399 results about "Network analysis" patented technology

The invention discloses an inquiry model training method and device based on a long thinking chain, and relates to the field of large models, semantic information is extracted through strategy network analysis of a model, and an initial step decision is generated in combination with context information in a historical memory library; sending the initial step decision into a reasoning path generator, and reasoning to generate a primary diagnosis disease source and an intermediate diagnosis step; sending the primary diagnosis source and the intermediate diagnosis step into a verification module, performing pathological logic verification according to a case diagnosis report and a medical knowledge base, and feeding back a verification result; the reasoning path generator updates the historical memory bank based on the feedback result, the preliminary diagnosis disease source and the intermediate diagnosis steps; the strategy network continues reasoning based on user feedback input and the updated context information in the historical memory bank, and finally an inquiry result is output. According to the scheme, technical means such as reinforcement learning, self-adaptive backtracking and memory enhancement are introduced into a long thinking chain reasoning framework, so that a large language model realizes multi-aspect comprehensive improvement in medical question and answer and auxiliary diagnosis scenes.

The invention discloses an intelligent early warning method for pipeline blockage of a slurry circulation system of a slurry shield, which relates to the field of intelligent early warning, and comprises the following steps of: performing spatial-temporal feature analysis on a standardized multi-dimensional data stream, constructing a blockage feature knowledge graph based on pipeline position and time sequence correlation analysis, and generating a blockage feature vector through a graph neural network; based on the blockage feature vector, analyzing the dynamic change trend of particle distribution through a long-short-term memory network and predicting the particle blockage risk in combination with an acoustic signal, then performing adaptive judgment by fusing geological conditions and construction stage information to obtain a risk assessment result, and inputting the risk assessment result and the blockage feature vector into digital twinborn simulation to obtain the particle blockage risk. A blockage scene is predicted based on fluid dynamics and a particle sedimentation model, early warning parameters are adjusted through Bayesian optimization, and graded early warning signals are generated; according to the invention, by generating the blockage feature vector, the recognition capability of the early local abnormal propagation trend is enhanced, and a reliable basis is provided for accurately predicting the blockage risk.

The invention discloses a photovoltaic power grid fault identification method and system based on circuit analysis, and relates to the technical field of fault identification, and the method comprises the following steps: obtaining the operation parameters of a photovoltaic power grid, and constructing a circuit analysis model; based on the circuit analysis model, equivalent response curves in different fault scenes are extracted, and the reference operation state is compared to generate a differential residual sequence; performing time-frequency joint decomposition on the differential residual sequence, and stripping photovoltaic output fluctuation from a load disturbance component to obtain a pure circuit characteristic component; based on the pure circuit characteristic component, a multi-dimensional characteristic coordinate space is formed, and the fault type is judged by using the dynamic bending rate of the fault response track; and mapping a fault type discrimination result back to the circuit analysis model, and positioning the position of a fault branch in combination with local disturbance distribution of the node impedance matrix. According to the method, pure circuit characteristic component extraction and multi-dimensional characteristic space dynamic analysis are combined, and accurate judgment of complex fault types and fault branch positioning are achieved.

The invention provides an energy digitization platform resource scheduling method based on cloud edge cooperative computing, which comprises the following steps: acquiring real-time supply and demand data, an energy price signal and network topology information from a distributed energy management system, and preprocessing to obtain a structured dynamic supply and demand scene data set meeting a unified format requirement; aiming at a dynamic supply and demand scene data set, respectively detecting the fluctuation frequency and amplitude of an energy price on different time scales by adopting a time sequence analysis method, detecting the change condition of a network topology structure in real time by adopting a network analysis technology, and extracting key parameters reflecting scene dynamic characteristics from the change condition; and extracting a scheduling demand of cross-regional energy flow from the adjusted edge node permission configuration, and optimizing a cross-regional energy flow path in combination with real-time inter-regional supply and demand difference data and network state evaluation to obtain a globally optimized cross-regional energy scheduling scheme.

