184 results about "Information dynamics" patented technology

The invention provides a multi-modal enterprise credit risk assessment method based on a knowledge graph, which integrates data such as enterprise relationships, industry policies and supply chain information by constructing an enterprise financial knowledge graph, processes entity static attributes and associated information by using a multi-modal embedding technology, captures the associated information in combination with a heterogeneous graph neural network, and evaluates the credit risk of an enterprise. And the dynamic space-time attention mechanism mines time and space features of the time series data, identifies a core risk conduction path based on an attention weight, and finally fuses graph-level features, dynamic space-time features and business rules to output a structured evaluation result. According to the method, multi-modal data is effectively integrated, the problem of incidence relation modeling deficiency is solved, deep fusion of enterprise multi-source data and accurate extraction of risk features are realized, and the accuracy and interpretability of enterprise credit risk assessment can be effectively improved.

The invention discloses a medical history recognition method and system based on a large model and an expert strategy, and the method comprises the steps: collecting data in a medical network, and carrying out the cleaning and standardization processing of the data; extracting medical entities and relationships thereof from the text, and optimizing the rule set; the extracted entities and relations are fused into a knowledge graph, a multi-relation graph is constructed, and attributes are defined; storing the knowledge graph in a graph database, and receiving new data through a real-time data stream processing system; basic information and symptom description of a patient are collected, and key information is extracted by using a large language model; an inquiry path is dynamically adjusted, and specific symptoms are clearly and fuzzily answered or deeply discussed by questioning; and generating a structured electronic medical record by using the large language model. Through an intelligent pre-inquiry process, the system can collect detailed medical history information and generate a standardized electronic medical record before a patient officially sees a doctor, so that the information verification and supplement time when a doctor receives the doctor is greatly shortened.

The invention relates to the technical field of natural language processing and information retrieval, and discloses a deep dense document recall method based on multi-view vector fusion, comprising the following steps: S1, preprocessing user query and candidate documents to generate a standardized text; s2, respectively constructing multi-view semantic vector representation of the query and the document, wherein multiple views at least comprise a keyword view, a semantic extension view and an intention view; s3, calculating a similarity score between the query and the document for each semantic view, and fusing the scores of all the views through a dynamic weight; and S4, sorting the documents according to the fusion score, and returning a front Top-K result. By constructing multi-dimensional semantic representation of keywords, semantic extension and an intention perspective, multiple semantic information such as term accurate matching, context association and task target consistency is effectively fused. The dynamic weight distribution mechanism adaptively adjusts the contribution degree of each view angle according to the query content, and overcomes the defect of insufficient semantic coverage of a single view angle.

The invention relates to the field of human resource management, and discloses a tobacco enterprise human resource management auxiliary calibration method based on big data, and the method comprises the steps: obtaining multi-source heterogeneous data related to enterprise internal human resources, and constructing a structured human resource data model in combination with a data standardization processing mechanism and an abnormality elimination strategy; post portrait modeling is carried out on the structured human resource data model, a capability dimension nesting analysis method is introduced, key capability factors and weight distribution required by each post are extracted, and a post capability demand graph is constructed; based on the post capability demand map, fusing the staff portraits and the historical job data, and identifying the deviation between posts and the staff through a multi-dimensional feature matching algorithm to form a preliminary calibration suggestion set; and a dynamic service association analysis method is introduced, and key matching parameters in the preliminary calibration suggestion set are dynamically corrected in combination with latest service demand data and real-time task assignment information. The method has the advantage of improving the management efficiency.

The invention discloses a federated learning client selection method based on reinforcement learning and a federated learning system. The method comprises the following steps: a client performs local detection training and collects loss, delay data and other related data; the central server divides the clients by using mean shift clustering based on data distribution of the clients, and regards each cluster as an independent agent; the intelligent agent dynamically selects a client based on the multi-dimensional state information, and optimization selection is carried out by adopting an exploration strategy; the client uploads model update after local training; and the central server carries out aggregation updating, and the intelligent agent optimizes client selection through a multi-target reward function according to a feedback adjustment strategy, maximizes a model convergence speed and balances communication and calculation overhead. By using the method and the system of the invention, under challenged environments of data heterogeneity, computing resource limitation, communication delay and the like, the client can be intelligently selected, the training efficiency of federated learning is optimized, and the performance and generalization ability of a global model are improved.

