826 results about "Structure analysis" patented technology

A system for adaptively caching network communication protocols enhances efficiency across heterogeneous device environments through a multi-level cache architecture with device-capability-based tiers. The system collects endpoint telemetry data including device capabilities and operational constraints to classify endpoints and generate context-aware protocol variants optimized for specific device types. Protocol optimization opportunities are determined through structural analysis of message patterns and state transitions. The system performs protocol deduplication by identifying functionally equivalent variants and maintaining canonical representations to reduce cache redundancy. Cache synchronization across distributed nodes uses enhanced Merkle tree structures with protocol normalization processing. The system predicts communication needs based on historical patterns, network context, and endpoint constraints, enabling proactive cache management tailored to device capabilities. Integration with event-driven data communication systems enables seamless protocol selection and translation while maintaining compatibility between diverse endpoint types, from high-performance servers to resource-constrained IoT devices.

The invention discloses a medical inquiry logic chain generation and verification method and system based on a large language model, and relates to the technical field of artificial intelligence, the method comprises the following steps: S1, generating a logic chain framework, constructing a medical inquiry logic chain generation model, and embedding a structured logic template and professional knowledge constraints by taking the large language model as a core engine; s2, designing a two-dimensional multi-level verification framework, and performing automatic verification on the consistency and medical rationality of the generated corpus logic chain; and S3, designing an evaluation index system and a dynamic optimization strategy, carrying out grading marking on the problem corpus identified in verification, carrying out corresponding correction, feeding back a correction result to a model training link, and optimizing a logic chain generation rule. Through an NLP technology, a language model generation mechanism, medical knowledge structure analysis and an intelligent verification algorithm, the quality and the structuring degree of a medical inquiry data set and the application value of the medical inquiry data set in an intelligent inquiry system are improved, and high-quality and controllable professional corpus support is provided.

The invention discloses a method for carrying out data cleaning decision making and automatic tuning by using AI workflow, and relates to the technical field of data processing, the method comprises the following steps: obtaining original data from various heterogeneous data sources, and generating a correlation table and a result through field extraction, structure analysis and language attribute recognition; based on this, storage and task generation are performed, and standardized data and task queue information are output; identifying a quality problem through feature analysis, defining a target and constructing a cleaning task atlas, and generating cleaning metadata through preliminary screening and marking; and an AI cleaning workflow containing multiple strategy paths is constructed and optimized, after execution, a downstream feedback evaluation effect is combined and adjustment is carried out, and finally a multi-version cleaning result is integrated and output. According to the method, the adaptation capability to different types of data sources is effectively improved, the dependence on a static preset strategy is reduced, the manual intervention requirement is reduced, the tuning efficiency is improved, and the systematicness and normalization of data cleaning are enhanced.

The invention discloses an intelligent film and television scene synthesis method based on generative multi-mode script semantic mapping. The method comprises the following steps: firstly, performing semantic unit segmentation and dependency structure analysis on a movie and television play text, and establishing a script semantic mapping relationship; carrying out cross-modal feature mapping training by adopting a generative multi-modal semantic alignment method so as to generate an initial scene layout map; performing space-time consistency optimization on the initial scene layout map, and constraining a synchronization relation between a role action sequence and a camera visual angle according to a time sequence and space depth information; refining and reconstructing the scene layout map sequence to generate a rendering result sequence; and determining a lens switching rhythm and a focal length change track according to a rendering result sequence and a script semantic mapping relationship, and automatically generating a playable movie and television scene video with natural plot logic and uniform visual style. According to the method, the generative semantic self-adaptive synthesis from the script text to the video content is realized, and the automation and intelligence level of film and television production is improved.

The invention provides a multi-modal large model incremental training data screening method, and relates to the technical field of data processing, and the method comprises the steps: executing modal structure analysis on newly added multi-modal data, extracting each modal vector, calculating a semantic matching degree, and removing samples lower than a preset first threshold value; calculating a multi-level semantic distance between a sample embedding vector and a historical clustering center in a unified semantic space, and dividing a core semantic region sample, a boundary semantic region sample and a discrete semantic region sample according to the change rate of the multi-level semantic distance; performing semantic fine-grained alignment on the boundary semantic region samples, when multimodal unstable distribution is detected, executing local context reconstruction to repair semantic deviation, and if the multimodal unstable distribution is still unstable, removing the semantic deviation; performing multiple rounds of small-batch reasoning, calculating a semantic stability coefficient based on a semantic prediction result, and when the semantic stability coefficient is lower than a preset second threshold value, determining that the sample is a potential drift sample and removing the potential drift sample; constructing an incremental training data set; according to the method, the autonomy and accuracy of incremental training data screening are improved.

