362 results about "Dynamic mapping" patented technology

An example operation includes one or more of rendering a graphical user interface within a software application including a plurality of elements, modifying locations of the plurality of elements within the graphical user interface based on user inputs on the graphical user interface, generating a dynamic mapping of the graphical user interface including the modified locations of the plurality of elements based on an execution of an artificial intelligence (AI) model on the rendered graphical user interface, and storing the dynamic mapping of the graphical user interface within a storage.

The invention relates to the technical field of digital twinning, in particular to a pile foundation construction whole process digital twinning system which comprises a twinning configuration module, a dynamic mapping module, a coupling analysis module and a twinning synchronization module. According to the method, a time axis driving model sequence is constructed based on space-time interpolation, burial depth coordinates, pouring time and segment numbers are dynamically bound, three-dimensional evolution mapping of a pile body model is achieved, axis coordinate deviation is combined with perpendicularity threshold extraction, virtual model coordinates are corrected in real time through point cloud registration, and a pump pressure value and settlement are fused to generate a pile sinking rate index. A bidirectional coupling relation between parameters and stratum response is established in combination with stratum counter-force and injection rate, the thickness of the model is adjusted according to a settlement rate difference value, the similarity of the injection rate and a counter-force curve is matched, a model parameter reverse updating mechanism is formed, and real-time intervention of construction abnormity, dynamic optimization of parameter coupling and model iteration correction are achieved. And the process controllability and the quality tracing precision are improved.

The invention belongs to the field of parallel computing architecture optimization, and relates to a matrix multiplication acceleration method and system based on dynamic computing resource mapping, and the method comprises the steps: constructing a dynamic topology model driven by tensor dimension features, and generating a thread block distribution mode according to matrix parameters and GPU hardware information; constructing a multi-dimensional resource scheduling strategy library, dynamically selecting an optimal thread block distribution strategy from the multi-dimensional resource scheduling strategy library, and generating a binding relationship between the thread blocks and the data blocks; calculating collaborative access logic of thread blocks and storage hierarchies based on block parameters and dynamic mapping function optimization; distributed calculation is carried out, calculation and data transmission are parallelized through pipelining and a double-buffering mechanism, and result aggregation across calculation units is completed synchronously through atomic operation and a barrier. According to the method, discontinuous memory access conflicts can be effectively reduced, the execution efficiency of the calculation instruction and the utilization rate of the cache space are improved, the parallel calculation process of accelerating and optimizing the general matrix multiplication is realized, and the data processing efficiency is improved.

The invention discloses a CAPEC vulnerability management system based on a large language model, and belongs to the technical field of network security. The system comprises three modules: a vulnerability-CAPEC dynamic mapping module jointly encodes vulnerability description and code context through a bimodal large language model, accurately associates vulnerability logic with a CAPEC attack mode in combination with comparative learning and knowledge graph construction, and breaks through semantic limitation of traditional rule matching; the adversarial repair code generation module is used for generating high-robustness repair codes through adversarial training and syntax tree verification by fusing CAPEC relieving suggestions and code features on the basis of the association result, so that the secondary vulnerability risk is remarkably reduced; and the full-process automatic verification and DevOps integration module performs multi-dimensional security verification such as symbolic execution, fuzzy testing and the like on the generated repair code, and deeply integrates a development tool chain to realize real-time pushing and closed-loop management of a repair scheme.

The invention discloses a modular reconfigurable production line control system integration method, which relates to the technical field of industrial automation, and comprises the following steps: establishing virtual mapping based on physical attribute parameters to form a production line digital twin basic model framework; collecting data in real time based on an on-site sensor, and establishing a digital twinborn dynamic mapping mechanism synchronous with a physical production line state; a reconstruction scheme is imported into a virtual environment, and key performance indexes are analyzed through a production line digital twin model rehearsal module combination process. A virtual production line model is constructed through a digital twin technology, a rehearsal and verification reconstruction scheme in a virtual environment is supported, trial and error time and cost required by traditional physical debugging are remarkably reduced, a reconstruction strategy is further optimized through a cloud AI algorithm, closed-loop optimization from virtual verification to physical execution is achieved in combination with edge end real-time control, and the real-time performance of the virtual production line is improved. The production line can quickly complete local or overall reconstruction according to production requirements, and the response speed and flexibility of the production line are greatly improved.