The systems and methods disclosed herein enable dynamic selection of a routing model for generation of an output in response to a provided input (e.g., a prompt for a large-language model). Based on the selected routing model, the data generation platform can evaluate the input and / or other suitable system parameters (e.g., system resource usage) to determine a suitable model for processing the provided input. For example, the routing model can determine a technical application associated with the input and dynamically determine to modify the input prior to generation of the output based on system resource measurement values and / or other suitable information, thereby conferring efficiency, security, and accuracy benefits while preserving system resilience.

The invention relates to the technical field of digital engineering, and particularly provides a BIM-based engineering construction dynamic three-dimensional visual management method and system, and the method comprises the steps: collecting construction multi-dimensional data, carrying out the correlation mapping with an initial BIM model component after preprocessing, and forming a structured construction element data set; dynamically reconstructing an initial BIM model based on the data set, generating a three-dimensional dynamic twinborn body, and dynamically displaying information such as superposition progress, quality, resources and environment; a prediction analysis model is called to generate progress prediction, quality early warning and resource optimization suggestions, and a final scheme is determined after visual simulation verification; and updating the data set and the twin according to an execution result, and iteratively optimizing the prediction model. According to the scheme, dynamic integration and visual management of construction total elements are achieved, the timeliness, accuracy and intelligent level of construction management are effectively improved through intelligent prediction and closed-loop optimization, the construction efficiency is improved, and the management cost is reduced.

The invention discloses an intelligent blasting sequence control system for mixed loading explosives, which is provided with a data acquisition module, a data preprocessing module, a model construction module, a parameter optimization module, a multi-target control module and a data storage module to intelligently control the blasting sequence of the mixed loading explosives. Distance attenuation features, delay features, topographic features and blasting energy features of blasting data are constructed, the blasting prediction model is updated and iterated based on a neural network algorithm and historical blasting data information, and the performance of the blasting prediction model is evaluated. The number of hidden layers and the number of nodes in the blasting prediction model are updated and optimized through a control variable method, the blasting prediction model is optimized, when mixed explosive is adjusted and the blasting sequence is optimized, the optimal mixed explosive composition is searched through a search optimization algorithm, and the optimal mixed explosive composition is obtained through control signals in combination with real-time blasting data information. And the blasting sequence and delay are dynamically adjusted, so that the blasting of the mixed explosive is intelligently and accurately controlled in real time.

The invention relates to the technical field of intelligent logistics and supply chain management, and discloses a cross-border e-commerce logistics intelligent storage scheduling and transportation cooperation method based on AI path planning, and the method comprises the steps: building a monitoring module, a dynamic information capturing module, a data classification module, a data analysis module, a storage scheduling and transportation cooperation module and a management module; the monitoring module monitors storage and transportation information in real time through a sensor and monitoring equipment, the dynamic information capture module collects external environment data and internal abnormal events, the data classification module classifies the monitored data and the captured information, and the data analysis module constructs a path planning model according to the stored data of the data classification module. The system comprises a storage scheduling and transportation collaboration module, a storage scheduling and transportation collaboration module and a management module, the storage scheduling and transportation collaboration module generates an optimal path and calculates a storage efficiency index # imgabs0 #, a transportation path lifting coefficient # imgabs1 # and a storage-transportation collaboration index # imgabs2 #, the storage scheduling and transportation collaboration module optimizes the path according to a calculation result and sets an early warning mechanism, and the management module assists management personnel in monitoring and decision making through a visual interface.

The invention relates to the technical field of knowledge maps, and discloses a knowledge map completion method based on semantic-structure multi-level fusion. According to the technical scheme, for a training set positive example triple, a structured rationality score and a semantic correlation score are fused to screen high-quality training samples; in order to complement a query search relation path, screening a guiding path through a uniqueness index; integrating the query text, the entity description, the neighbor facts and the guide path to construct an enhanced input prompt; dynamic structure embedding is generated by using a relational graph convolutional network, the dynamic structure is mapped by an adapter module and then injected into a large language model, and completion prediction is completed by combining a fusion input fine tuning model. According to the method, through multi-level fusion of semantics and structural information, intelligent sample screening, prompt enhancement construction and structural information dynamic injection are achieved, the accuracy, reasoning ability and training efficiency of a large language model in a knowledge graph completion task are effectively improved, and the method is especially excellent in performance in complex relation reasoning and inductive completion scenes.

