4134 results about "Business data" patented technology

The invention discloses a power transmission and distribution production task cooperation system and method based on an intelligent agent, and relates to the technical field of power distribution production task scheduling, the system comprises six modules: a natural language input interaction module processes a user instruction and multi-modal information, and generates structured data; the electric power field knowledge enhancement analysis module establishes mapping from a natural language to business data; the dynamic interaction context memory module stores historical interaction data and generates a context feature vector through a bidirectional LSTM and an attention mechanism; the intelligent task scheduling and conflict resolution module is used for disassembling instructions into sub-tasks, dynamically evaluating priorities in combination with three-dimensional indexes and resolving resource conflicts; the agent task execution and cooperation module drives agents to execute tasks according to priorities and synchronize states in real time; the system closed-loop feedback optimization module analyzes the execution log and automatically updates model parameters; according to the system, the problems of term analysis deviation, strategy staticization and insufficient self-optimization capability of a traditional scheduling system are solved.

The invention provides a construction method of a double-path collaborative decision network for multi-agent collaborative path optimization, and the method comprises the steps: obtaining multi-source heterogeneous real-time business data and historical business data, and constructing a standardized data set; converting the standardized data set into a dynamic environment input sequence; at least two domain agents perform full-process business logical reasoning based on the dynamic environment input sequence to generate a decision chain structure covering the full life cycle of the business; implementing full-flow path planning simulation based on the decision chain structure, and generating an initial path scheme including multi-resource collaborative scheduling; the environment perception intelligent agent puts the initial distribution path scheme into an actual logistics distribution scene for execution, and loads real-time environment change information to determine a logistics decision chain weight; and the logistics strategy agent optimizes the initial distribution path scheme based on the logistics decision chain weight to obtain a logistics business knowledge graph so as to construct a logistics double-path collaborative decision network.

The invention provides a service data security protection method and system for digital enterprise management, and the method comprises the steps: obtaining an access request sequence initiated by a target user at a service operation terminal, generating a dynamic access control strategy vector according to a historical behavior track associated with an access operation identifier, and carrying out the dynamic access control strategy vector; analyzing the dynamic access control strategy vector through a strategy decoder, and generating a real-time access permission instruction; based on the real-time access permission instruction, performing homomorphic encryption mapping on a sensitive data segment in the request context information to generate a ciphertext transmission channel, and performing security parameter synchronization on the ciphertext transmission channel and the cross-department conjoint analysis model; and calling a federated learning framework to carry out multi-modal feature fusion on the real-time operation flow of the service operation terminal, generating an abnormal operation confidence score, triggering a cross-level interception protocol when the abnormal operation confidence score exceeds a dynamic threshold, and rolling back the current session state to a security baseline version. According to the invention, the accuracy and response timeliness of anomaly detection can be improved.

The invention relates to a multi-modal data and RAG technology fused power design knowledge base construction method, and belongs to the technical field of power software development. The method comprises the following steps: carrying out collection and information extraction on multi-source heterogeneous original data; the method comprises the following steps of: constructing a multi-dimensional knowledge element structure containing parameters, specifications and case relationships by carrying out classification, specialized and precise processing and cross-modal association on data; based on a vector, graph and relational database mixed storage architecture, semantic vector efficient retrieval, knowledge graph relation management and business data synchronization are achieved respectively; and a dynamic optimization result is subjected to hybrid retrieval, a dual-drive reasoning mechanism outputs compliance conclusions and bases, and a retrieval enhancement generation service ensures that output contents conform to specifications. And systematic management and intelligent application of the electric power design knowledge are realized.

The invention discloses a collaborative scheduling method based on a security agent, and relates to the technical field of network security design. The specific operation of the security agent collaborative scheduling method comprises the steps of system deployment and initialization, security operation task execution process, agent self-learning and capability evolution implementation, cross-domain security agent collaborative adaptation implementation and security operation visualization and traceability implementation. Connection paths between the security agent and business data, a private knowledge base, a security tool and multiple models are broken through, the problem that all elements in a traditional mode lack efficient communication and collaboration is solved, and by means of agent routing dynamic scheduling, A2A protocol interaction, MCP protocol tool calling, RAG business interface calling and multi-model combination, the security of the security agent is improved. Integration and intellectualization of the safety operation process are achieved, the continuity and the response speed of the operation process are improved, safety operation is more efficient and collaborative, and complex and variable safety requirements are met.