The invention relates to a campus Internet of Things terminal scheduling method based on digital twinning. The method comprises the following steps: defining a general twinning state representation structure; introducing a state mapping relationship, and establishing a one-to-many dynamic mapping relationship between a physical state and a virtual twinning state by using a finite state machine; when the state of the physical terminal changes, triggering multi-level response mapping of the twin in the virtual space; semantic fusion is carried out on twinborn real-time data, a scheduling sensitivity index is introduced, disturbance simulation is carried out based on past scheduling behaviors, and the potential influence of a certain scheduling behavior on the whole system is analyzed; carrying out local division on the situation map; using Bayesian estimation and Monte Carlo combination to sample and evaluate the feasibility and income of each solution set in a period of time in the future; simulating the expected influence of each scheduling strategy through a twin body, and selecting a scheduling path; unified modeling and semantic fusion of multiple types of terminals are realized, the intelligent level and predictive ability of scheduling decision are improved, the collaborative scheduling ability of the terminals is enhanced, and coupling unit level regulation is realized.

The invention relates to a time sequence knowledge graph multi-hop reasoning method and system oriented to the legal field. The method comprises the following steps: establishing a dynamic mapping relationship through a semantic association technology; extracting life cycle states of the legal provisions, and introducing a legal conflict detection algorithm; setting a revocation influence factor; determining time embedding representation, and introducing clause conflict matrix elements to construct a time sequence knowledge graph; constructing a space-time coupled completion model based on the graph attention network and the long short-term memory network to perform path completion; determining three selected agents, performing reinforcement learning, designing a reward function for each agent, constructing a collaborative arbitration mechanism in combination with a dynamic priority strategy and a weight adaptive algorithm, and performing multi-hop reasoning. By collecting multi-source legal data, the authority, the real-time performance and the relevance of the data are ensured; according to the method, the path reasoning capability in a low-coverage scene can be improved, so that a complex legal knowledge multi-hop path reasoning task can be accurately and efficiently completed.

The invention discloses a virtual-real combination-based power generation side industrial control system network security target construction method. The method comprises the following steps of: constructing a virtual-real combination target environment consisting of a physical equipment layer and a virtual model layer; the heterogeneous industrial control protocol between the physical equipment layer and the virtual model layer is analyzed, protocol semantic information is extracted, and a bidirectional dynamic mapping rule of a physical equipment state and a virtual model state is generated based on the protocol semantic information; based on a state change event of the physical equipment layer, according to a bidirectional dynamic mapping rule, synchronizing changed equipment state data to the virtual model layer in real time, and simulating protocol behavior logic corresponding to the equipment state data in the virtual model layer according to a security test requirement; and based on the attack instruction or the abnormal state signal generated by the virtual model layer, according to the protocol specification format of the target physical equipment, converting the instruction or the signal into an executable control command, and driving the physical equipment layer to execute an operation corresponding to the control command.

The invention discloses a mechanical arm natural language instruction control system and method based on a large language model, and belongs to the field of intelligent manufacturing. Aiming at the limitation that traditional mechanical arm control depends on pre-programming and a static rule library, a dynamic mapping mode from a natural language instruction to an atomic action sequence is designed, an atomic skill library including detection, grabbing, moving, placement and other operations is constructed, and semantic analysis and a multi-mode cooperation technology are combined, so that the atomic action sequence is obtained. And support is provided for man-machine cooperation of a flexible assembly task. The method specifically comprises the steps that a DeepSeek-Distil-Llam-8B large model and a LoRA fine tuning technology are adopted, and a natural language instruction is converted into an executable atomic action sequence; based on a transfer learning optimized YOLOv8 target detection technology and a binocular vision positioning technology, a sensing module adaptive to an assembly scene is constructed and is fused with a mechanical arm motion planning module, and positioning grabbing of parts and tools is achieved. And an interactive interface is built by combining a voice-to-text large model and a Gradio front-end framework, so that the convenience of man-machine interaction is improved. By optimizing large model reasoning and motion planning cooperation efficiency, response delay from instructions to execution is reduced, and an efficient and extensible solution is provided for man-machine cooperation in intelligent manufacturing.

