12727 results about "Large model" patented technology

The invention discloses an aerospace intelligent manufacturing large model construction method, which comprises the steps of collecting original data, performing preprocessing and data association, and constructing an aerospace intelligent manufacturing database; establishing a knowledge acquisition and structured conversion assembly line, a multi-dimensional associated domain knowledge graph, a knowledge quality control system and a dynamic updating mechanism, and constructing a professional knowledge base; aligning the cross-modal manufacturing data to generate a corpus; combining base general large model pre-training, injecting terminology semantics and multi-modal association capability, and completing knowledge migration; based on the pre-trained aerospace intelligent manufacturing large model, constructing an aerospace manufacturing cognitive agent, and forming a complex engineering problem solving framework; professional ability is optimized through a two-stage progressive multi-task training strategy, and dynamic adaptation of a production environment is realized in combination with an online learning and incremental updating mechanism. The intelligent level of aerospace intelligent manufacturing is remarkably improved.

Provided in the present application is an intelligent question answering method based on a collaboration between a large language model and a knowledge graph, relating to the technical fields of artificial intelligence and natural language processing, the method comprising: decomposing a complex question into a plurality of simple questions, and analyzing the degree of association between the simple questions and a basic function so as to form a multi-hop reasoning path; automatically extracting structured information from the simple questions on the basis of a multi-task learning framework of a large model, so as to construct a knowledge graph; and constructing a cumulative reasoning learning framework on the basis of a logic reasoning large model, and performing iterative verification on a process result formed by the knowledge graph on the basis of the multi-hop reasoning path, so as to correct the reasoning path until a correct answer is inferred.

The invention discloses an intelligent programming auxiliary method and system based on a multi-modal AI language model, and belongs to the technical field of programming auxiliary tools. The method comprises the following steps: a multi-modal input processing stage; a dynamic context modeling stage; a hierarchical semantic analysis stage; in the code generation stage, codes are generated in two stages by adopting a Codex-Plus large model; the reinforcement learning driven code optimization stage is used for carrying out multi-objective optimization and reward function design on the codes generated in the code generation stage; a multi-dimensional feedback stage; an interaction and visualization stage; code semantic deep analysis, dynamic context sensing, multi-target optimization generation and real-time interactive feedback are realized by fusing code texts, natural language description, developer behavior data and a domain knowledge graph, and programming efficiency and code quality can be remarkably improved.

The invention relates to a knowledge graph construction method and system based on a large language model, and the method and system achieve the automatic construction and dynamic maintenance of a knowledge graph through multi-modal data fusion, reinforcement learning and comparative learning joint optimization, teacher-student model knowledge migration, time sequence dynamic analysis and an incremental updating mechanism. Constructing a reinforcement learning framework, and taking accuracy and integrity as reward indexes to train a large model to extract an entity relationship; a large-scale knowledge graph is used as a teacher model, and conflict resolution and semantic alignment of newly added knowledge and an existing graph are realized through a graph attention network; a verification rule is dynamically generated based on historical data and domain knowledge, and relation periodicity and mutation points are detected in combination with Fourier transform and a CUSUM algorithm; and finally, generating a traceable knowledge graph through incremental updating and version control. And the multi-modal data processing precision, the entity relationship extraction dynamic adaptability and the knowledge graph maintenance efficiency are improved.

The invention discloses a building digital twinning three-dimensional reconstruction method and system based on a large model, and relates to the technical field of building digital twinning and three-dimensional modeling fusion, and the method comprises the steps: laying multi-source equipment in a building, collecting building multi-angle data, and carrying out type distinguishing and structured preprocessing. An adaptive deep network model is constructed and trained to identify building features, and the robustness of the model is improved by adopting a multi-type enhancement strategy. Point cloud splicing and coordinate unification are realized by using a fusion algorithm, and twin model updating and synchronization are realized based on a multi-dimensional threshold. According to the method, high-quality input is established for a depth model; building structure feature recognition has the advantages of robustness and precision; and the twinborn model has dynamic updatable capability. The three parts form a complete closed loop, the technical bottlenecks of a traditional reconstruction method in precision, stability and renewability are finally broken through, and a new building digital twinning three-dimensional modeling path facing a complex scene is achieved.