The invention discloses an aviation equipment detection and maintenance method and equipment based on target detection and a medium, and relates to the technical field of aviation equipment detection, and the method comprises the steps: inputting a correction image set into a pre-trained target detection network, carrying out the multi-scale feature analysis and edge texture recognition, and generating a part detection result; establishing a component topological relation according to a component detection result, and comparing the component topological relation with a standard configuration by using a graph structure analysis method to generate topological consistency information; performing defect positioning and quantitative analysis on the topological consistency information, extracting a defect position, a defect type and a defect quantitative index, tracking an extension track of the defect quantitative index along with time through continuous time frame difference, and generating a defect parameter; and performing risk assessment on the defect parameters, generating and executing a maintenance scheme, verifying the maintenance effect through real-time image detection, and forming a detection and maintenance closed loop. According to the invention, the accuracy and integrity of aviation equipment detection are finally improved.

The invention relates to the technical field of construction monitoring, in particular to a temperature monitoring and stress monitoring method and system for a fan foundation. The method comprises the following steps: collecting concrete hydration heat release monitoring signals, performing temperature gradient distribution excavation and finite element numerical simulation, and constructing a concrete pouring process temperature evolution model; concrete surface temperature monitoring parameters are obtained, multi-time-point surface layer temperature calculation is carried out, table environment temperature fluctuation mapping is carried out on the concrete pouring process temperature evolution model, and a real-time area temperature twinborn model is constructed; strain state sensing parameters in the concrete pouring process are identified, multi-scale spectral decomposition is carried out, then temperature strain signal stripping is carried out, and pure mechanical strain component characteristics are obtained; and performing internal micro-pore structure analysis based on pure mechanical strain component characteristics to generate a concrete situation prediction result. The intelligent level of the whole fan foundation construction stage is improved, and the fan construction efficiency and safety are improved.

The invention discloses a simulator detection method and system based on multi-dimensional feature fusion, and relates to the technical field of simulator detection, and the method comprises the steps: collecting the static feature information of a to-be-detected device; reading a memory mapping file of the equipment process in a preset time window, and obtaining a memory address allocation state at each moment to form a time sequence data set; further constructing an address space evolution sequence, and performing domain theory modeling to construct an address space domain; obtaining an address evolution function through a function construction algorithm; generating a regular mark through a natural transformation detection algorithm; performing topological structure analysis and coherence calculation on the address space category to generate address space complexity features; static feature information, regular marks and address space complexity features are integrated through a multi-layer fusion strategy, and simulator detection results are screened, judged and output layer by layer. According to the method, through a complementary collaborative system of static verification, dynamic rules and topology complexity, the anti-avoidance capability and robustness of detection are improved.

The invention belongs to the cross technical field of artificial intelligence and image recognition, and discloses a cursive script image sequence recognition method for modeling by fusing a stroke structure, and the method comprises the steps: preprocessing and normalizing a cursive script image, eliminating interference information, normalizing the form of the cursive script image, and guaranteeing the stability and effectiveness of a subsequent processing flow; based on stroke decomposition and feature coding of structural analysis, stroke-level structural features are extracted through character skeleton modeling, and cursive writing characters are converted into time series data; modeling a cursive script recognition neural network fused with structural semantics, and recognizing a stroke time sequence by adopting a BiLSTM neural network; an identification output and result optimization module is designed to ensure the final identification quality; through application of scene integration and platform deployment interface design, practical deployment of the model and butt joint of a front-end platform are realized, and a complete cursive script recognition ecology is constructed. According to the method, not only is the structured representation capability of the cursive script image improved, but also the recognition precision and generalization performance of the model on complex handwriting are remarkably enhanced.

The invention relates to the field of adaptive noise reduction, in particular to an adaptive noise reduction method and system for a multi-mode audio SoC main control chip. The method comprises the following steps: acquiring an original audio input signal according to an SoC main control chip, performing time-frequency domain dual deconstruction and cross-frequency domain noise interference structure analysis, and constructing a full-frequency domain noise interference topology table; carrying out multi-mode interference factor separation on the full-frequency-domain noise interference topology table, carrying out multi-noise environment modeling, and constructing a real-time noise scene model; time sequence noise slope fluctuation modeling is carried out on the real-time noise scene model, dynamic noise reduction response learning optimization is carried out, and a dynamic noise reduction optimization strategy is constructed; and performing neural coding gain and audio boundary line reconstruction according to the original audio input signal to obtain a coding gain key audio signal and a weak audio optimization signal. According to the invention, by flexibly adjusting the noise reduction intensity of the audio signal, the real-time scene noise reduction performance is optimized.