The invention discloses a method for obtaining high-temporal-spatial-resolution port traffic observation data from AIS data, and the method comprises the following steps: S1, carrying out the preprocessing of the historical trajectory of a ship based on AIS data; s2, based on a stay index formula and a K-means algorithm, dividing the preprocessed historical ship trajectory into a stay section and a moving section; using an isolated forest algorithm to extract center point coordinates of the stay section to obtain a ship travel chain; marking a staying type and a port to which the staying type belongs for each staying section based on a space rule and a process logic; s3, key indexes reflecting the port traffic state are constructed through time-space statistics of mooring and anchoring behaviors; a directed shipping network with ports as nodes is constructed, and traffic relation and characteristics between the ports are mined in combination with a network analysis method. According to the method, the timeliness and accuracy of port traffic situation monitoring can be improved, and the problems of slow updating, coarse granularity and high heterogeneity of traditional statistical data are solved.

The invention belongs to the technical field of low-altitude flight safety early warning, and provides a multi-modal data fusion method for low-altitude flight risk early warning. Comprising the steps of flight state data and meteorological data alignment processing, missing data filling, spatial feature extraction, spatial feature and flight state data fusion and space-time convolutional neural network and space-time diagram convolutional network analysis. According to the invention, the flight state and the meteorological data are fused, so that the prediction accuracy and reliability are improved; through the space-time convolutional neural network and the space-time diagram convolutional network, the space and time dependency relationship in the flight data and the meteorological data can be captured at the same time; through spatio-temporal data fusion, missing data can be accurately aligned and complemented, and the data quality and the real-time performance of the system are improved.

The invention discloses a rice nitrogen response regulation and control network analysis and breeding target identification system and method based on multi-omics data. According to the system, organic combination of regulation and control network construction based on single or multiple varieties of materials, key transcription factor recognition and accurate positioning of regulation and control areas where transcription factors play roles is achieved through an expression-chromatin accessibility correlation research method, and cis-trans effect distinguishing of the regulation and control areas is achieved through a deep learning model. The method comprises the following steps: carrying out nitrogen starvation pretreatment on rice, then carrying out nitrogen resupply, collecting a root sample, and carrying out ATAC-seq and RNA-seq sequencing; an eCAAS method is adopted to construct a regulation and control network, and key transcription factors are identified and accurately positioned; the chromatin accessibility difference of different varieties is predicted through a deep learning model, the cis-action effect and the trans-action effect are distinguished, an upstream transcription factor target is provided for genes dominated by the trans-effect, and haplotype and editable regulatory region targets available for direct breeding are provided for genes dominated by the cis-effect.

The invention discloses a network data integration analysis system and method based on a model context protocol MCP, and relates to the technical field of computer networks. The system comprises an MCP-LSP adaptation layer, a network analysis language server, an IDE plug-in, a remote cooperation management module, an MCP analysis engine, an LSP client and an IDE UI component. According to the invention, deep fusion of the network protocol analysis capability and the integrated development environment is realized; by constructing an MCP-LSP adaptation layer, a network analysis language server can convert a semantic analysis result of an MCP analysis engine into an LSP standard message format, and standardized services of functions such as protocol analysis, session state and anomaly detection are realized; the working efficiency of a developer is improved by the IDE plug-in; the remote cooperation management module realizes synchronization and sharing of session states, improves integration, expandability and cooperation efficiency of network analysis, and is suitable for diversified requirements of modern software development teams.