The invention discloses a flood control and emergency rescue plan generation method, which comprises the steps of S1, collecting flood control and emergency rescue original data, performing preprocessing and feature extraction, and labeling and confirming extracted feature data to serve as a training set; s2, performing LoRA fine tuning of a low-rank decomposition matrix on the basis of a pre-trained large language model, and performing large language model training operation through the training set; s3, constructing a flood control and emergency rescue field ontology model, learning distributed representation of the knowledge graph by adopting a graph neural network technology, and dynamically updating the knowledge graph and executing reasoning query according to real-time dangerous case information by establishing a dynamic reasoning mechanism; and S4, constructing a multi-modal information fusion framework which is used for carrying out information fusion processing on the knowledge graph reasoning result and then guiding the big language model to generate an emergency plan as a control signal. According to the method, the technical bottlenecks of low plan generation quality, difficulty in knowledge updating, insufficient multi-source information fusion and the like of a traditional method in a complex dangerous case scene are effectively solved.

The invention belongs to the technical field of engineering data processing, and particularly relates to a digital twinning-based engineering completion delivery data information extraction method, which comprises the following steps of: acquiring completion site entity data, constructing a digital twinning body of building information model and live-action three-dimensional fusion, and fusing semantic tag information based on an improved ICP (Inductively Coupled Plasma) algorithm. Performing space coordinate alignment and attribute association on the digital twinborn body to generate a completion information dynamic twinborn mapping map; a multi-level rule inference engine is used for extracting information items conforming to delivery standards, a dynamic risk propagation model is used for simulating the influence of data deviation on a delivery result through multi-node consensus verification data of a construction party, a construction party and a supervisor, and information extraction weight parameters are corrected in real time; and dynamically generating a delivery data packet and a visual auditing report and synchronously updating the digital twin archive library in combination with engineering change records and acceptance specifications. Therefore, the problems of incapability of capturing engineering parameter fluctuation, poor transmission efficiency and the like in the prior art are solved.

The invention relates to the technical field of power dispatching, in particular to a virtual power plant control system and method. Comprising a data fusion module, a dynamic optimization scheduling module, an intelligent prediction module, a user interaction module, a self-learning adaptive module, a cooperative control module, a safety protection module and a storage unit. Key information is accurately extracted; the dynamic optimization scheduling module is based on a deep reinforcement learning algorithm, fully considers various factors to generate a global optimal strategy, and overcomes the defects of local optimization; the safety protection module constructs a multi-layer protection system to guarantee the safety of the system; the user interaction module covers the functions of information display, demand response, service customization and the like, personalized demands of users are met, complex conditions of the energy market and power grid operation can be handled on the whole, the operation benefit and safety of the virtual power plant are improved, and popularization is facilitated.

The invention discloses an enterprise financial risk determination method and a related device, and relates to the technical field of artificial intelligence. Six dimensions of mobility, credit, market, operation, industry macroscopic and public opinion reputation are respectively processed through six special and precise agents including a mobility risk analysis agent, a credit risk analysis agent, a market risk analysis agent, an operation risk agent, an industry macroscopic risk analysis agent and a public opinion and reputation risk analysis agent; and full-quadrant scanning of enterprise financial risks is realized. The cross validation coordinator and the dynamic cooperation engine form a double-wheel drive, and the cross validation coordinator can obtain a cooperation agent identifier set, a cooperation task type and cooperation configuration information; the dynamic collaboration engine dispatches multi-agent parallel collaboration in real time based on the collaboration agent identifier set, the collaboration task type and the collaboration configuration information, information islands are eliminated, and misjudgment and missing report are remarkably reduced. Therefore, accurate enterprise financial risks can be obtained.

The invention relates to the technical field of agricultural product production management, and discloses an agricultural product production base information dynamic monitoring and analysis system. The system comprises a basic data acquisition module for acquiring base and land parcel information and coding and storing the base and land parcel information; the production process dynamic monitoring module is used for collecting and fusing production activity data according to product forms; the environment data integration module is used for collecting, processing and analyzing environment parameters through an Internet of Things sensor network; the detection data integration module is used for detecting product quality data by using multi-mode equipment, screening abnormal values and storing the abnormal values in a block chain; and the traceability code generation module is used for generating batch traceability codes according to the multi-module data so as to realize whole-process data association and consumer query. The problems of disordered information management, incomplete production monitoring, lagged environment monitoring, lack of association of quality detection, difficulty in tracing and the like in a traditional management mode are solved, the management level of an agricultural product production base is improved, the quality safety of agricultural products is guaranteed, the credibility of consumers is enhanced, and agricultural modernization development is promoted.