The invention discloses a software development project progress prediction management method based on artificial intelligence, and relates to the technical field of project management, and the method comprises the steps: extracting a causal relation from entity relation graph data, and constructing a causal knowledge graph; a graph structure analysis method and a time sequence prediction method are combined, predicted completion time and delay probability are calculated for the causal knowledge graph and the associated time sequence data, and a progress risk assessment result is generated; performing causal chain identification on the progress risk assessment result and the causal knowledge graph by adopting a causal reasoning method to generate delay interpretation information; according to the delay interpretation information, a preset resource priority and a task weight, a rule driving and priority scheduling strategy is adopted to construct a compensation scheduling scheme; and implementing a compensation scheduling scheme, collecting implementation effect data and analyzing a scheduling effect through a closed-loop feedback and dynamic adjustment mechanism, and generating an optimized scheduling management scheme. The intelligent level and the practical value of project progress management are greatly improved.

The invention relates to the technical field of industrial communication protocol analysis, in particular to a protocol semantic matching analysis method based on reinforcement learning, and the method comprises the steps: constructing an industrial interaction environment containing industrial control equipment, a communication module and a protocol simulation assembly, defining a state space containing a flow attribute and a communication state, field processing, semantic annotation and dependency configuration related action space are carried out; calculating a reward value through a multi-level reward function of dynamic weight adjustment; based on the preprocessed multi-source protocol data set, adopting a Transform and graph neural network dual model to pre-train and generate a template library, and embedding an intelligent agent state processor; the intelligent agent updates the strategy after state processing, action distribution output by the strategy network, verification and execution of the action executor and interaction with the environment; and storing the interaction experience for iteration, and finally outputting protocol structure analysis, field classification and abnormity prompt information. According to the method, low-manual-dependence automatic analysis, unknown protocol self-adaptive adaptation and high-precision semantic matching can be realized.

The invention discloses a design method of an equivalent simulation component of a wall-type connection metal shear damper, and relates to the field of design methods of anti-seismic components. Comprising the steps that a non-damping structure model without a damper is established, the plane arrangement position of the metal shear damper in a building is determined, the yield bearing capacity of the damper is determined according to the proportion of earthquake shear force shared by the damper in the X direction and the Y direction of the structure, and the yield displacement of the damper is determined according to the design performance target of the damper. And determining the post-yield stiffness ratio of the metal shear damper, establishing a damping structure analysis model with a damper unit and a connecting wall pier unit, calculating the secant stiffness of the metal shear damper, and calculating the stiffness of the metal shear damper connecting the wall pier. According to the design method, the equivalent simulation component of the damper in the construction drawing model can be designed according to the performance parameters of the metal shear damper and the connecting wall pier, and the design method has important significance on correct establishment and equivalent elasticity analysis of the construction drawing model of the damping structure of the metal damper.

The invention discloses a mine geologic structure analysis and safety assessment method based on image processing, relates to the technical field of mine geologic analysis and assessment, and provides the following scheme: obtaining analysis image information of a mine slope, including confidence coefficients of crack probability, non-crack probability, regional deformation probability and internal damage probability; dividing the image into fixed-size areas and carrying out smoothing processing, and obtaining candidate areas after removing low-confidence areas; taking a confidence coefficient difference value between the crack and the non-crack as an evaluation index, and screening a target area; determining low, medium and high risk levels according to comparison results of the deformation and damage probabilities and corresponding threshold values; and generating and executing a reinforcement scheme for the high-risk area, obtaining an image calculation evaluation index again, and iteratively adjusting the scheme until conditions are met. According to the method, comprehensive risk layering evaluation of surface and internal damage is realized, and the safety and long-term stability of the slope are improved through dynamic optimization processing.

In one aspect, the present disclosure relates to a material analysis method which may comprise the steps of: (a) preparing a specimen for structure analysis; (b) training an artificial intelligence model with a training image labelled with a structure aspect of an arbitrary specimen; (c) analyzing a plurality of analysis images not labelled with a structure aspect of a specimen using the trained artificial intelligence model, and removing analysis images classified into preset noise structure aspects; and (d) analyzing, by using the trained artificial intelligence model, the analysis images from which the analysis images classified into the noise structure aspects have been removed, and classifying the analysis images into at least one target structure aspect.