The invention relates to the technical field of artificial intelligence and big data analysis, in particular to a data operation system and method based on a knowledge graph, and the method comprises the steps: extracting an entity and semantic relationship from multi-source business data, and constructing a dynamic evolvable initial knowledge graph; node features are aggregated, and multi-dimensional situation state vectors are generated in combination with gating loop unit modeling behavior path dependence; through a structure-semantic coupling attribution scoring mechanism, a statistical information gain and semantic similarity are fused to identify a core driving factor, and a causal regression model of the factor and an operation target is established; dynamically adjusting the edge weight and the structure of the atlas in real time, and triggering a new path discovery mechanism to continuously optimize the atlas; and according to a quantitative business target, reversely extracting a high-confidence influence path from the atlas, and generating a personalized strategy combination through intervention simulation and multi-target Pareto optimization, thereby realizing intelligent recommendation and decision closed loop driven by an operation target. According to the invention, higher-precision operation situation awareness and strategy generation are realized.

The invention discloses a laboratory quality management document intelligent generation method and system based on retrieval enhancement, and relates to the technical field related to data processing.The method comprises the steps that semantic coding is conducted on a preset standard text, and a vector knowledge base is constructed; retrieving associated standard terms according to the document theme, and extracting structured data from a laboratory business system; embedding the standard terms and the business data into a Prompt template, and calling a preset large language model to generate a text; and performing paragraph splicing and hierarchical control on the generated text, automatically checking compliance by utilizing term consistency of rule model fusion and a numerical value comparison algorithm, and outputting a quality management document. The technical problems that in the prior art, standard term retrieval and matching are not accurate, laboratory business data fusion is difficult, and consequently document compiling efficiency and quality are poor are solved, and the technical effects that minute-level automatic generation of laboratory quality management documents is achieved, and document compiling efficiency, quality and compliance are improved are achieved.

The invention discloses a multi-source business data collaborative acquisition method for subcompartment change, and relates to the technical field of forest resource management, and the method comprises the following steps: S1, capturing subcompartment boundary, ownership and operation attribute change events in real time through a dynamic topology perception layer, and generating a dynamic topology map; according to the multi-source business data collaborative acquisition method oriented to the subcompartment change, a dynamic topology sensing mechanism and a standardized acquisition process are constructed, so that the problem of multi-source data splitting caused by frequent subcompartment change is solved, and real-time collaborative acquisition and consistency guarantee of multi-source heterogeneous data are realized. A dynamic topological graph accurately senses boundary adjustment and ownership change events, and a double-channel analyzer is combined to automatically map service rules into equipment instructions, so that the data association error rate is remarkably reduced; the bimodal verification mechanism effectively eliminates version conflicts and business rule contradictions through time sequence alignment and sandbox simulation, and improves the accuracy of forest resource assessment.

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 relates to the technical field of natural language processing, in particular to an intelligent search engine system and method based on NLP and vector hybrid retrieval, and the system comprises a query analysis module which is used for receiving a query statement input by a user and obtaining structured query information based on the query statement; the symbiotic index module comprises a sparse extension index unit which is used for constructing a sparse inverted index table based on keywords in the structured query information; the vectorization hypergraph index unit is used for forming a hypergraph index based on the business data; the recall arrangement module is used for acquiring a candidate object set according to the structured query information; the constraint rearrangement module is used for calculating a joint priority score between query and candidate objects based on the candidate object set, and obtaining a sorting result under constraint conditions of meeting a supplier proportion, a category proportion and a price interval; and the evidence generation module is used for generating evidence information based on the sorting result and outputting the evidence information to the user interface.