The present invention belongs to the field of natural language processing technology and intelligent knowledge retrieval technology, and provides an open-ended question implicit intention recognition method and system based on multi-agent reasoning. The technical solution is to use multi-language translation agents and information retrieval agents to construct multi-language background knowledge; based on the constructed multi-language background knowledge, conduct topic analysis, generate multi-domain expert agent information, and dynamically construct multi-domain expert agents; according to the guiding questions raised by the host agent, use multi-domain expert agents to generate their own implicit intentions and reasoning text content according to the guiding questions raised by the host agent; use the host agent to evaluate the quality of the implicit intentions and reasoning text content, and use the speech of each screened expert agent as a node to construct an implicit intention library. Effectively solve the deficiencies in processing complex semantic understanding and other aspects, and more accurately and comprehensively identify the implicit intentions in open-ended questions.

The invention relates to the technical field of maritime navigation and intelligent shipping, in particular to an electronic chart shipping relation network construction and dynamic updating method and system based on a chart database, and the method comprises the steps: obtaining electronic chart static data and shipping dynamic information, and carrying out standardized preprocessing to unify a space reference and an attribute format; defining node types and edge relation types of channel space elements based on the graph database, and constructing a shipping relation network; shipping dynamic information is accessed in real time, and the weight and the attribute of the side relation are dynamically updated to reflect the real-time state of the navigation channel; and receiving a user path planning query request, executing a path planning algorithm fused with the navigation constraint condition based on the updated network, and generating and outputting an optimal route, thereby effectively solving the problems of weak data association, difficult cross-scale fusion and poor dynamic adaptability of a traditional electronic chart, realizing intelligent management and accurate path planning of a channel relationship, and improving the user experience. And the shipping efficiency and safety are improved.

The invention belongs to the field of intelligent workshop scheduling, and particularly discloses a large model enabled workshop scheduling end-to-end self-decision engine, method and device. According to the method, the large model is positioned as a core decision maker instead of a traditional auxiliary tool, so that the powerful capabilities of the large model in the aspects of natural language understanding, context learning, complex logical reasoning and content generation are fully utilized; according to workshop real-time state information, a dynamically changing workpiece pool, a scheduling target input by a user through a natural language and emergency description, an autonomous and end-to-end scheduling decision can be directly carried out, so that the rapid response capability and intelligent processing level of a scheduling system to dynamic events are improved, and the scheduling efficiency is improved. And the application threshold is reduced through natural language interaction, the dependence on explicit programming and rule customization is reduced, a low-code development paradigm is developed, and the method has a far-sighted decision potential exceeding a traditional heuristic rule.

The embodiment of the invention provides a data processing method and system, electronic equipment, a storage medium and a program product. According to one method, after a first article classification identifier corresponding to a target article is determined according to multi-modal information of the target article, a tax rule suitable for transportation of the target article is obtained from a knowledge graph based on the first article classification identifier; the knowledge graph is dynamically constructed on the basis of import and export article control information, obtained in real time, of each region, and the knowledge graph contains a plurality of nodes and node relations; the nodes have two types, namely article classification identifiers and tax rules; the node relationship reflects the association relationship between the article classification identifier and the tax rule. And further, determining a tax value of the target article based on the applicable tax rule, thereby automatically generating a customs declaration file of the target article according to the tax value. Therefore, automatic generation of the customs declaration file can be realized, so that the customs declaration efficiency is improved, and errors and tedious processes of manual operation are reduced.

The invention relates to the technical field of industrial data management, in particular to an energy network dynamic game optimization method under a carbon quota constraint, and the method is suitable for strategy linkage and response relationships generated by different management subjects due to carbon resource allocation and scheduling policy adjustment under a carbon quota system. According to the method, various local policy adjustment events are simulated, the response influence of the events on other management subjects is analyzed, and the possible management conflict risk is recognized; on the basis, a strategy buffer area and a time sequence transition mechanism are introduced, and transition path planning from a local strategy to global coordination is achieved; and the management strategy is dynamically adjusted in combination with feedback information in the execution process. According to the method, the potential instability region can be dynamically identified in the operation process, local reconstruction and iterative optimization are implemented in real time, and the compliance and stability of an energy system in a complex carbon quota management environment are guaranteed.