The invention discloses a large model enhanced code security detection method. The method comprises the following steps: dynamically associating CVE vulnerability features with code semantic representation through a knowledge graph construction module; extracting vulnerability features from a CVE vulnerability library by utilizing a knowledge graph construction module, and dynamically associating the vulnerability features with code semantics; based on the CodeQL standard, a query language is generated by adopting a large model and input into CodeQL for AST analysis, and code structure features are extracted; the large model generates a query language meeting the CodeQL standard, codes are analyzed through CodeQL, and key structure features are extracted; integrating a static analysis tool chain to carry out multi-dimensional credible verification on model output; integrating an SAST tool and a symbolic execution tool, and verifying the model output from different dimensions; and summarizing and fusing detection results of the enhancement layers, and determining a final detection result by adopting a decision optimization method. According to the method, through technical fusion and dynamic optimization, the problems of rule stiffness, one-sided detection and low result credibility are systematically solved.

The invention provides a standard document automatic generation and multi-dimensional auditing method and system based on a large model, and relates to the technical field of artificial intelligence, and the method comprises the steps: 1, building a distributed database of a multi-source document, and analyzing a heterogeneous text through natural language processing to obtain a standardized knowledge network; step 2, extracting index elements based on the standardized knowledge network, and forming a structured parameter library through verification and verification; and step 3, based on the structured parameter library, constructing a template library, analyzing user demands in combination with semantic matching, and automatically generating a standard document outline. The document generation efficiency and quality are improved, the manual auditing cost is reduced, and the auditing comprehensiveness and accuracy are enhanced.

The invention relates to a cross-domain intelligent reasoning method based on knowledge graph enhancement, and the method achieves the precise reasoning in a complex scene through the construction of a hierarchical knowledge expression framework and a dynamic optimization mechanism. A multi-source heterogeneous data fusion technology is adopted, subject fine-grained knowledge units are generated through multi-modal feature extraction, and a three-dimensional knowledge graph structure comprising a core common concept layer, a subject feature ontology layer and a dynamic semantic mapping layer is established; based on a path exploration algorithm driven by reinforcement learning, cross-domain implicit association is mined while subject independence is reserved, and controllability and interpretability of the reasoning process are achieved in combination with an attention fusion mechanism of a large language model. According to the method, the limitation of traditional unified ontology modeling is broken through, the problems of concept drift and path deviation existing in reasoning in the cross fields of medicine-finance, engineering-law and the like are effectively solved, and the accuracy and knowledge traceability of complex decision tasks are remarkably improved.

The invention discloses an NL2SQL optimization method and device based on a large model, equipment and a medium, and relates to the technical field of artificial intelligence, the method comprises the following steps: constructing a target metadata knowledge base, and obtaining an initial natural language query request; determining each target entity corresponding to the initial natural language query request, and determining missing target SQL elements in the initial natural language query request based on each target entity; generating a first cue word based on the initial natural language query request, the target SQL element and the target metadata knowledge base, and complementing the target SQL element based on the first cue word by utilizing the target large model to obtain a target natural language query request; and generating a plurality of candidate SQL statements corresponding to the target natural language query request by using the target large model, verifying each candidate SQL statement, and determining a target SQL statement from each candidate SQL statement based on a verification result. According to the method, the accuracy of the NL2SQL can be improved by utilizing a large model.

The invention provides a decision generation and execution method and system based on an AI agent, and the method comprises the steps: analyzing a user demand document through a natural language processing technology, and extracting key information to construct a structured cue word; then inputting the cue word into a private domain AI agent based on a large model, and generating a preliminary decision scheme in combination with a professional domain database; automatically generating adversarial introspection probe cues, and guiding an AI agent to carry out consistency, risk and constraint conformity evaluation on the preliminary scheme; the system collects feedback response of the AI intelligent agent, analyzes the feedback through a pre-trained graph neural network, and calculates a confidence score of a decision scheme; when the confidence reaches a preset threshold value, automatically generating an execution script according to the decision scheme; and the execution script automatically operates the target system through the preset API and generates an execution document. The whole process realizes a closed-loop intelligent decision-making process from demand understanding, scheme generation, self-verification and automatic execution, and the decision-making efficiency and reliability are remarkably improved.

