4830 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 relates to the technical field of data analysis, and discloses a business data analysis method, device and equipment based on big data and a storage medium, the method comprises the following steps: collecting and preprocessing original log data to obtain standardized log data, and performing adaptive multi-modal feature extraction to obtain comprehensive feature representation; inputting a heterogeneous graph neural network to carry out anomaly detection and multi-level root cause analysis to obtain a hierarchical anomaly event; executing task scheduling and resource allocation of reinforcement learning to obtain a task allocation scheme; performing deep learning enhanced root cause reasoning and knowledge graph construction on the task allocation scheme to obtain a system state diagram and a causal relationship; and generating a multi-dimensional task data analysis report. According to the method, comprehensive analysis, anomaly detection, root cause reasoning and intelligent scheduling of log data are realized. The reliability and performance of the system can be improved, and more intuitive and accurate decision support can be provided for operation and maintenance personnel.

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 relates to the field of business data distribution, financial science and technology and medical health, and discloses a multi-agent cooperative task execution method, which comprises the following steps: receiving a task instruction input by a user, and carrying out task step configuration on the task instruction to obtain a sub-task sequence; judging whether the task information of each subtask meets a preset execution logic standard, and if yes, sending the subtask to an intelligent agent according to an identifier of the intelligent agent corresponding to the subtask; and recording task data generated when each agent executes the subtask, extracting a second keyword in the task data to construct a decision-making dependency relationship graph among the agents, extracting collaborative data of each agent collaborative completion task instruction from the decision-making dependency relationship graph, and optimizing subsequent task allocation by utilizing the collaborative data. According to the method, the cooperation process among the agents is optimized, and continuous learning and improvement are carried out, so that the overall efficiency and reliability of the multiple agents are improved.

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 the technical field of data transfer and verification, in particular to a business data testing method and system and a storage medium. The method comprises the following steps: acquiring an original service data stream; deploying a supervision submission shadow processing sandbox based on the original business data flow to obtain a data isolation verification environment; configuring an alliance block chain server based on the original service data flow, and constructing a distributed data processing traceability platform; based on the distributed data processing traceability platform, performing data processing proof generation by using the original service data stream to obtain a data processing hash chain; configuring differential privacy protection processor parameters based on the data isolation verification environment to obtain a privacy protection test data generator; a privacy protection test data generator is trained to generate a synthetic test data set. By combining the alliance block chain and the differential privacy technology, a high-credibility, automatic and traceable data testing system is constructed, and the data security, the verification efficiency and the system consistency are remarkably 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 provides a business vitality prediction and business district evaluation method based on multi-modal feature fusion, and relates to the technical field of spatial data analysis, and the method comprises the steps: obtaining business POI data and time sequence trajectory data of a target area, extracting the score and emotion features of user comments, and calculating the pedestrian flow distribution based on the time sequence trajectory data; then inputting the features into a recurrent neural network to predict a commercial vitality time sequence, constructing POI data into a knowledge graph, and constructing and optimizing a network reachability matrix in combination with position information and human traffic; and finally, dividing a target region by using a Louvain algorithm, fusing multi-dimensional features by edge computing nodes of each sub-region, dynamically adjusting the weight of an evaluation index through reinforcement learning, and outputting a commercial service evaluation result. By fusing multi-source data and a deep learning method, the life circle business service quality can be evaluated more accurately and comprehensively, and decision support is provided for urban planning and business development.

The invention provides a business data mining method and system applied to intelligent port scheduling, and the method comprises the steps: firstly obtaining real-time operation data containing an equipment operation state, a transportation task queue and environment monitoring information during the loading and unloading operation of a target port, then carrying out the dynamic preprocessing of the real-time operation data, screening and marking conflict tasks according to a preset rule, and obtaining effective operation data; and then performing multi-dimensional feature coding on the effective data to generate an operation feature set containing equipment operation features, task distribution features and environment constraint features, and based on the operation feature set, generating a port equipment scheduling strategy meeting balance of operation efficiency and resource consumption through a pre-training model, covering an equipment start-stop sequence and the like. And finally, dynamically adjusting port equipment to execute tasks according to a scheduling strategy, and updating real-time operation data, thereby realizing efficient and reasonable scheduling of the intelligent port.