The invention provides a novel traffic information dynamic time sequence diagram topological structure, which relates to the technical field of dynamic time sequence diagrams, and comprises the following steps: configuring a namespace and time granularity system of a traffic information dynamic time sequence diagram, setting a global timestamp rule and a propulsion strategy, defining an information identification bit structure, and setting a visibility and consistency control strategy. And a unified time window scheduling and snapshot life cycle management mechanism is configured. A dynamic extensible time sequence naming system is formed by establishing a unified naming space, time granularity delta t and a global timestamp rule, windowed organization and snapshot chain management of traffic information are achieved, the structure can automatically advance snapshot in a time driving mode and a data driving mode, time sequence consistency and real-time performance are guaranteed, and the time sequence naming efficiency is improved. The problems that node time management is disordered and data cannot be traced in a traditional traffic map model are solved.

The invention provides an electric vehicle charging demand prediction method and system and electronic equipment, and relates to the technical field of deep learning prediction. Comprising the following steps: acquiring historical observation data and corresponding time characteristic data of an electric vehicle charging station; an MIFM prediction model is constructed, an initial input information coding module receives initial data and carries out parallel embedding processing, and unified input representation containing node specific adaptive embedding is constructed; the spatial-temporal information dynamic fusion module obtains the unified input representation, captures explicit geographic space dependence and implicit function correlation in parallel through a two-channel dynamic graph learning mechanism, dynamically integrates multi-layer spatial-temporal characteristics by using an adaptive Kalman filtering fusion strategy, and generates an enhanced fusion signal; and the space-time dependency modeling and prediction module learns the spatial relationship representation based on the enhanced fusion signal by using a stacked graph attention network, captures time dynamics through a time sequence decoding module, and generates a charging demand prediction result of a future time step.

The invention relates to the technical field of information management, in particular to an electronic information dynamic scheduling and management system and method. Real-time electronic information data is collected through a system data acquisition module, a resource matrix is constructed based on the real-time electronic information data, and a task instruction receiving module receives a system task request and analyzes the system task request to generate a task description vector; a coarseness matching module traverses the initial information matrix data according to the task description vector based on an improved Hungary algorithm to perform coarseness matching; the fine-grained optimization module performs fine-grained optimization on the candidate node subset data by using a DRL-OPT dynamic reward and punishment reinforcement learning model, selects a node for maximizing long-term reward through Q-Learning and outputs an optimal node, and the system scheduling management module schedules a system according to a resource allocation instruction, can adaptively learn a system operation rule, and outputs a resource allocation instruction to the DRL-OPT dynamic reward and punishment reinforcement learning model to the DRL-OPT dynamic reward and punishment reinforcement learning model. And dynamically adjusting the strategy according to the historical distribution effect.

The invention discloses a natural resource monitoring multi-source heterogeneous spatio-temporal data fusion verification system and method, relates to the technical field of geographic information systems and spatial data processing, and is used for solving the problems of low fusion precision of multi-source heterogeneous data and difficulty in conflict processing. The system performs standardized packaging on the same geographic element of different data sources through an evidence packaging module to generate a standard evidence unit containing element type claim and data source credibility weight; the intelligent judgment module corrects the evidence probability based on the DS evidence theory in combination with the positioning precision, calculates the conflict degree and outputs a high-low confidence coefficient judgment result, so that the fusion reliability is improved; the semantic coupling module performs spatial topology and service semantic verification on the judgment result to generate a type recommendation sequence; the knowledge updating module dynamically adjusts data source credibility and semantic rule weight according to user confirmation information, a self-learning mechanism is established, and efficient conversion from multi-source heterogeneous data to high-credibility monitoring information is achieved.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a response optimization method, device, equipment and medium based on term consistency. Inputting a language model to generate a candidate response; performing term consistency comparison and context consistency comparison on the candidate responses to generate a sorting result; term anomalies and safety risks in the candidate responses are detected, and the sequence is adjusted according to a detection result; and outputting the target response and obtaining feedback information, and updating the term knowledge base according to the feedback information. Through linkage of the term knowledge base and the security policy, response term consistency, semantic coherence and security are ensured, the knowledge base is dynamically updated based on feedback information, and reliability and specialty of a language model in multiple rounds of dialogues are achieved.