The invention relates to the technical field of ocean current data analysis, in particular to a multi-scale modeling and verification method and system based on ocean current spring layer structure analysis. And obtaining the thermocline and halocline parameters of the target sea area and carrying out abnormal value elimination and spatial interpolation to form a thermocline data set. A convolutional neural network is used to extract spring layer space structure features, and based on Gaussian curvature quantification boundary complexity, a thermocline and halocline equivalent geometric model is constructed. On the basis, taking the equivalent geometric model as a boundary condition, and performing multi-scale decomposition on the Navier-Stokes equation set to generate a dynamic approximate equation; and then inputting the approximate equation and the equivalent geometric model into a physical information neural network for constraint training to obtain a multi-scale flow field prediction model, and comparing the multi-scale flow field prediction model with actually measured flow field data to complete verification. According to the method, automation, refinement and multi-scale coupling of spring layer structure modeling are achieved, and scientificity and engineering applicability of ocean current modeling are improved.

The invention discloses a fast moving face recognition method and system based on multi-frame image enhancement, and the method comprises the following steps: collecting a multi-frame image sequence through an imaging device, and carrying out the preprocessing of the multi-frame image sequence, and obtaining a multi-frame image; inputting the preprocessed multiple frames of images into an improved MonST3R network, and performing spatial alignment and structural analysis to obtain aligned images and three-dimensional structural information; inputting the result into a CRNet network, and separating and enhancing the multi-scale features to obtain enhanced features; the enhanced features are input into a space-time attention fusion module, and multi-frame feature representation is obtained through weighted fusion; inputting the fusion feature into a recognition network, and performing dimension reduction to obtain a face embedded feature vector; and performing similarity comparison on the embedded features and a database template, and outputting an identification result and a confidence prompt. According to the invention, efficient, accurate and resource-friendly face recognition can be realized in a dynamic complex environment.

The invention discloses a formatted document intelligent generation method and system, and the method comprises the steps: obtaining a multi-source formatted document sample, carrying out the structural analysis through a deep learning technology, and constructing a hierarchical document structure knowledge base; performing template matching through neural manifold representation learning and comparison self-supervised learning on the basis of the knowledge base in combination with document requirements input by a user, and outputting an optimal document template; performing structure analysis on the template, generating document content through a diffusion model in combination with user content, and performing quality diagnosis and optimization by adopting optimal loss value estimation; carrying out format self-adaptive adjustment and style consistency check through a value-free strategy optimization algorithm of the reward partition; and performing multi-format conversion export and quality evaluation on the finished document. The technical problems that an existing system is insufficient in flexibility, format adjustment is lack of intelligence, and document semantic coherence and style consistency are difficult to maintain are solved.

The invention relates to the technical field of structural analysis, and provides a structural model generation method and device based on an architectural drawing, equipment and a medium, which can reconstruct a line unit of a basic architectural drawing according to a line type assignment rule so as to realize visual distinguishing of component information. The drawing exchange format file processing library is called to accurately extract drawing information of the target architectural drawing and write the drawing information into the dictionary, so that hierarchical management and control of information can be realized, and component information confusion is avoided; each structure model standard layer is created according to the building drawing information file and the modeling information file, nodes are added to each structure model standard layer based on a node duplicate removal mechanism, and efficient node creation can be realized on the premise of avoiding repetition; structural component modeling is carried out on the basic framework according to the modeling information file and the pre-processing parameter file, pre-processing parameter configuration and model data engineering output processing are carried out on an initial structural model to obtain a target three-dimensional structural model, and therefore accurate modeling of the three-dimensional structural model is automatically achieved.

The invention relates to the technical field of computer vision and edge computing, and discloses a multi-computing-power collaborative video structured analysis method, which comprises the following steps of: acquiring a plurality of paths of original video streams, and synchronously decoding the plurality of paths of original video streams; gradient analysis is adopted to evaluate the decoded multiple paths of original video streams, and a preprocessing algorithm combination is selected based on a quality feature vector; establishing a data transmission channel, and adopting an asynchronous communication protocol and a multi-level cache system; performing content complexity analysis, and performing model matching according to the content complexity analysis to obtain a scheduling decision; carrying out distributed deployment, and carrying out pipeline reasoning on the deployed model; pipeline reasoning results are fused, and consistency constraint verification is carried out on fusion results; optimizing system configuration parameters according to a video structured analysis result to realize video structured analysis; the real-time performance, the accuracy and the system stability of video analysis are improved.