The invention belongs to the technical field of artificial intelligence and high-performance computing, and particularly relates to a neural network large model efficient reasoning method based on multiple GPGPUs. The method aims to solve the problems of high communication overhead, non-uniform load, low resource utilization rate, high data transmission delay and the like among multiple processors. Dividing a calculation task into a plurality of sub-graphs through static analysis and mixed granularity partitioning of a model calculation graph; distributing the sub-graphs to the optimal GPGPU based on a weighted cost function in combination with heterogeneous resource perception and a dynamic mapping strategy; a global pipeline scheduling plan is constructed by using communication topology perception, and calculation and communication overlap are maximized; data are loaded in advance through a host side hierarchical caching and asynchronous prefetching mechanism, and transmission delay is hidden; multi-stream concurrent execution and event-based lightweight synchronization are adopted on each GPGPU, so that waiting overhead is reduced. According to the method, the reasoning delay can be remarkably reduced, the throughput and the hardware utilization rate are improved, and the method has good adaptivity and expandability.

The invention belongs to the technical field of cloud computing and edge computing collaboration, and particularly discloses an intelligent scheduling and optimizing method for a cloud-edge collaboration network based on reinforcement learning. By constructing the state sensing matrix and generating the action decision vector, the problem that a traditional scheduling method is insufficient in correlation analysis of multi-dimensional operation state data in a complex network environment is solved, and the comprehensive sensing capability of the operation state of the network node is improved; a dynamic mapping mechanism among the running state, the resource limitation and the task allocation strategy is established, the task allocation and resource scheduling strategy is automatically and differentially adjusted according to the real-time state of the node, and the optimal matching between the task demand and the resource supply and the dynamic balance between the performance and the efficiency are realized; through performance index monitoring and closed-loop feedback optimization, the scheduling effect is mastered in real time, continuous iterative optimization is performed on the reinforcement learning model according to objective data, and resource waste and scheduling delay are reduced.

The invention relates to the technical field of computer software engineering and cross-platform development, in particular to a system for assisting in transplanting Android language to ArkTS (ArkTS) of the swan Mongolia, which comprises a code input module for acquiring Java and Kotlin double-language source codes, an analysis module for analyzing codes by using an improved grammar analysis algorithm, and a storage module for storing the codes. The translation module constructs an abstract syntax tree based on an analysis result and generates an attribute description array, and the generation module generates an ArkTS interface code according to the attribute description array; the distributed computing architecture realizes efficient task scheduling and processing, and the post-processing optimization module guarantees the code quality. Through multi-module cooperation, the probability-rule fusion algorithm, the type dynamic mapping and other technologies, code transplantation can be rapidly and accurately completed, the large project transplantation period is shortened from several weeks to several days, the manual correction cost is reduced, the code conversion accuracy and the system stability are improved, and the development of the wide-gap application ecology is powerfully promoted.

The invention discloses an auxiliary control system and method for abnormal intervention of a manufacturing and assembling production line, and relates to the technical field of intelligent manufacturing workshop auxiliary equipment and physical execution control of the intelligent manufacturing workshop auxiliary equipment, and the system comprises a multi-dimensional physical state collection unit which is used for generating a multi-dimensional physical state data set; the dynamic mapping and anomaly detection unit is used for detecting an abnormal state in real time; the multi-process linkage intervention unit is used for generating a flexible intervention control instruction; the auxiliary control unit is used for executing an intervention action; and the closed-loop optimization unit is used for constructing feedback comparison and residual attitude error judgment of an intervention action effect and forming a self-adaptive correction closed loop of the auxiliary device. The flexible adaptive capacity and the plug-in mounting stability of the workshop auxiliary mechanical device are enhanced, and the workshop auxiliary mechanical device has the characteristics of non-intrusive mounting, modular expansion, multi-process rapid adaptation and the like, and is particularly suitable for auxiliary control and accurate intervention of various and batch workpieces in a complex assembly scene.

The invention relates to the technical field of distributed computing and computing power scheduling, in particular to a computing power resource fusion method based on a distributed flow pipeline, which comprises the following steps of: disassembling a user computing power request into a flow pipeline unit for packaging computing logic, an input / output interface and a resource demand label; meanwhile, computing power types, real-time load rates, memory occupancy rates, network round-trip delays, geographic positioning and energy consumption data of cloud edge end nodes are collected, a hierarchical topology network framework is constructed based on the collected data, node computing power available values are calculated, an inter-node transmission cost matrix is generated, a fault probability prediction model is constructed, and a fault probability prediction model is constructed. And finally, analyzing a data dependency relationship of the pipeline unit through a four-dimensional joint decision engine, executing dynamic mapping, and preferentially mapping the high-computing-power demand unit to a GPU cluster node, so as to realize non-interruption reconstruction during pipeline topology operation. And the global computing power resource utilization rate, the task operation efficiency and the service stability are improved.