The invention relates to the technical field of smart factory management platforms, and discloses a data real-time monitoring method based on a smart factory management platform system, and the method comprises the steps: carrying out the standard format conversion of supplier Excel data, carrier GPS track data flow and warehouse RFID goods location data in a smart factory ERP system, and obtaining standardized business data; calculating comprehensive scores of suppliers based on the standardized business data and generating a dynamic sorting list; receiving purchase order information and creating a logistics scheduling plan, and pre-allocating an optimal goods allocation for the warehousing commodities to obtain a warehousing pre-allocation scheme; monitoring the execution progress of the storage pre-distribution scheme in real time, and synchronously updating service state information; the auditing state of the purchase order information is monitored based on the service state information, and an intelligent collaborative decision-making scheme is generated, real-time synchronous updating of the service states of the supplier management module, the logistics scheduling module and the warehouse management module is ensured, and the supply chain collaborative management efficiency is improved.

The invention relates to a method and a system for storing and backing up electric power data of a data-in-data station based on use popularity, and belongs to the technical field of data-in-data stations, and the method comprises the following steps: classifying electric power business data into hot data, temperature data and cold data based on preset access frequency, time window and data volume index; according to a classification result and a business importance degree, storing hot data in an SSD cloud disk or a memory database, storing temperature data in a distributed column database, and compressing cold data and then archiving the cold data to an object storage or a tape library; dynamically configuring differentiated backup strategies for different types of data; automatic scheduling of storage and backup is realized through a cloud platform component, and hot switching of master and slave databases, OSS archived data recovery by a DataWorks tool and a remote multi-copy recovery mechanism are supported; and migrating data in advance in combination with a historical access prediction popularity trend, and triggering cold data compression and archiving when storage resources exceed 80%. The method is suitable for power industry scenes, the storage cost is optimized, and the data calling efficiency is improved.

The invention discloses an intelligent management and control system based on a large model and integrated multi-dimensional spatio-temporal data fusion, and belongs to the field of artificial intelligence and engineering application. The system comprises a space-time large model base, an engineering object three-dimensional or four-dimensional integrated model, an intelligent agent cooperation module and an autonomous analysis decision and control module. The method comprises the following steps: embedding engineering field knowledge type public domain data based on a large model, forming a space-time large model base, fusing three-dimensional or four-dimensional geometry and attribute data of an engineering object and dynamic business data such as monitoring and equipment automation, constructing a private domain data source which is integrally expressed, stored and managed, and autonomously analyzing and deciding by utilizing the space-time large model reasoning capability. And the scheduling agent collaboratively completes task planning and arrangement, safety production analysis and decision support and control, and automatically generates various engineering image-text reports. The intelligent management and control problem in the engineering fields of mines, factories, traffic, water conservancy, buildings, machinery and the like can be solved, and the practicability of an artificial intelligence system in the engineering field is remarkably improved.

The embodiment of the invention provides a data transmission method and device of Internet of Things equipment, electronic equipment and a storage medium, and relates to the technical field of Internet of Things, and the method comprises the steps: obtaining business data collected by the Internet of Things equipment, and determining the target business priority of the business data; acquiring network index data of communication equipment connected with the Internet of Things equipment; performing data volume prediction according to the service data to obtain a corresponding predicted data volume; performing transmission mode selection according to the predicted data volume and the network index data to obtain a first transmission strategy for the service data; and issuing the first transmission strategy to the Internet of Things equipment, so that the Internet of Things equipment transmits the service data according to the first transmission strategy, thereby optimizing the data transmission mode, realizing dynamic adjustment of the transmission strategy, improving the network adaptability, improving the success rate of data transmission, and reducing the probability of network congestion.

The invention discloses a project side end data fragmentation recombination and service breakpoint resuming method in a weak network environment, and the method comprises the steps: receiving original service data collected on site through a project side end, carrying out the fragmentation according to the division of a construction region and a time interval, and generating a fragmentation data package with a time-space identifier and a check code; verifying the received fragmented data packet, and generating a fragmented cache set with a processing progress; when all fragments of a certain region-time fragment in the fragment cache set are received, the preprocessing progress of the corresponding service is read from the persistent file, and remaining service processing continues to be executed from the progress interruption position; and integrating the recombined original data of each region-time slice, the business processing result, the breakpoint resume record and the fragment transmission log, and generating a business data file containing a complete data chain and a processing track. By utilizing the embodiment of the invention, the data transmission efficiency and the service continuity in a weak network environment can be improved, and the reliability requirement of a project side end scene is met.