The invention provides an IBS micro-ecological transplantation intelligent prediction method and system based on multi-omics driving, and relates to the technical field of biomedicine. The method comprises the following steps: establishing a multi-omics data fusion subsystem to collect metagenome, metabolome, host genome and clinical phenotype group data of a target patient; inputting the data into a flora-metabolite combined network analysis model to construct an interaction network and extracting features; generating an incidence matrix based on the features and the host genome data and calculating indexes; generating indexes through a dynamic response algorithm in combination with the clinical phenotypic data and the indexes; and outputting a curative effect prediction result by using a transfer learning framework combined with modeling. The system comprises a data acquisition module, a network analysis module, a correlation calculation module, a dynamic response module and a joint modeling module. According to the method, multiple omics data are integrated, the flora and host relation is accurately mined, intelligent prediction of the micro-ecological transplantation curative effect is achieved, powerful support is provided for IBS personalized treatment, and meanwhile data processing and safety guarantee measures are taken.

The invention relates to the technical field of chip testing, in particular to chip internal signal transmission impedance matching testing equipment which comprises a vector network analysis module and a testing socket module, the testing socket module is internally provided with an inert gas cavity and a cylindrical cavity groove, the inert gas cavity and the cylindrical cavity groove are communicated with each other, and the inert gas cavity and the cylindrical cavity groove are communicated with each other. Gas in the inert gas cavity is inert gas; according to the impedance matching test equipment for signal transmission in the chip, the probe part is actively controlled to retract during test, and the probe part is controlled to eject out to establish test connection after the chip is placed in place, so that hard friction or collision between a solder ball contact of the chip and the probe part can be effectively avoided.

The invention provides a cell specific transcription factor regulatory network analysis method and a visualization platform, and relates to the technical field of bioinformatics, the method comprises the following steps: constructing a gene regulatory network through a GRNBoost2-cisTarget-AUCell-Cell GRN workflow based on a transcription factor in combination with a motif database; screening a direct regulation relationship in combination with the database, and calculating an activity score of a regulator in each cell; based on activity scores and cell type annotation results, grouping the single cell data by using a unified manifold approximation and projection (UMAP) dimensionality reduction method and a Leiden clustering algorithm, and displaying the following results through an interactive visualization tool: a cell clustering UMAP graph, performing color marking according to cell types; a UMAP graph and a heat map of transcription factor regulator activity; according to the visual map of the gene regulation and control network, transcription factors and target genes are distinguished through node shapes, and regulation and control relations are marked through line weights and colors. According to the invention, an accurate regulation and control network can be provided.

The invention discloses a city management AI dispatch algorithm and system based on historical mining and responsibility matching, and the method comprises the steps: building and dynamically updating a city management element evolution graph through obtaining the multi-mode description information of a city management case and the real-time state data of disposal resources; calculating potential disposal effects of different candidate dispatching schemes by using a causal inference engine, and generating a comprehensive efficiency estimation vector; on the basis, a multi-target reinforcement learning strategy is adopted to generate an optimal dispatch instruction, and system parameters are continuously optimized through online element learning during execution; cooperative processing network analysis is activated for sudden complex events, and responsibility atlas reconstruction is triggered when the matching efficiency is low. According to the method, the accuracy and efficiency of case disposal are remarkably improved, disposal timeliness optimization, resource load balancing and improvement of the first solution rate are realized, and meanwhile, the adaptive capacity and continuous optimization efficiency of the system to complex scenes are enhanced.

The invention discloses a network protocol analysis system based on a model context protocol MCP, and relates to the technical field of computer networks. The system comprises an MCP module, a network analysis module, a data source module, a context module, an MCP interface, a data source interface and a network analysis interface. An MCP module, a network analysis module, a data source module and a context module. All the modules work cooperatively through standardized interfaces to form a unified network protocol analysis platform. The system supports a context-driven tool calling process and a double-channel execution structure, control logic and data transmission are decoupled, and efficiency and expandability in a large-size Pcap data processing scene are improved. Through a unified semantic modeling and context state tracking mechanism, through analysis of a multi-layer protocol, multi-round traceable execution of a task and structured expression of an analysis result are realized, and infrastructure support is provided for a modularized, pluggable and reusable intelligent network protocol analysis platform.