The invention discloses a text and image fused online comment toxicity detection and filtering method and system, and relates to the technical field of natural language processing, and the method comprises the steps: extracting a semantic vector of a comment text through a RoBERTa-Marge model; the visual features of the image are extracted through an OfficientNet-V2 model; aligning heterogeneous modal features by adopting a double-flow contrast loss function; self-adaptive decision making of culture sensitivity: loading a regional sensitive rule table according to a user IP address, and dynamically adjusting symbolic semantics; calculating an intimacy correction factor based on the social relationship between the publisher and the receiver; and performing context weighted toxicity scoring, calculating a user historical behavior weight, and outputting a final toxicity probability. According to the method, a deep dynamic mapping mechanism of text and visual features is constructed, heterogeneous features are extracted through RoBERTa-Large and OfficientNet-V2 double-flow architectures, semantic space alignment is forced by utilizing comparative learning, and the problem of image-text splitting detection in the traditional technology is solved.

The invention provides a chip security key protection method and system capable of resisting side channel attack, and relates to the technical field of chips, which comprises the following steps of: obtaining a key operation request, performing address space mapping based on a dynamic mapping rule to obtain encrypted key data, decrypting the encrypted key data, fragmenting key intermediate data, and allocating an independent processing channel to perform decoupling operation; and injecting a time-varying noise signal to cover the association between the key and the physical characteristics, and finally performing recombination and reverse conversion to generate a final key output. According to the method, side channel attacks such as power consumption analysis and electromagnetic analysis are effectively resisted, and the security of key storage and operation processes is improved.

The invention relates to the technical field of data import, in particular to an intelligent Excel data import method and system based on dynamic mapping, and the method comprises the steps that the system traverses all Excel fields for each database field, calculates the feature similarity and field matching degree of the Excel fields, and generates a field mapping template set according to all field matching results; reading the Excel content according to rows, cleaning each row of content into a standard value, performing quality scoring, and outputting a target format data structure set; screening a structure alignment data row and a field structure exception record set which conform to a threshold value; the system reads the structure alignment data rows line by line, processes the extraction value of each field and outputs a legal data set; and extracting a field sequence set, searching whether fields marked as primary keys or unique keys exist in the field sequence set, and combining the fields into a uniqueness judgment set to judge whether the currently written Excel data exists in the target database table.

The invention belongs to the technical field of artificial intelligence, is applied to the field of financial science and technology and the field of medical health, and discloses a rule engine configuration method, device and equipment based on machine learning and a storage medium. The method comprises the steps that historical calculation data and performance indexes in historical calculation tasks are collected, and a training data set is constructed; a machine learning model is trained, and algorithm parameters of the rule engine are dynamically optimized through the training data set; configuring an expression and a user-defined function of a rule engine, and realizing analysis and execution of scripts through JEXL (JavaScript Exchange Library); establishing a dynamic mapping relationship between the algorithm configuration library and the business variables, mapping algorithm variable names and business variable names, and generating configurable algorithm rules; and analyzing task load and resource requirements based on a pre-trained machine learning model, distributing system resources, and repairing abnormal behaviors in a calculation process. According to the invention, the problems of low processing efficiency, high maintenance cost, unreasonable resource allocation and poor expandability in the prior art are solved.

The invention discloses a model deployment method and device, computer equipment and a storage medium, and the method comprises the steps: determining a target model from a model square through responding to a model deployment request; obtaining model support information of the target model based on the dynamic mapping table; obtaining computing power resource index data of each computing node corresponding to the model support information; and deploying the target model based on the computing power resource index data of each computing node and the dynamic mapping table. According to the method, insufficient or excessive computing power resources caused by computing power resource estimation deviation of a user can be avoided; according to the method, the computing power resources can be accurately and flexibly distributed and deployed for the target model, the situation that the computing power resources are insufficient can be avoided, and when the computing nodes break down, the computing power resource distribution of the target model can be dynamically adjusted according to the computing power source index data of each computing node, so that the overall resource utilization rate is improved.