The invention relates to the technical field of data processing, can be applied to the field of science and technology finance / digital medical treatment, and discloses a multi-source heterogeneous data verification and tracing method and device and related equipment. The method comprises the following steps: acquiring multi-source heterogeneous data from a service data source of a target service; preprocessing the multi-source heterogeneous data, extracting a first data field in each data source, and matching the first data fields with the same semantics in different data sources based on a preset semantic mapping rule; performing consistency verification on the matched first data fields to obtain second data fields with inconsistent verification results; performing root cause tracing on the second data field based on a preset difference analysis rule, and determining a difference type corresponding to the second data field according to a root cause tracing result; and generating a multi-source heterogeneous data verification and tracing result according to the business data source, the second data field, the root cause tracing result and the difference type. According to the method, matching, difference identification and root cause positioning of multi-source heterogeneous data are realized.

The invention relates to a brain inspired data service system and a brain inspired data service method, which are characterized in that a layered architecture is constructed, the layered architecture comprises a data node cluster deployed in a mixed manner, the data node cluster specifically comprises a core node, an acquisition node and a gateway node, and the core node, the acquisition node and the gateway node are respectively used for processing core service data, real-time edge data and cross-domain connection; the small-world connection layer realizes same-domain efficient communication through high-clustering local connection, and breaks a data island through dynamic long-range cross-domain connection; the intelligent control layer integrates a DMN control center, a collaborative state monitoring engine, a prediction analysis module and a reinforcement learning driven resource scheduler. The system initializes a topological structure through a small-world network manager, and when the communication frequency of a cross-domain node exceeds a threshold value, long-range connection is dynamically inserted to ensure that the hop count of a cross-domain path is stably not higher than a preset value. The DMN control center realizes dual-mode switching based on a system load threshold; in an idle period, data pre-cleaning, knowledge graph construction, predictive pre-fetching and resource pre-allocation are executed; and optimizing routing in combination with a graph traversal algorithm in a task period.

The invention discloses a reliable communication system based on a publishing and subscribing mode in a satellite-ground weak network environment, and aims to solve the problems of high round-trip delay, limited link bandwidth, high instantaneous packet loss rate, easiness in blocking and switching influence of links and the like in a data transmission process between a low-orbit satellite and a ground station. A publishing and subscribing model is introduced into an application layer to realize service logic decoupling, deep fusion with an underlying enhanced QUIC transmission protocol is realized, and mechanisms such as message persistence, breakpoint resume, forward error correction and connection migration are combined, so that stable, orderly, complete and safe transmission of service data can still be ensured under the conditions of link instability, quality fluctuation and communication interruption, and the service data transmission efficiency is improved. And high availability and continuity of a satellite-ground communication task under a weak network condition are realized.

The invention relates to the technical field of graph neural networks, in particular to a user association analysis method and device based on a graph neural network, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: acquiring at least one kind of business data, wherein the types of the business data comprise user attribute data and user interaction data; performing data fusion processing on the user attribute data and the user interaction data, and generating a dynamic relation graph; according to the dynamic relation graph, risk propagation quantitative calculation and weight calculation are carried out by using a graph neural network, and a dynamic weight calculation result is obtained; and adjusting a dynamic relation graph by using the dynamic weight calculation result, and carrying out user association analysis according to the adjusted dynamic relation graph. By adopting the method, the association relationship between the users can be updated in real time in a multi-platform operation mode, the risk conduction effect is effectively captured, and the accuracy of risk identification and credit evaluation is improved.

The invention provides a power business data analysis processing method and system. The method comprises the following steps: receiving a business expansion work order containing a structured text and an unstructured image through a business hall terminal; analyzing text key entity fields, extracting image anti-counterfeiting feature points, and mapping the image anti-counterfeiting feature points to a unified vector space through a multi-modal alignment layer to generate semantic image feature flow; loading to a hardware acceleration chip to execute parallel matrix operation, identifying a logic binding relation between entities, and outputting a nested relation topological graph; calling a historical work order decision path to train a reinforcement learning agent to generate a control strategy; comparing the similarity between the to-be-anti-fake feature point and a pre-stored template, and outputting a verification result containing a missing identifier and anti-fake abnormity; and if the field is complete and the anti-counterfeiting similarity is greater than or equal to a threshold value, releasing the work order to a service system, otherwise, freezing the work order and triggering an alarm. According to the invention, the processing efficiency is improved, the abnormal generation risk is blocked, and automatic anti-counterfeiting verification and flow control of the electric power work order are realized.