The invention relates to an ore control element extraction and weight determination method based on a knowledge graph, and the method comprises the following steps: constructing a geological mineral knowledge graph: collecting geological text data, extracting entities and semantic relationships in the geological text data, and constructing the geological mineral knowledge graph; network analysis and simplification: utilizing a community clustering algorithm to divide an ore deposit, and combing and simplifying a complex geological knowledge map in combination with a modularity algorithm process; subgraph generation: constructing a series of subgraphs according to community categories of nodes, and forming an information set which completely and structurally represents specific geologic features and metallogenic conditions; weight calculation and integration: quantifying the indicating significance of different geological entities on ore deposit prospecting through an entity-relation weight result, and defining the weight of ore control elements; and element screening: sorting according to the weight values, and rapidly screening out geological entities which have important influence on ore deposit mineralization as ore control elements. According to the invention, an intelligent and automatic decision support tool is provided for geological prospecting work.

The invention provides a pelvic floor muscle function exercise method and system based on remote digital guidance, and relates to the technical field of remote rehabilitation guidance, and the method comprises the steps: arranging a multi-channel micro sensor array at a preset position of a pelvic floor muscle group, and collecting electromyographic signals and tissue deformation data in resting and contraction states; generating a biomechanical model through Fourier transform and multi-dimensional mapping calculation; performing convolutional neural network analysis on the model and a standard physiological database to generate functional damage positioning data; the remote medical data center calculates a neural pathway activation parameter based on the injury positioning data, generates a training instruction and converts the training instruction into a biological feedback control signal; collecting real-time biomechanical data in the training process, calculating a muscle group synergy efficiency value and generating a fatigue characteristic curve; training parameters are adjusted in real time through wavelet transformation and deep reinforcement learning network analysis, a training scheme iteration closed loop is formed, and precise remote guidance of pelvic floor functional rehabilitation is achieved.

The invention relates to the technical field of elevator safety monitoring, and discloses an elevator guide rail abrasion online monitoring and deep learning early warning system. The system comprises an active excitation multi-mode sensing end and a causal inference residual network analysis engine, and the analysis engine compares multi-physical field response data collected in real time with a theoretical health response signal under a current working condition based on a health response baseline model trained under a health state; the method comprises the steps that firstly, a multi-dimensional residual signal capable of separating working condition interference is generated, then, a system executes online self-calibration of a cross-modal sensor through physical constraints contained in a model, the effectiveness of the signal is judged, finally, the effective residual signal is input into a causal inference network, and the specific reason of guide rail abrasion is recognized and traced. The technical problem that the monitoring result is unreliable due to working condition interference, unknown abrasion reasons and sensor faults is solved, and high-precision, traceable and high-reliability online monitoring and early warning of elevator guide rail abrasion are achieved.

The invention relates to the technical field of graph data processing, can be applied to the medical field and the financial science and technology field, and discloses a dynamic feature enhancement method and device based on a graph convolutional network, equipment and a medium, which are applied to a dynamic investment relation network analysis scene or a multi-modal medical knowledge graph scene. Carrying out pretreatment on the raw materials; generating a target dynamic adjacency matrix through the self-supervised graph convolutional network; performing spatial-temporal feature aggregation processing based on the target dynamic adjacency matrix and the target historical graph sequence to obtain a global graph; local neighbor comparison loss calculation and global structure loss calculation are carried out respectively, parameter adjustment and model training are carried out on the self-supervised graph convolutional network according to a local comparison loss value and a global comparison loss value, and a target dynamic feature enhancement model is generated; and outputting a target enhanced feature based on the to-be-processed dynamic graph data through the target dynamic feature enhancement model. According to the method, the comprehensiveness and accuracy of feature extraction are improved.