The invention relates to the technical field of engineering cost, and discloses a data entry method for engineering cost software, which comprises the following steps of: S1, importing an engineering quantity list file in an Excel format through an interface of the engineering cost software, and automatically analyzing a data structure of the engineering quantity list file; s2, based on a preset intelligent matching algorithm, performing dynamic mapping on the Excel data field and a data field of a project cost software function module; s3, adopting a data type self-adaptive rule to automatically classify and convert numerical values, texts and compound data in the Excel; s4, based on a data association generation algorithm, automatically establishing a hierarchical relationship and a dependency relationship between the engineering quantity list items; s5, executing one-button data import, and writing the processed structured data into a project cost software database in batches; according to the method, high-precision automatic mapping of the Excel field and the software module is realized through an intelligent matching algorithm and a dynamic weight adjustment mechanism based on the feature vector similarity, the matching accuracy is greatly improved, and the manual correction frequency is greatly reduced.

The present technology pertains to receiving, by a multi-tenanted cloud-native headend, network traffic from a source that is directed to a destination within a network of a first tenant, where the network traffic is routed using BGP over a multi-tenanted BGP network, where the multi-tenanted BGP network utilizes route isolation to isolate the network of the first tenant from networks of other tenants making use of the multi-tenanted BGP network, mapping the received network traffic based on at least a source IP, source port, and virtual network interface (VNI) to a port associated with the network of the first tenant over the isolated BGP route, where the mapping is a stateful mapping, where the mapping is also potentially based on other information associated with the network traffic, and directing the received network traffic to a local address in the network of the first tenant.

The invention relates to the technical field of three-dimensional simulation scheduling, and discloses a three-dimensional simulation deduction and intelligent scheduling system for multi-scene collaborative operation. The system comprises a scene fusion modeling module, a dynamic deduction period calculation module, a resource dimension analysis module and a deduction strategy generation module. The scene fusion modeling module is used for generating a three-dimensional collaborative operation space model containing different operation scene space coordinate mapping relations and inter-scene resource flow paths based on multi-scene parameters configured by a user; a dynamic deduction period calculation module obtains the remaining duration from the current time to the preset deduction ending time, and calls a first dynamic mapping relation to calculate an initial deduction period; a resource dimension analysis module identifies the number of types of resources participating in the collaborative operation, and calls a second dynamic mapping relation to generate a periodic regulation factor; and the deduction strategy generation module performs weighted fusion on the two to obtain a self-adaptive deduction period, and formulates a three-dimensional simulation deduction strategy according to the self-adaptive deduction period to adapt to multi-scene dynamic change so as to realize efficient coordination of simulation and scheduling.

A codebase system that maintains and utilizes a dynamic mapping between a collection of tests and files within a codebase to ensure error detection and code validity. The codebase system accesses a collection of tests configured to process files within the codebase to identify errors. It then generates a mapping by executing these tests to determine the specific files each test operates on. Upon detecting updates to the test collection, the codebase system updates this mapping by re-executing the updated tests, identifying the new set of files for each test, and aggregating these updates into the existing mapping. When changes to the files in the codebase are detected, the codebase system accesses the updated mapping to identify which tests relate to the altered files. The codebase system then executes this relevant subset of tests to validate the modifications made to the files.

The invention provides a flow and form decoupling cooperative processing method, and relates to the technical field of business flow management, and the method comprises the steps: constructing a layered architecture, separating a flow control layer from a form service layer, defining a flow node jump rule by the flow control layer based on a BPMN standard, and dynamically defining a form structure by the form service layer through JSON Schema; a form identifier is declarated in the process definition file, a corresponding form template is matched through a dynamic mapping service according to context information, and the context information comprises a process instance state, a user role, a device type and a service type; establishing a data sandbox isolation mechanism, generating a unique data space identifier for each process instance, and managing data access authority through a multi-dimensional isolation strategy of a tenant level, a service level and a process instance level; deploying an event bus communication mechanism, triggering two-way communication between a process engine and a form engine through a predefined event type, and ensuring the consistency of cross-system operation by adopting a transaction compensation mechanism.

The invention discloses a data processing method and device, electronic equipment, a storage medium and a product. The method comprises the steps of obtaining a disk dynamic mapping request; in response to the disk dynamic mapping request, a data repair migration task is generated, and the data repair migration task is used for triggering disk dynamic mapping and data repair migration processes of the erasure code redundant group; and executing the data restoration migration task.