The invention relates to the technical field of operation and maintenance management and control, and discloses an intelligent building remote operation and maintenance management and control system based on a large model and a cloud edge collaborative architecture, and the system comprises a configuration subsystem which is used for carrying out the reverse proxy and persistent connection configuration of edge gateway equipment in an intelligent building, and obtaining a TLS encryption bidirectional communication channel; the construction subsystem is used for constructing a cloud-edge-end three-layer cooperative computing architecture; the conversion subsystem is used for performing unified semantic abstraction and conversion on the heterogeneous protocol data to obtain semantic data in a standard JSON (JavaScript Object Notation) format; the rule processing subsystem is used for performing rule processing through a near-end decision engine of the edge gateway to obtain a local intelligent response result; the multi-dimensional analysis subsystem is used for carrying out multi-dimensional analysis through a cloud large model engine to obtain an equipment health score, a fault prediction result and an energy optimization strategy, seamless fusion and interoperation of heterogeneous system data are achieved through the system, and then global optimization and linkage control of the intelligent building are achieved.

The invention belongs to the technical field of virtual person generation, and particularly relates to a digital person video generation method based on a multi-modal large model, and the method comprises the following steps: 1, constructing a multi-modal data system; 2, multi-modal large model training and adaptation are carried out; 3, constructing a digital human three-dimensional model; step 4, performing semantic analysis and modal mapping; 5, generating a time sequence action and a mouth shape; step 6, building and rendering a virtual scene; step 7, audio and video synchronous rendering and synthesis; step 8, quality optimization and defect repair; and step 9, performing user interaction and iterative optimization. Through technical innovation and engineering, the core pain point in digital human video generation is solved, efficient, vivid and customizable content production capacity is provided for virtual anchors, intelligent customer service, enterprise training and other scenes, and the AI digital human technology is promoted to be applied to large-scale business from experiments.

The invention relates to the technical field of intelligent operation and maintenance and artificial intelligence crossing of a power system, in particular to an intelligent power work order processing method of an RPA fused multi-modal large model, which analyzes multi-modal work order data such as texts, voices, images and the like through a domain adaptation large language model, and realizes fault key information extraction and conflict resolution in combination with a dynamic knowledge graph; performing work order priority scoring and resource allocation by using space-time constraint reinforcement learning; an analysis result is converted into an automatic execution script through an RPA engine, and a whole-process closed loop of order sending, processing and feedback is achieved; meanwhile, a feedback optimization and conflict resolution cooperation mechanism is constructed, and the knowledge graph and the model precision are continuously iterated. The method improves work order processing efficiency and analysis precision, enhances decision scientificity, and is suitable for an intelligent operation and maintenance scene of a power system.

The invention relates to a cross-modal knowledge reasoning method based on a multi-modal large model. In a cross-modal knowledge reasoning process, an existing model is usually limited by single-modal information extraction and shallow feature fusion, so that deep semantic association among data such as texts, images and videos is difficult to fully capture. In order to solve the problem, the invention provides a model for fusing multi-modal information such as texts, images, videos, documents and the like, and processing of multi-modal data is converted into unified feature extraction, interaction and deep reasoning tasks by fully utilizing a supervision fine tuning strategy, a self-adaptive attention mechanism and a cross-language processing technology. The model adopts a modular design, integrates multi-source data complementary analysis, spatial-temporal feature modeling and emotional semantic analysis, and realizes multi-modal collaborative interaction, dynamic scene understanding, long video key event analysis and man-machine co-emotional response. Through sufficient training, the multi-modal large model shows excellent logical reasoning ability and emotion understanding ability in a complex cognitive task, and a brand new solution is provided for efficient extraction, deep semantic analysis and intelligent response of cross-modal information.

The invention relates to the technical field of intelligent data fusion deduction, in particular to a task planning method and system based on intelligent deduction, and the method comprises the steps: constructing a rule knowledge graph with weight annotation through integrating a real-time battlefield situation and a preset rule library, and carrying out the task planning through the real-time battlefield situation; a large-model intention understanding engine is used for analyzing a commander instruction to generate a five-dimensional intention vector, an initial scheme is generated by combining a double-engine mechanism of a rule hard constraint engine and a large-model soft constraint engine, and graph weights are dynamically adjusted through reinforcement learning to optimize a rule path. The system monitors node conflict frequency in real time, adopts a near-end strategy optimization algorithm to re-distribute weights for high-frequency conflicts, activates a new rule injection mechanism when the intention conformity is insufficient, and finally outputs a three-level structured scheme including a main scheme, an alternative scheme set and a risk early warning report, thereby realizing an intelligent closed loop from situation awareness to scheme generation. And the command decision-making efficiency and the combat collaboration are obviously improved.