The invention particularly relates to a spatial analysis method based on large model agent driving. According to the spatial analysis method based on large model agent driving, syntactic analysis and intention recognition are carried out on a spatial problem described by a natural language through a large model, and spatial-temporal clustering, network analysis and a field simulation algorithm module are dynamically matched; packaging a heterogeneous algorithm into a standardized service component based on a model context protocol (MCP), and automatically constructing an analysis process chain; and integrating a GIS engine and a visual template library, generating a dynamic thematic map according to the processed spatio-temporal data through the visual engine, and superposing multi-dimensional analysis annotations. According to the spatial analysis method based on large model agent driving, the full-chain intelligent capability from data processing to decision support is constructed, the efficiency and precision of spatio-temporal evolution modeling are remarkably improved, meanwhile, the system integration and maintenance cost is reduced, the method is suitable for high-complexity scenes needing real-time spatio-temporal data analysis, and the method is suitable for popularization and application. And the method has remarkable technical innovation and industrial application value.

The invention belongs to the field of natural language processing, particularly relates to an aviation safety accident report analysis method based on topic modeling and a word co-occurrence network, and aims to solve the problems that an existing topic modeling method is limited in understanding ability in aviation safety accident text analysis and cannot quantitatively reveal key causes. The method comprises the steps that a report text is acquired and preprocessed; based on a preset semantic fusion enhanced topic modeling engine keyword, obtaining a semantic fusion enhanced feature vector, and extracting a topic structure by adopting an improved deep embedding clustering model; and constructing an aviation safety accident word co-occurrence network based on a dynamic cosine similarity threshold value, performing network analysis, and determining key risk factors. According to the method, structured topic mining and deep learning semantic extraction are dynamically fused, and a network analysis method is combined, so that deep analysis and risk identification of the aviation safety accident report are realized, and the limitation of a traditional method on capturing text deep semantic information and subtle semantic difference is broken through.

The invention discloses a top coal fracture network reconstruction method based on three-dimensional modeling, and belongs to the field of mining informationization, and the method comprises the steps: collecting top coal fracture initial data through on-site survey and a sensor, analyzing the morphological change characteristics of the top coal fracture initial data, and obtaining a narrowing distribution model of fractures from an opening to a deep part; geological condition parameters are obtained based on the narrowing distribution model to determine the non-uniformity degree; a three-dimensional dynamic index is extracted from the non-uniformity degree, a preliminary space frame is constructed, and the expansion state of the fracture in the depth direction is obtained; fusing the expansion state of the fracture in the depth direction with the geological condition parameters, performing fracture behavior simulation, and determining a dynamic adjustment scheme of the spatial network; obtaining an optimized three-dimensional model based on the dynamic adjustment scheme; and a fracture behavior prediction value is extracted from the optimized three-dimensional model, and a final top coal fracture network reconstruction result is obtained. According to the method, the accuracy and reliability of fracture network analysis in a complex geological condition environment are effectively improved.

Embodiments relate to generating specialized large language models by performing transfer learning on a base large language model. The base large language model is trained using network traffic capture files as training data to predict information in a network traffic capture file during inference. The base large language model is modified into specialized large language models for including in different applications for performing communication network analysis. In this way, the specialized large language models may be developed in an expedient and efficient manner by leveraging the training performed on the base large language model.

The invention relates to the technical field of network analysis, and further relates to a network toughness index multi-dimensional quantitative evaluation method, which comprises the following steps of: 1, acquiring original topological data of a target network, and constructing a topological base toughness index of the network under the condition of no external impact; 2, calculating a crisis exposure factor; 3, calculating the mediation centrality of each node; calculating a structural hole limit system of each node; combining the mediation centrality and the information redundancy degree to generate an adaptation-recovery index for measuring the self-healing and regeneration capacity of the network; and step 4, carrying out impact resistance correction on the topological base toughness index and the crisis exposure factor, then carrying out index weight amplification according to the adaptation-recovery index, and finally generating a multi-dimensional network toughness comprehensive index in combination with stability correction of network toughness fluctuation. According to the method, network vulnerability identification and toughness strategy optimization based on the structure attribute and the dynamic historical response can be realized.