The invention discloses an electric power multi-protocol data conversion method and system based on object model dynamic mapping, and the method comprises the steps: receiving an original protocol message, recognizing a protocol type, extracting a logic name as a unique identifier of equipment, extracting an object identifier as a measurement point identifier, and extracting an electric energy array as a measurement point value; according to the protocol type and the equipment unique identifier, matching a corresponding object model template from a unified object model library; mapping the equipment unique identifier, the measuring point identifier, the measuring point numerical value and the acquisition timestamp obtained by analysis to corresponding fields in a template; and generating an object model data object conforming to a standard format, packaging the object into a message, executing an encryption authentication mechanism of a network layer and a transmission layer through the power Internet of Things security access gateway, and sending the message to an Internet of Things management platform. According to the invention, standardized, automatic and secure access of different protocol terminals is realized through protocol analysis and the unified object model library.

The invention provides a domain self-adaption-based generative attack and defense confrontation method. Directional interference on a black-box intelligent system is realized by constructing a generative confrontation network framework. The method takes a domain self-adaption theory in transfer learning as a core, builds a dynamic mapping relation between a white box model group and a black box target system, designs a dual-channel generative adversarial network, jointly supervises sample authenticity and cross-model transfer consistency through cross entropy loss and KL divergence constraint, builds a dynamic boundary feedback system, and achieves the dynamic boundary feedback. And a disturbance generation strategy of the generator is adjusted according to the real-time attack effect, and a self-adaptive attack and defense game closed loop is formed. Compared with a traditional black box attack method, the method has the advantages that migration robustness of an adversarial sample in a model heterogeneous scene is remarkably improved, stable attack efficiency can still be maintained when a defense system has protection mechanisms such as adversarial training and input reconstruction, and the method can be applied to the fields such as penetration testing of an intelligent security system and the like. And a technical support is provided for vulnerability evaluation of the intelligent system in a complex environment.

The invention provides a clustering-based QoS mapping method and device for a 5G-TSN, and relates to the technical field of 5G time-sensitive network converged communication. The method comprises the following steps: a TSN flow QoS acquisition module sends an acquired TSN flow QoS index to a TSN flow aggregation module; the TSN flow aggregation module performs flow aggregation processing on the TSN flow QoS index through an enhanced density space clustering algorithm to obtain an aggregated flow; the TSN flow aggregation module sends the aggregation flow to a 5QI mapping module; and the 5QI mapping module performs dynamic QoS mapping processing on the aggregation flow through a fuzzy logic algorithm to obtain a mapping result. According to the invention, the TSN service flow can be accurately matched with the 5G QoS parameter, and the problem that the traditional mapping method is rigid and poor in dynamic adaptability can be solved.

The invention discloses an ERP (Enterprise Resource Planning) system-oriented interface service design method, which relates to the technical field of computer software, and comprises the following steps of: loading and analyzing a standardized interface description file, extracting a calling party demand, constructing a data structure according to the calling party demand, and defining a preliminary field mapping rule; based on the preliminary definition and the data structure of the field mapping rule, a mapping rule file is generated and verified, a test case is constructed, the effect of the mapping rule is verified, and a final mapping rule file is dynamically generated according to the verification result; and according to the final mapping rule file, the interface service processes the external calling request in real time, adjusts the field mapping rule, updates the interface description file, and generates an updated interface description file. Through a dynamic mapping rule generation and verification mechanism, the problem that a static rule cannot deal with a complex or changing scene is avoided, and real-time optimization of the mapping rule is realized, so that adaptability and reliability are enhanced.

The invention provides a document adaptive conversion method and device based on a multi-modal large model and a medium, and belongs to the technical field of data processing. The method comprises the following steps: inputting a multi-modal document into a pre-constructed multi-modal large model, wherein the multi-modal large model comprises a multi-modal joint framework fusing a ViT visual model and an LLM model; using a ViT visual model and an LLM model to respectively extract multi-scale visual features corresponding to non-text content and semantic features corresponding to text content, and using a cross-modal attention mechanism to bidirectionally align the multi-scale visual features and the semantic features to obtain a multi-modal document; carrying out self-adaptive blocking on the multi-modal document by using a self-adaptive blocking strategy, and modeling a relative position relationship between blocks; establishing a dynamic mapping rule from a document element to an HTML tag, and mapping the multi-modal document into a front-end interaction component in combination with a natural language instruction of a user; and dynamically rendering the front-end interaction component in an on-demand mounting and resource isolation mode. The problem of insufficient document restoration capability in the prior art can be solved.