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 discloses an abnormity early warning method and system based on artificial intelligence, and relates to the technical field of abnormity early warning. The method comprises the following components: S1, brain wave data acquisition and preprocessing, S2, emotional state recognition and feature extraction, S3, specific business data acquisition and arrangement, S4, data fusion and abnormal feature construction, and S5, abnormal early warning decision and notification. According to the method, the brain wave emotion features and the specific business data are fused, abnormal feature construction is carried out by adopting a fusion algorithm of multi-scale feature matching and a support vector machine algorithm based on quantum annealing optimization, the abnormal feature rule can be mined more accurately through the comprehensive data processing and analysis mode, and the accuracy of abnormal feature extraction is improved. Therefore, in the real-time data monitoring and analysis process, the accuracy of abnormal early warning is improved, meanwhile, the system can dynamically adjust the monitoring frequency according to the fluctuation characteristics of the data, the timeliness of early warning information is ensured, and related personnel can take countermeasures in time.

The invention discloses a network access permission dynamic management and control method and system based on behavior analysis, particularly relates to the technical field of network security, and is used for solving the problem of systematic service interruption caused by neglecting implicit association between permission and service logic in the existing dynamic permission strategy. The method comprises the following steps: constructing a permission dependency graph by collecting real-time behavior data of a user and equipment in real time, identifying hidden dependency nodes which have indirect data streams with a target interface but are not explicitly called, and generating a hidden path weight coefficient in combination with a historical behavior log; based on the real-time behavior characteristics and the map topological structure, quantizing the outage probability of permission adjustment on dominant and hidden dependency paths, and dynamically generating an alternative access interface and a data transfer interface; when an alternative path abnormal operation is detected, an original path is traced back through a permission dependency graph, and permission configuration is iteratively optimized; the continuity of the business data flow is maintained while the dynamic contraction of the authority is ensured, and the interruption of the associated service caused by local authority change is avoided.

The invention discloses an intelligent data query method and device based on a large model and a medium, and relates to the technical field of large model application. The method comprises the steps of receiving a natural language query text input by a user, and determining a target interface group from pre-associated interface groups through a preset business line classification rule and a keyword matching algorithm; inputting the natural language input text into a pre-trained large model to analyze a parameter set required by interface calling; wherein the parameter set comprises at least two of the following items: a time range field, a business main body field and a statistical dimension field; calling an API interface in the target interface group according to the parameter set, and obtaining original business data; performing context supplementation on the original business data based on the business logic rule to generate a standardized data result containing business background description; and reconstructing the standardized data result according to a display format preset by a user, and outputting a combined visual result containing charts, tables and text description.

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 relates to the technical field of operation data analysis, in particular to a multi-dimensional operation analysis index automatic adjustment and optimization management method, which comprises the following steps of: connecting a plurality of service systems through a distributed data acquisition gateway, and constructing a standardized data pool based on a dynamic pattern recognition algorithm; calling a dynamic index calculation engine, performing multi-dimensional feature extraction on the standardized data pool according to a preset index generation rule, and generating an initial index set; inputting the initial index set into an optimization model, generating an index optimization scheme through an association constraint analysis module, and outputting an optimization index set; and establishing an index dynamic update link. According to the invention, the health state of the system is monitored in real time in the release process of each business index, rollback control is triggered based on the change trend of the health index, and the updating safety is improved.

The invention relates to the technical field of data processing, and discloses a cross-border trade data intelligent processing method and system based on an intelligent port. The method comprises the steps of performing functional model hierarchical processing on cross-border trade business data, obtaining a functional module calling relation network, performing process template construction processing, obtaining a dynamic process assembly rule base, performing service burying point processing, obtaining a user behavior analysis data set, performing guide strategy construction processing, and obtaining a user behavior analysis result set; and obtaining a service guidance rule base, carrying out knowledge graph construction processing, obtaining a service knowledge inference rule set, and carrying out cross-border trade data processing to obtain a port service intelligent decision scheme. According to the method, intelligent processing of cross-border trade data is realized, a complete technical chain from data acquisition, behavior analysis to decision support is constructed, accurate business guidance and intelligent decision support are provided, and the problems of low business processing efficiency, inaccurate service guidance, weak decision support capability and the like in the prior art are solved.