The invention belongs to the field of transformer pile head recognition, and particularly relates to a distribution transformer pile head automatic recognition system which comprises a data integration module, a data processing module, a system control module, a waveform parameter calculation module, an excitation circuit analysis module, an equivalent inductance conversion module and a looseness recognition algorithm module. According to the scheme, the high-cost limitation of a traditional vibration sensor or 3D modeling is broken through, and remote monitoring of looseness of the transformer pile head can be achieved through equivalent excitation inductance conversion and waveform similarity analysis without additional hardware transformation based on voltage and current data collected by an existing digital platform (a resource middle platform, a marketing middle platform and the like) of a power grid. Compared with the traditional scheme in the industry, the method has the advantages that the hardware transformation cost is reduced by more than 70%, the problem of missing inspection of manual inspection is avoided, the average recognition time of the pile head loosening fault is shortened to be within 15 minutes from 4 hours of traditional manual inspection, the monitoring efficiency is remarkably improved, and non-intrusive and low-cost accurate sensing of the state of the transformer is realized.

The invention relates to an earth pressure balance shield construction safety toughness dynamic evaluation system and method based on an extended cloud model and a network analysis method, and the system comprises an index system construction module which builds a multi-level toughness evaluation system based on a literature measurement and factor analysis method; an entropy weight TOPSIS weight calculation module objectively calculates the initial weight of the index through an information entropy theory; the ANP network weight optimization module corrects and optimizes the global weights of the indexes by constructing an inter-index nonlinear dependency network and a feedback mechanism; and the extended cloud toughness evaluation module quantifies qualitative indexes into membership degrees for different toughness levels based on expectation, entropy, hyper-entropy and other digital characteristics, comprehensively integrates weights and the membership degrees, and finally outputs soil pressure balance shield construction safety toughness levels and targeted optimization strategies. According to the method, multi-dimensional toughness quantitative evaluation and dynamic prevention and control of the construction safety risk of the earth pressure balance shield (EPB) can be realized under the complex stratum condition.

The invention discloses an intelligent team forming system and method, relates to the technical field of artificial intelligence and big data technology cross application, and aims to solve the problems of low team forming efficiency and poor team forming effect of a traditional team forming system. The system is provided with a user input module, a large language model support layer, a dialogue type information acquisition module, a multi-dimensional user portrait construction module, a self-adaptive intelligent matching recommendation module and an interpretable matching decision module to form a complete intelligent interaction closed-loop system. When a team formation demand exists, the system performs deep semantic understanding, multi-strategy fusion and graph network analysis to calculate an optimal team combination, and provides clear matching decision interpretation; team forming result feedback is collected, matching strategies and user portraits are continuously optimized, and the accuracy of future recommendation is continuously improved; the team forming quality and the user experience are continuously improved; the problems that a traditional team forming system is low in team forming efficiency and poor in team forming effect are solved.

The invention discloses an intelligent talent allocation management system and method for chain enterprises, and the method comprises the steps: collecting the flow data and knowledge transmission records of each store, recognizing organization memory nodes through network analysis, and constructing a knowledge fracture risk conduction diagram; frequency features are extracted based on shock wave simulation and Fourier transform, and a cascade influence path of personnel loss is predicted; obtaining a capability-load mismatch coefficient through phase analysis, and generating a deployment opportunity window; a personnel flow potential field is constructed, capability diffusion simulation is carried out, and a multi-dimensional deployment decision space is generated by using tensor operation; carrying out gradient search in the decision space to identify an optimal balance point, generating a ripple allocation path set, and optimizing to obtain an optimal sequence; detecting tissue knowledge density change after deployment is executed, and triggering knowledge structure recombination based on a phase change critical point. According to the method, conversion from passive response to active prediction is realized, the risk of knowledge fracture can be effectively prevented, and talent resource allocation is optimized.