The invention relates to the technical field of cluster scheduling, and provides a GPU heterogeneous cluster scheduling method and system oriented to large model training and reasoning, which constructs a set of complete cluster scheduling system by integrating multi-source information such as hardware features, running states and historical task data and applying technologies such as a clustering algorithm, a fuzzy comprehensive evaluation method and reinforcement learning. Comprehensive, intelligent and dynamic management and scheduling of GPU cluster resources are realized, the cluster scheduling system can significantly improve the execution efficiency of GPU heterogeneous clusters in large model training and reasoning tasks, the resource utilization rate is improved, the energy consumption is reduced, and the stability and adaptability of the system are enhanced. And an efficient and reliable solution is provided for large-scale deep learning application.

A system and method for efficient natural language processing combines large and small language models with a thought caching architecture. The system includes a router that directs prompts either to a large language model for thought generation or to a thought cache containing previously generated thoughts. When using the large model, generated thoughts are combined with the original prompt and routed through a smaller language model to produce responses. The thought cache stores reasoning patterns that can be retrieved and reused, eliminating the need to regenerate similar thoughts for related prompts. The system supports both local and cloud-based caching, enabling personal and enterprise-wide thought storage and retrieval. This architecture reduces computational overhead while maintaining reasoning capabilities, effectively extends context windows beyond traditional limits, and enables efficient scaling across different deployment scenarios. The system can operate with reduced resources by leveraging cached thoughts without requiring constant access to the large model.

The invention provides a shield intelligent auxiliary type selection system and method based on a large language model. The model selection system comprises a data input module, a rule knowledge base module, a large model reasoning module and a result generation module. The integrated decision-making system integrating a rule knowledge base, a deep learning model and expert system logic is constructed for the practical problems of complicated geological conditions, multiple rule constraints, high expert dependency and the like in shield construction, and the system combines a structured model selection rule and historical case data, and has the advantages of intelligence, standardization, self-learning, high efficiency and the like. The problems of low efficiency, high subjectivity, insufficient intelligent degree and the like of the existing shield tunneling machine model selection depending on artificial experience and partial standardized guide are solved, and the transformation of shield construction management from artificial experience to intelligent decision can be promoted.

The invention relates to an automatic driving large model training optimization method based on multi-scene data balance. Comprising the following steps: (1) constructing a real vehicle high-speed driving scene library; (2) constructing a visual language automatic driving large model, training by adopting an iterative training framework based on a real vehicle high-speed driving scene library, designing a multi-task joint loss function and a weight adaptive adjustment strategy, realizing multi-task target balance, and obtaining a trained visual language automatic driving large model; (3) dynamic simulation is carried out for the automatic driving working condition, and a high-fidelity simulation data test set is constructed based on simulation data; and (4) performing hyper-parameter optimization and lightweight processing on the trained visual language automatic driving large model according to the high-fidelity simulation data test set to obtain a scene data balanced automatic driving large model. According to the method, the large model reasoning speed is increased, and resource occupation is reduced, so that the judgment capability of the large model on dynamic working conditions and high-risk scenes is remarkably improved.

The invention relates to a concrete working performance measurement method and system based on a multi-modal visual large model, and solves the problem that rapid detection of concrete working performance parameters is troublesome, and the method comprises the steps: based on the spatial semantic understanding capability of the multi-modal visual large model, combining a multi-view stereoscopic vision and structured light scanning technology, and calculating the working performance of concrete; reconstructing a three-dimensional geometric structure of the concrete slurry, extracting morphological characteristic parameters, and forming characteristic vectors; inputting the feature vectors into a pre-trained multi-task neural network, fusing the spatial-temporal features and combining a rheological algorithm to identify various working performance parameters; integrating identification results for at least three times by adopting integrated learning, and verifying parameters based on a fluid dynamics basic equation through a fluid simulation platform; and based on the verification result, generating a mix proportion optimization suggestion containing the material components. The method has the advantages that non-contact rapid measurement of concrete working performance parameters is achieved, precision and efficiency are improved, and mix proportion optimization suggestions are provided.