The invention belongs to the technical field of multi-modal intelligent data management, and discloses a multi-modal business data co-processing and intelligent conversion method, which comprises the steps of fusing multi-source data, coupling cross-modal time sequence association, detecting image-text semantic conflicts, and generating an RAG semantic unit and a space-time alignment semantic packet. Constructing a four-dimensional priority label to divide resource pool categories, dynamically allocating resource pool computing resources, generating a low-redundancy feature set, triggering a forced preemption strategy during resource competition, and synchronously generating a resource arbitration log; injecting a dynamic rule node according to a data type, executing confidence arbitration in combination with a conflict arbitration identifier, generating business decision content and generating a rule correction decision packet; constructing a full-link semantic thinking chain, synchronously capturing a system operation track, and aligning to generate a dual-channel tracing report; identifying residual sensitive fields, triggering hot update to load corresponding rules, quantizing effectiveness to generate compliance reports, reversely updating an industry rule base, and optimizing a resource allocation strategy.

The invention relates to the technical field of artificial intelligence and natural language processing, in particular to an intelligent customer service method and system based on a large model, and the method comprises the steps: generating a unique session ID based on a user identity, and associating a historical context; multi-modal input of texts, voices, images and the like is carried out, and data standardization is carried out through a unified interface; realizing mixed intention recognition by combining a rule engine and a large model; constructing a dynamic customer portrait through multiple rounds of dialogue records, historical work orders and business data; according to the question emergency degree and customer value grading response, analyzing customer emotion in real time, adjusting a reply style, and outputting an answer; the manual operation process of the traditional customer service is converted into an AI automatic closed loop; the method has the beneficial effects that customer intention deep understanding, multi-mode interaction, emotion perception and service process automation are realized through a large language model (LLM) technology, and the service efficiency and the user satisfaction are remarkably improved.

The invention discloses an automatic official document generation method based on a dynamic rule, and relates to the technical field of official document generation, and the method comprises the following steps: receiving official document writing demand data input by a target user, carrying out semantic analysis on the official document writing demand data, extracting structured parameter data based on a semantic analysis result, and generating an official document according to the structured parameter data. Performing official document type identification on the structured parameter data to confirm an official document type identification code; a dynamic rule engine is used for matching the corresponding format verification rule base and the content logic knowledge graph, and an official document writing framework is generated; receiving business data corresponding to the target user, extracting a key information triple from the business data, inputting the key information triple into a preset intelligent mapping model for semantic level association analysis, and generating a preliminary official document manuscript in combination with an official document writing framework; and verifying the preliminary official document manuscript, and adjusting and optimizing the preliminary official document manuscript according to a verification result to generate a final official document manuscript. The method has the effect of improving the document generation efficiency.

The invention relates to the technical field of data processing, and discloses an electronic manufacturing intelligent production data set generation system based on multi-modal data fusion, and the system comprises a system module assembly which comprises a data collection layer, a data processing layer and a quality management layer. The invention further provides an electronic manufacturing intelligent production data set generation method based on multi-modal data fusion, and the method comprises the following steps: S1, multi-modal data collection: collecting data including enterprise business data, public compliance data, sensor data and video monitoring data; and S2, data preprocessing and labeling. Through a multi-modal data fusion technology, limitation of a single data type is broken through, cross-modal alignment and feature association of images, texts, sensor data and industrial identification data are realized, and the utilization rate of electronic manufacturing scene data is improved.

The invention provides a smart park operation and maintenance management method and system based on CIM and a medium, and belongs to the technical field of smart park management. The method comprises the following steps: constructing a CIM digital twinborn model based on a park building group, a geographic position, Internet of Things equipment and business data, and synchronously extracting equipment state and energy consumption information; analyzing the state data by using the equipment fault prediction model and generating a repair work order to realize predictive maintenance; performing gridding processing on the energy consumption data, generating a thermodynamic diagram and identifying an abnormal energy consumption area; and positioning a high-energy-consumption grid by combining an energy consumption analysis model, and generating a comprehensive operation and maintenance scheme of equipment regulation and control, energy scheduling and system optimization through matching of an energy management strategy library. According to the invention, the digital twinning technology and the CIM platform are fused, so that full-life-cycle management and dynamic energy consumption optimization of the equipment are realized, the operation and maintenance efficiency is remarkably improved, the failure rate and energy waste are reduced, and both economy and environmental protection are achieved.