The invention provides a low-altitude traffic service information fusion and interaction system and method, and relates to the technical field of low-altitude traffic management, and the system comprises a multi-source information collection module which is used for obtaining multi-dimensional data from different information sources; the dynamic weight information fusion module is used for calculating the dynamic weight of each information source based on the data credibility coefficient and the updating frequency factor of each information source, performing weighted fusion and generating low-altitude traffic comprehensive data; the traffic service information generation module is used for matching the low-altitude traffic comprehensive data with a preset service rule base to generate traffic service data; and the multi-terminal information interaction module is used for transmitting the traffic service data to different terminals by adopting different communication protocols. According to the system and the method provided by the invention, by integrating multi-dimensional information, the weight of the information source is dynamically adjusted, and the fusion accuracy is improved; the system adapts to various communication protocols, seamless interaction with different terminals is realized, and the service quality of low-altitude traffic service is remarkably improved.

The invention discloses a map construction method of a three-dimensional Gaussian SLAM (Simultaneous Localization and Mapping) algorithm based on depth information fusion, which comprises the following steps of: firstly, adopting a dynamic point cloud downsampling method based on depth information, so that the defect that structural information is easy to lose due to the fact that point cloud spatial distribution and geometric characteristics caused by the depth information are not considered in traditional random downsampling can be overcome; the sampling rate is dynamically adjusted through depth information, a key geometric structure is reserved while data redundancy is reduced, good geometric priori is provided for generation of Gaussian point clouds, and the generated Gaussian point clouds are more fit with the surface of an object; moreover, according to the method, the optimization efficiency in the map reconstruction process is improved through the perspective principle of depth information adaptive point size and simulation of human eye observation, and the geometric error of luminosity rendering of the reconstructed map is smaller; in addition, the depth information is optimized by utilizing multi-frame depth fusion, so that the geometric position generated by the Gaussian point cloud is more accurate, and the subsequent optimization time is reduced.

The invention discloses a video target tracking system based on historical information dynamic propagation and future frame prediction collaborative optimization, which belongs to the field of visual target tracking and comprises a dynamic attention token module, a multi-constraint future prediction module, a real-time bidirectional calibration module, a tracking decision module and a scene adaptive memory module. The dynamic attention token module is used for receiving continuous video frames and generating a multi-scale token sequence dynamically adjusted along with target states, and the target states comprise a shielding area, a movement speed and an appearance scale; the target tracking robustness problem in a complex scene is solved by dynamically fusing the historical trajectory features of the target and future state prediction, and the method can be applied to the fields of intelligent monitoring, automatic driving visual perception, unmanned aerial vehicle inspection, robot visual navigation and the like. A bidirectional optimization mechanism based on historical token propagation and future frame prediction breaks through the limitations of insufficient utilization of time sequence information and insufficient dynamic adaptability in the prior art.

The invention relates to a non-signalized intersection mixed vehicle group cooperative passing method based on an information dynamic game, and belongs to the technical field of intelligent traffic. The method comprises the following steps: constructing an urban non-signalized intersection mixed traffic scene, and dividing a non-signalized intersection area; according to the divided non-signalized intersection area and the obtained heterogeneous vehicle information difference, a mixed vehicle group subgroup division scheme is established, and a mixed vehicle group information dynamic game joint judgment rule is established; respectively establishing heterogeneous vehicle longitudinal kinematics models, establishing a hybrid vehicle group game priority collaborative decision-making mechanism, and constructing a hybrid vehicle group collaborative driving revenue function; and then a hybrid vehicle group driving strategy is optimized through the Stackelberg equilibrium theory, and the optimal control input with the hybrid vehicle group cooperative driving income maximization as the target is obtained through a reverse induction method. For a non-signalized intersection mixed traffic scene, the method can effectively ensure that vehicles safely, orderly and quickly pass through, and the passing efficiency is improved.

The invention provides an OCR (Optical Character Recognition) information dynamic verification method and system based on multi-modal fusion and a medium. The method comprises the following steps: firstly, acquiring OCR identification data and scene environment data, extracting a multi-modal feature set, then training a multi-modal quality evaluation model based on historical data, performing field correctness, format compliance and logic relevance scoring on each modal, calculating confidence to position a problem modal, and finally obtaining a multi-modal quality evaluation result. The method comprises the following steps of: obtaining an OCR (Optical Character Recognition) identification result, performing traceability analysis on a problem mode, implementing mode enhancement, recalculating an enhanced quality score and a confidence score, dynamically distributing each mode weight according to enhanced confidence, obtaining a global quality score after weight fusion, and finally judging whether the OCR identification result is qualified or not based on a global score threshold value. According to the method, the accuracy and the robustness of OCR information verification are remarkably improved through collaborative verification and dynamic weight fusion of the multi-modal information, and identification interference in a complex scene can be effectively coped with.