An intelligent unmanned aerial vehicle pneumatic structure reduced-order analysis model comprises a pneumatic reduced-order model and a structure reduced-order model. The aerodynamic structure order reduction analysis process comprises the steps that an aerodynamic order reduction model and a structure order reduction model are initialized; under the given calculation working condition, the aerodynamic load acting on the intelligent unmanned aerial vehicle structure is obtained through calculation of the aerodynamic reduced-order model; transmitting the load on the pneumatic grid to the structural grid through a force interpolation algorithm; solving deformation response by the structure order reduction model under the action of aerodynamic load; the structural deformation is fed back to the pneumatic grid through displacement interpolation, and the pneumatic configuration is updated; and performing iterative calculation until a convergence criterion is met, finally obtaining a pneumatic structure analysis result in an equilibrium state, and outputting a pneumatic coefficient and structural deformation and stress. According to the method, the CFD / CSD model is replaced by the equivalent model, so that the calculation efficiency is remarkably improved on the premise of ensuring the calculation precision.

The invention relates to the technical field of task progress monitoring, in particular to an intelligent monitoring system and method for a data task processing progress state, and the method comprises the steps: obtaining task records, filing task numbers in batches, arranging execution hop segments and node name generation stage sections, and checking a node behavior marking idle state. And tracking a state field change frequency to extract a hopping stage, and classifying and identifying an abnormal stage to generate a progress deviation result. According to the method, task item division and group collection are completed by constructing a batch tracking structure of task numbers and attributions, execution hop segments and frequent segments are extracted to form a hop distribution set, operation traces are extracted in combination with node behaviors and stage segments, non-behavior nodes are marked, state fields are classified according to stages, and change frequencies and hop sequences are generated. And performing cross comparison on the state and behavior records to output a fluctuation trajectory identifier, and supporting task jump structure carding and state hierarchical labeling.

The invention discloses a natural language structured query generation method and system based on pattern reasoning, and relates to the technical field of natural language processing and database query. The method comprises the steps of mode knowledge graph construction in an offline stage and three-step reasoning generation in an online stage. In the offline stage, through structure analysis, semantic enrichment and data portrait processing, a database mode is converted into a knowledge graph Schema-KG rich in structure and semantic information. The online stage adopts three-step reasoning: firstly, decomposing a natural language of a user into an intention component through LLMParser, and linking the intention component to a graph entity; secondly, reasoning an optimal JOIN path on the Schema-KG based on a graph search algorithm, and generating a mode perception intermediate representation SA-IR; finally, the SA-IR is compiled into an SQL, and pre-verification is executed. According to the method, the accuracy of complex query is remarkably improved through a structured reasoning mechanism, errors during operation are effectively avoided through pre-execution verification, a clear reasoning process is provided through SA-IR intermediate representation, and the interpretability and debugging efficiency of the system are greatly improved.

The invention relates to the technical field of engineering monitoring, in particular to a bridge structure state monitoring method and system based on digital twinning, and the method comprises the steps: building a multi-source monitoring data matrix based on a data timestamp; establishing a geometric model, a material attribute model and a load model of a bridge structure based on the multi-source monitoring data matrix, and fusing to obtain a bridge digital twin initial model; and carrying out structural mechanical analysis based on the bridge digital twin initial model to obtain stress distribution data of each region of the bridge. Through real-time acquisition and analysis of multi-source monitoring data, the method can dynamically evaluate the structural state of the bridge and timely identify potential safety hazards, so that the safety of the bridge is improved; and through calibration of the initial model, the obtained digital twin precise model reflects the real state of the bridge, and a more accurate data basis is provided for subsequent structural analysis.

The invention relates to a BIM (Building Information Modeling)-based building structure modeling method, device, equipment and medium, and the method comprises the steps: aiming at a curvature-mechanics semantic fault problem existing in a process of converting a special-shaped curved surface structure from a BIM geometric model to finite element analysis, generating a curvature tensor field with parameter coordinates by analyzing an NURBS curved surface; integrating along the maximum curvature change direction to construct an internal force flow function, and forming a guide field for controlling the grid density; shell / beam units are divided based on the guide field gradient, space coordinates are marked, and a curvature-stiffness matrix and an internal force flow driving variable cross-section are combined for calculation to generate a structural analysis model; the gradient distribution of the guide field is dynamically corrected through displacement error feedback, and lossless transmission of geometric topology and mechanical properties is achieved. According to the method, the distortion risk of special-shaped structure analysis is remarkably reduced, artificial experience correction is avoided, and high-precision automatic modeling support is provided for a complex building structure.