The invention belongs to the technical field of supply chain risk early warning, and particularly discloses a server supply chain risk conduction early warning method based on a fusion graph neural network, and the method comprises the steps: obtaining historical time sequence data and real-time business data of each entity in a server supply chain, constructing a dynamic heterogeneous supply chain graph, and carrying out the early warning of the risk conduction of the server supply chain; the method comprises the following steps: identifying a causal association relationship of different risk types among nodes, quantifying a corresponding time lag parameter, dynamically generating at least one risk element path for each risk type, and fusing real-time business data and the risk element paths by taking a graph as a structure basis to obtain a risk element path; through neighborhood state aggregation along a meta path and multi-scale time sequence feature extraction, a dynamic risk score of each node is output, the score is compared with an adaptive early warning threshold to generate a graded early warning signal, and an integrated risk early warning report is generated and output in combination with risk conduction path information. Accurate prediction and early warning of the supply chain risk spatio-temporal dynamic conduction process can be realized.

The invention relates to the technical field of project intelligent management and control, and discloses a multi-agent power grid project intelligent monitoring, management and control and evaluation system and method, and the system comprises a mixed data resource library, a multi-agent system, an index calculation module and a process control module. The mixed data resource library stores structured business data and unstructured documents; the multi-agent system comprises an information extraction agent, an index design agent, a code generation and treatment agent and the like, works cooperatively, and converts a high-level natural language management and control rule into an executable code; the index calculation module is responsible for executing codes according to a scheduling strategy and calculating quantitative indexes; and the process control module automatically identifies risks and executes management and control operations such as process locking according to the index result and a preset threshold value. According to the invention, the complex business logic can be automated and coded, the accuracy and timeliness of risk identification are improved, and refined and prospective intelligent management and control of the power grid project are realized.

The invention relates to the technical field of intelligent customer acquisition, in particular to a cross-border supply chain financial service AI intelligent customer acquisition system based on big data analysis. Functional components such as a big data access engine, an artificial intelligence analysis model, a customer portrait modeling engine, a preference setting interface, a public opinion and risk identification module, an intelligent screening engine and a docking output module are integrated; multi-source data such as customs data, trade data, enterprise industry and commerce data, judicial public data, international manufacture and cutting lists and internet public information from all places around the world are widely fused. The system performs modeling calculation on industry categories, regional distribution, export history, transaction records, public opinion trends and compliance risks of import and export enterprises, establishes customizable customer optimization rules, accurately outputs a target customer list which can be covered by credit insurance and financing service in combination with a machine learning algorithm, and attaches contact information. Various import and export related data resources can be accessed, automatic screening and output of credit insurance and account receivable financing adaptive exporters are realized through preference setting and AI analysis, the customer obtaining efficiency is remarkably improved, the marketing and risk control cost is reduced, and the economic benefit is improved. The problems that traditional cross-border supply chain financial service customer obtaining depends on manual recommendation, offline visit and a static customer library, the efficiency is low, and small and medium-sized export enterprises are difficult to cover are solved.

The invention provides an intelligent business document approval method and approval system based on a large model, and the method comprises the steps: analyzing an unstructured text, and extracting an audit rule for a business document from the unstructured text according to a historical data analysis result and a risk assessment result; the extracted auditing rule is stored based on a comprehensive rule knowledge base, the comprehensive rule knowledge base comprises a recessive rule and an expression description rule, and the auditing rule is updated when the business relation is changed; and automatically examining and approving the obtained business document according to the stored examination and approval rule, generating a cue word by combining the business data in the business document according to a preset examination and approval scene and a general rule, automatically examining and approving the business document based on the cue word and the business data by utilizing a large model, and returning an examination and approval result. The invention further provides an approval system for implementing the method. When the business document is approved, the requirement of the implicit rule for the approval is considered, and the approval quality is improved.