The invention relates to the technical field of power supply and demand prediction, and discloses a power supply and demand prediction method suitable for a complex market environment. The method comprises the following steps: collecting multi-dimensional real-time data of a power market, wherein the multi-dimensional real-time data covers a user-side load fluctuation sequence and a market electricity price fluctuation signal; constructing a power supply and demand dynamic prediction model based on the data, and outputting a theoretical supply and demand prediction value; comparing the theoretical predicted value with the actual measurement electric power data, carrying out multi-dimensional difference analysis containing load sequence deviation response delay, and generating a market-level difference coefficient matrix; inputting the matrix into a regional energy network analysis model, generating a supply and demand abnormal propagation path probability distribution diagram and positioning a potential market imbalance region in combination with power grid node transmission capability and economic main body distribution information; and a prediction adjustment strategy is adaptively configured according to the probability distribution diagram, high-frequency data monitoring is started for a high-probability imbalance area, policy disturbance testing is applied to an associated market subject, and stable operation of the power market is supported.

The invention discloses a safety and environmental protection management system and method based on multi-pollutant cooperative treatment, and relates to the technical field of environmental management. According to the invention, multi-pollution data is collected in real time through a multi-parameter sensor network; optimizing and constructing an optimized pollutant relevance model by adopting a graph neural network and a Nelder-Mead simplex method, analyzing a collaborative / antagonistic relationship among pollutants, and generating a multi-pollutant processing list; multi-target optimization is carried out on a three-dimensional target function containing the removal rate, the cost and the by-product amount through an improved genetic algorithm, an optimal scheme is selected, balance between the environment and economic benefits is achieved, a dynamic multi-pollution map is constructed, and a pollution migration path is accurately tracked; a by-product material flow network analysis system is established, and resource recycling is achieved through a directional separation and reprocessing technology; according to the method, the combined pollution analysis precision is improved, the treatment cost is reduced, and the occurrence rate of secondary pollution is reduced.

The invention relates to the field of life insurance management, and discloses a life insurance risk management system based on an AI large model, which is used for constructing an intelligent, dynamic and closed-loop optimized life insurance anti-money laundering risk management framework. Comprising the following steps: automatically analyzing an external unstructured supervision text by utilizing a large language model, and generating a structured rule set which can be dynamically updated; jointly inputting the rule set and real-time transaction data into a network analysis model based on multi-body system dynamics, and constructing a transaction network situation map to identify abnormal cooperative behaviors; a suspicious transaction report draft meeting supervision requirements is automatically generated by a large language model based on the graph; carrying out cross validation and confidence rating by fusing the offline data extracted by the OCR technology; and finally generating a standard report file and submitting the standard report file to a bank system. According to the invention, automation and intelligentization of the whole process from rule interpretation, risk identification, report generation to supervision report are realized, and the anti-money laundering risk management efficiency of the life insurance industry is improved.

The invention relates to the technical field of enterprise credit evaluation, and discloses a blockchain finance enterprise credit evaluation system, which comprises an enterprise credit evaluation system, and the enterprise credit evaluation system comprises a multi-source data acquisition layer, a credit processing engine layer, an algorithm and model layer, an intelligent contract layer and an application service layer. The multi-source data acquisition layer comprises a multi-dimensional data unit, an enhanced data management and control unit and an external data introduction unit; the credit processing engine layer comprises a data cleaning and verification unit, a learning model training unit and an association network analysis unit. According to the enterprise credit assessment system and method for block chain finance, the multi-source data acquisition layer, the credit processing engine layer, the algorithm and model layer, the intelligent contract layer and the application service layer are arranged, layered architecture design is adopted, bottom-layer data acquisition is performed, upper-layer application service is performed, all the layers are clear in division of labor, and an enterprise credit assessment function is cooperatively realized; the system has the characteristics of clear modularization and strong expansibility.