibilities are realized, and the intelligence and dynamic adaptability of data permission management are improved.

The invention discloses a multi-mode driven cross-industry digital twinning universal platform architecture and an implementation method, and relates to the technical field of digital twinning and artificial intelligence. The method comprises the following steps: establishing a multi-modal driven cross-industry digital twinning universal platform, deploying a multi-source heterogeneous data acquisition component in a data access layer to access text, image and time series data, and converting unstructured data into a unified feature space by adopting a Transform-GNN cross-modal encoder in a multi-modal fusion layer; in the large model scheduling layer, feature vectors are analyzed through a multi-modal large model center based on an industry knowledge graph, an algorithm is dynamically matched, and an initial decision strategy is generated; the method comprises the following steps of: establishing a parameterized template library, and supporting security cooperative training of a third-party algorithm scheduling engine on a third-party algorithm, and deploying a lightweight digital twin engine in a twin engine layer: establishing the parameterized template library: pre-defining three templates of geographic space, equipment assets and business processes; deploying the twin model to an edge node by adopting a knowledge distillation method; in the interactive application layer, loading the BIM / GIS model in a lightweight manner through a low-code tool, and completing scene construction through a dragging component; and rendering a twin state in real time through a three-dimensional cockpit, analyzing a natural language instruction and performing corresponding operation.