The invention discloses a large model reasoning efficiency dynamic optimization and hardware sensing compression method. The method comprises the following five steps: S1, generating an input complexity signal representing calculation complexity; s2, synchronously monitoring a hardware resource index of the operation platform, and generating a hardware state signal reflecting a real-time load; s3, inputting the input complexity signal and the hardware state signal into a dynamic strategy selector, and generating a compression control signal through a pre-trained decision model; s4, according to the compression control signal, dynamic reconfiguration operation is executed on the large model weight and the activation value of the current reasoning task; and S5, performing reasoning calculation by using the reconfigured large model, and feeding back a hardware resource index to the step S2 in real time in the calculation process to form a closed-loop optimization link. According to the large model reasoning efficiency dynamic optimization and hardware perception compression method, the problems of low resource utilization rate, delay fluctuation and energy efficiency imbalance caused by a static compression method in dynamic input and heterogeneous hardware environments can be solved.

The invention belongs to the technical field of unmanned aerial vehicle intelligent navigation, and discloses a low-altitude economic unmanned aerial vehicle data processing method and system based on a large model, and the method comprises the steps: calculating the scene adaptation weight of multi-modal data through a dynamic attention mechanism based on a space-time alignment feature package, and carrying out the fusion to generate a multi-modal joint feature matrix; based on the multi-modal joint feature matrix, constructing a three-dimensional topological model of an urban airspace, predicting a dynamic obstacle trajectory in combination with a space-time diagram neural network, and generating a hierarchical navigation instruction set; the unmanned aerial vehicle executes a flight instruction according to the hierarchical navigation instruction set, and generates a flight state monitoring log by collecting data in flight of the unmanned aerial vehicle in real time; according to the method, the multi-modal joint feature matrix is generated through environment parameter driving weight distribution, and the complex scene sensing precision is remarkably improved.

The invention discloses an ST code generation method based on graph alignment coding large model and multi-agent collaboration, and the method comprises the steps: receiving an ST code programming demand inputted by a user through a demand analysis module, refining and analyzing the demand based on multiple rounds of interactive conversations of the user and insight agents, and generating a standardized ST code programming demand; the retrieval module receives a standardized ST code programming requirement, and retrieves and obtains related knowledge through a retrieval agent in combination with an ST code knowledge base; and the double-agent collaborative self-correction code generation module receives standardized ST code programming requirements and retrieved related knowledge, a graph alignment coding large model constructed based on a graph neural network and a cross-modal alignment technology serves as a coding agent to cooperatively work with a review agent, ST code structure information is injected into the large model, and a final ST code is generated. According to the method, high-accuracy and high-reliability ST code automatic generation can be realized, and the development efficiency of a PLC program in the industrial control field is improved.

The invention discloses a fault early warning method and system based on an AI large model, and the method comprises the steps: obtaining multi-source heterogeneous data, and carrying out the denoising and standardization processing, and obtaining fusion data; inputting into a feature extraction model, and outputting a feature vector set; identifying the dynamic operation mode based on a K-means algorithm to obtain an operation mode baseline; inputting a feature sequence model, and outputting a precursor feature sequence; calculating an abnormal score according to the precursor feature sequence, marking as abnormal if the score is greater than or equal to a threshold value, otherwise, marking as normal, and obtaining an abnormal detection result; evaluating a risk level according to a detection result; inputting the risk level into a fault analysis model to obtain fault cause distribution; determining optimized operation mode parameters according to the fault cause distribution; and performing deviation analysis on the data and the optimized parameters, and if a deviation value is greater than a threshold value, triggering an early warning signal. The method can solve the problem of insufficient recognition capability in a scene with variable fault types.

The embodiment of the invention discloses an intelligent contract auditing method and device based on a multi-modal large model and a medium, and relates to the technical field of contract auditing, the method comprises the steps that a to-be-audited target contract document is acquired, multi-modal data in the target contract document is analyzed, and the multi-modal data comprises text data, table data and image data; performing feature extraction on the multi-modal data to obtain multi-modal feature data, mapping the multi-modal feature data to a unified dimension space so as to calculate an association weight between each modality through a cross-modal attention mechanism, establishing cross-modal bidirectional link data, and obtaining multi-modal data; the multi-modal feature data comprises any one or more of text semantic features, table relation maps and image visual features; and based on the multi-modal feature data and the cross-modal bidirectional link data, performing single-modal self-consistency verification and cross-modal contradiction detection on the target contract document to generate auditing information of the target contract document.