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 invention discloses an information co-processing platform based on a knowledge graph and a potential relationship prediction method. The information co-processing platform comprises a data acquisition processing layer, a knowledge extraction intelligent analysis layer and a knowledge application decision-making layer, the data acquisition processing layer comprises a data acquisition module, a data format conversion module and a knowledge base management module; the knowledge extraction intelligent analysis layer comprises a keyword extraction module, a keyword layering module, a semantic fusion module and a knowledge ontology dynamic evolution module; and the knowledge application decision-making layer comprises an evolution state judgment module, a military strategy graph generation module, an evaluation result acquisition module and a military strategy guidance information updating module. According to the method, a large amount of dispersed and isolated intelligence is integrated from actual military field business data by taking a real-time military strategy as a guide, association of military intelligence information is realized from a semantic level, powerful support is provided for comprehensive analysis, research, judgment and display of the intelligence information, analysis efficiency is improved, and effective cooperation of military strategy and intelligence data processing is 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 belongs to the technical field of data quality management, and particularly discloses a power transmission and transformation equipment manufacturing full-process data checking method and system, a terminal and a medium, and the method comprises the steps: collecting original business data of each stage of power transmission and transformation equipment manufacturing, and carrying out the standardization, format normalization and preliminary abnormity elimination; uploading the processed data to a cloud processing module, and executing semantic fusion and field mapping processing; calling a corresponding checking rule set according to the manufacturing state, and executing field-level and record-level quality checking on the structured data; generating an abnormal identifier for the unqualified data, feeding back the abnormal identifier to a client interface, and storing the qualified data to the block chain module to realize credible evidence storage; and finally, generating a data quality checking report and performing structured visual display. According to the method, the problems that existing manufacturing data semantics are not uniform, rules cannot be dynamically adapted, abnormity is difficult to identify with high confidence, and data evidence storage is missing are solved.

The invention belongs to the technical field of order examination, and discloses an intelligent order examination method, system and device based on artificial intelligence and a medium, and the method comprises the steps: obtaining unstructured business data; performing information extraction and conversion on the unstructured business data based on a pre-trained small model to obtain structured text data; identifying a pre-made approval system file, generating an approval rule, and constructing a rule engine based on the approval rule; performing preliminary compliance auditing on the structured text data based on a rule engine to obtain a first auditing result; finding out associated knowledge features associated with the text data based on the structuralization; performing semantic analysis on the structured text data based on a pre-trained large model to obtain semantic features; comparing and auditing the associated knowledge features and the semantic features to obtain a second auditing result; and generating an audit report based on the first audit result and the second audit result. According to the invention, the accuracy and consistency of auditing are improved.

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 provides a business data sharing fusion system and method based on a trusted data space, and relates to the technical field of data sharing. According to the method, sharing and fusion of service data are realized by constructing a trusted data space infrastructure. The method comprises the following steps: firstly, establishing a node path planning model for priority evaluation and data matching; then forming a model collaborative deployment scheme to distribute tasks to edge nodes; then perception scoring and resource scheduling are carried out on the tasks, and a multi-task concurrent transmission scheme is generated; constructing a multi-domain network service level protocol evaluation model to perform service decomposition and business collaborative management; and finally carrying out heterogeneous processing and data fusion on the business data. According to the method, the problems of information islands, multi-source heterogeneous data fusion and cross-domain business collaboration among regional mechanisms are solved, and the system performance and the user experience are 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 embodiment of the invention belongs to the field of data processing, and relates to a business risk assessment method and device, computer equipment and a storage medium, and the method comprises the following steps: constructing a risk knowledge graph according to business data; acquiring a risk conduction path in the risk knowledge graph based on a graph search algorithm; acquiring associated risk event information in a database according to the risk conduction path, and analyzing the associated risk event information according to a data mining algorithm to obtain risk event influence information; quantifying the risk event influence information, and training a risk event influence evaluation model according to quantified risk event influence index data; performing risk simulation evolution according to the risk conduction path and the risk event influence evaluation model to obtain a risk event influence result; and performing risk analysis according to the risk event influence result to obtain a comprehensive risk value. According to the invention, accurate risk assessment can be carried out on business of an enterprise.