The invention discloses a content abstract generation method based on chapter structure analysis, and belongs to the technical field of natural language processing. The method comprises the steps that firstly, an original text is preprocessed, then text structure deep analysis is carried out, the text type of the text is recognized, an explicit / implicit text relation is extracted, and special symbols are introduced through a Prompt normal form for implicit text relation extraction to strengthen logic semantics; secondly, scoring sentences by adopting a double-path scoring mechanism in combination with a deep neural network model of chapter structure features and an optimized text sorting algorithm, and fusing scores through a logistic regression model; then, screening target sentences based on a chapter relation weighted secondary modulus function and a greedy algorithm, and finally, carrying out post-processing to generate an abstract. According to the abstract generation method, the chapter structure logic is deeply utilized, so that the problems of logic unsmoothness, information redundancy or key relation missing in the existing abstract generation are solved, and the semantic coherence and information integrity of the abstract are improved.

The invention discloses a social group discovery system based on fused semantic information. The system comprises a presentation layer, a presentation layer, a service layer and a data layer. The presentation layer supports checking of a group division result; the display layer displays a user structure network, group dynamic division and group characteristic indexes through a visual tool; the service layer is composed of a semantic tag generation module, a user structure analysis module, a social group division module and a social group analysis module, semantic and structural features are fused through a graph attention network, and accurate group division is realized in combination with an adaptive embedded graph aggregation algorithm; and the data layer stores the original data set of the social platform and the processed data. The system inputs user texts into a large language model to generate semantic tags, node embedding is generated by calculating node association weights, group division is completed by combining an adaptive embedding graph aggregation algorithm, and finally group characteristics are quantitatively analyzed from the aspects of cohesion, conductivity and the like. The system is suitable for scenes such as vertical domain community mining.

The invention provides a rock mass internal pore intelligent characterization and three-dimensional reconstruction method and system, and relates to the technical field of rock mass internal structure analysis, and the method comprises the steps: obtaining a two-dimensional image sequence of a rock core in a target rock mass through a focused ion beam scanning electron microscope slicing-imaging sequence working mode; performing pore segmentation on each two-dimensional image in the two-dimensional image sequence by using the pore segmentation network to obtain a plurality of pore two-dimensional segmentation maps; on the basis of the pore two-dimensional segmentation map, constructing three-dimensional binary volume data of the pores according to a slice sequence; according to the three-dimensional binary volume data, performing three-dimensional reconstruction and structural characterization on rock core pores; according to the method, an imaging-recognition-reconstruction-evaluation integrated workflow is constructed, and a full-chain closed loop from nanoscale real three-dimensional data acquisition to intelligent recognition, three-dimensional reconstruction and quantitative analysis is realized.

The invention relates to the technical field of flexible production and industrial intelligent control, in particular to a liquid crystal display screen production line operation management method and system. Comprising the following steps: performing timestamp alignment and de-noising processing on multi-source heterogeneous data; inputting the physical state feature vector into a two-channel hybrid reasoning unit to generate a comprehensive prediction reference; calculating a difference value between the physical state feature vector and the comprehensive prediction reference, and generating an original residual vector; carrying out topological structure analysis on the original residual vector to generate a residual semantic signal; performing drift judgment on the dynamic tolerance boundary to generate a corresponding dynamic operation mode instruction; in response to a dynamic operation mode instruction indicating benign line change fluctuation, constructing an evolved cognitive model; adaptive closed-loop control from data space to physical space is constructed in response to a dynamic operational mode instruction indicating a malignant anomaly. According to the invention, a reliable basis is provided for subsequent differential regulation and control, and continuity and stability of operation of a production line are ensured.

The invention discloses an intelligent image batch generation method and device, equipment and a storage medium, and the method comprises the steps: receiving an Excel data file uploaded by a user, carrying out the table structure analysis and data content extraction of the Excel data file, and obtaining target data; obtaining a design template selected by a user, and establishing association between the target data and elements in the design template by adopting a binding algorithm to obtain a binding relation mapping table; an AI image processing function is called to process the bound target data to obtain processed data, and the AI image processing function comprises at least one of AI sectional drawing, AI image sharpening, AI document generation and AI text drawing; based on the binding relation mapping table, applying the processing data to the bound design template to obtain an initial image; and performing image rendering on the initial image through a rendering engine to generate a target image. According to the method, various AI tools are integrated, so that the processing efficiency is remarkably improved.