The invention discloses a multi-modal knowledge graph construction method and device based on a large model and a program product, belongs to the technical field of artificial intelligence and knowledge engineering crossing, and particularly relates to a knowledge graph dynamic construction and evolution method based on a large language model technology and multi-modal data processing. The problems of symbol grounding and semantic understanding obstacle in the prior art are solved. According to the method, innovation and breakthrough are realized in three dimensions of knowledge acquisition, representation and reasoning by fusing deep learning and knowledge engineering technologies. The multi-modal knowledge graph construction method and device based on the large model and the program product are applied to the field of multi-modal knowledge graph construction and are suitable for specific task scenes such as intelligent question and answer, decision support and semantic search.

The invention relates to the technical field of drawing auditing, and discloses an artificial intelligence assistant for auditing a CAD drawing of a transformer substation. The artificial intelligence method for auditing the CAD drawing of the transformer substation comprises the following steps: obtaining a DWG drawing and an auditing specification PDF file through a uniform interface, analyzing a primitive by using a pyautocad or ezdxf library, extracting equipment information and a topological relation, constructing a position-based drawing knowledge graph, and storing the position-based drawing knowledge graph in a Neo4j database. And performing OCR and BERT vectorization on the PDF, and storing the PDF into an FAISS search library. A LangChain framework is utilized to integrate a knowledge graph and a text library to construct a mixed retriever, an optimization cue word is generated and input into a Deepseek large model for examination and reasoning, and finally, an error position is marked in AutoCAD with a red box. According to the method, the equipment information can be automatically extracted from the drawing, the topological relation of the equipment can be identified, and accurate and rapid auditing is performed in combination with related auditing rules, so that the substation drawing auditing efficiency and accuracy are remarkably improved.

The invention discloses a software automatic testing system based on generative artificial intelligence, which is characterized in that a software analysis module identifies attribute information of a UI component based on a multi-modal large model for a test object, and constructs a UI component knowledge graph according to a structured information document; the knowledge retrieval module receives the test requirements and retrieves related historical test cases, test scripts and related test data; the organization interaction module sends a test intention and demand information to the knowledge retrieval module for retrieval according to the input user test intention, and test demand knowledge is returned; sending the UI component knowledge graph to the software analysis module for searching the knowledge graph of the test object, and returning the UI component knowledge graph; a test generation module receives test demand knowledge and the UI component knowledge graph, and generates a test case and a test script; and the script execution module receives the test case and the test script, starts a test process and records a test result.

The invention discloses an intelligent inspection method for fusion of Internet of Things and virtual reality based on an AI large model, particularly relates to the technical field of industrial intelligent inspection, and is used for solving the problem of end-to-end response lag caused by multi-modal data fusion delay and resource competition under an existing layered architecture. The method comprises the following steps: synchronously acquiring heterogeneous data of target equipment through a multi-source sensor and visual equipment, and generating time-space synchronous data through cross-modal feature extraction and time-space alignment; computing resources are dynamically allocated to an AI model or a rendering pipeline in combination with cross-modal correlation analysis and environmental interference assessment, and key tasks are preferentially guaranteed to be executed; performing deep correlation reasoning on the multi-modal data by using an AI large model, and generating equipment state features and an abnormal region mask; and finally, superposing the abnormal features and the three-dimensional scene through a virtual-real fusion rendering technology to form a visual interaction interface. Efficient fusion and real-time interaction of multi-modal data are realized, and the accuracy of anomaly detection and the decision response efficiency of an operator are remarkably improved.

The invention relates to the field of data backup, in particular to an intelligent data backup method and system based on an AI large model. The method comprises the following steps: acquiring an enterprise global data list, performing intelligent data structure deconstruction and dynamic attribute mapping modeling, and constructing a holographic data semantic perception model; performing real-time transient risk mutation detection on the holographic data semantic perception model, and constructing an intelligent backup triggering mechanism; carrying out storage resource demand prediction based on an intelligent backup trigger mechanism, carrying out multi-storage cloud environment resource dynamic scheduling, and constructing an elastic backup storage resource pool; carrying out incremental backup analysis and self-adaptive compression coding to obtain an incremental backup coding packet; and performing dynamic backup sequence adjustment and intelligent incremental backup decision on the incremental backup coding packet based on the elastic backup storage resource pool, and constructing an intelligent incremental backup execution engine. According to the method, the reliability, the accuracy and the traceability of a backup result are improved through self-adaptive intelligent incremental backup.