3976 results about "Iterative refinement" patented technology

The invention discloses a dynamic visual content collaborative generation method based on a cognitive logic chain, and belongs to the technical field of visual content generation, and the method comprises the following steps: S1, user intention analysis and data input; s2, dynamically constructing a cognitive logic chain; s3, intelligent scheduling of the multi-modal generation module; s4, cross-modal content collaborative generation is carried out; s5, collaborative editing and real-time feedback are carried out; s6, iterative optimization of logic chain driving; s7, multi-dimensional quality evaluation: constructing an evaluation matrix containing semantic consistency, visual attraction and user participation degree, predicting a content propagation effect in combination with a deep learning model, and generating a quantitative improvement suggestion report; and S8, updating the self-adaptive knowledge reversely marking the cognitive logic chain according to the finally adopted content version, extracting a new association rule, and injecting the new association rule into the rule base. Through deep semantic analysis and dynamic logic chain construction, the system accurately captures a core creation target of a user and converts the core creation target into an executable visual strategy.

The invention relates to a building design scheme multi-objective optimization comparison and selection method and device, equipment and a medium. The method comprises the steps of generating a multi-dimensional design parameter set by obtaining building information model data and parameterized design data; performing multi-dimensional target analysis and evaluation by using a multi-field joint simulation platform to generate a multi-dimensional evaluation index; a dynamic multi-objective optimization model is constructed through a dynamic weight adaptive algorithm in combination with project stage demands and user interaction data; carrying out iterative optimization by adopting an improved non-dominated sorting genetic algorithm to obtain an optimized design scheme gene sequence result, and introducing a spatial topology connectivity constraint to generate a Pareto optimal solution set; and according to the Pareto optimal solution set, generating an optimization scheme through user weight adjustment and scheme screening. According to the method, the optimal design scheme set meeting the project requirements can be quickly and efficiently generated and screened out, the project stage requirements and user preferences are met, and the design efficiency and the scheme quality are improved.

The invention relates to the technical field of artificial intelligence assisted medical rehabilitation, in particular to a peripheral nerve injury personalized rehabilitation system and method based on a multi-modal large model, and the method comprises the steps: a feature fusion module employs space-time attention to fuse multi-modal time series data, and constructs an evaluation map; the personalized generation module is combined with historical data and reinforcement learning to generate a scheme containing virtual scene parameters; the interaction feedback module collects data through mixed reality and calculates action deviation; and the adaptive adjustment module adopts a meta-learning optimization model and a distributed iterative output scheme. According to the method, the cross-modal association precision of the motion features and the mechanical parameters is improved, dynamic matching of the training scene and the motion ability of the user is achieved, the virtual environment and the mechanical feedback threshold are optimized by dynamically adjusting the rehabilitation scheme parameters, the scheme optimization period is shortened based on an online iterative optimization mechanism, and the training efficiency is improved. The core defects of personalized adaptation lagging and low utilization efficiency of multi-modal data are overcome.

The invention relates to the technical field of data processing, can be applied to business scenes such as financial science and technology and medical health, and discloses a self-adaptive data processing optimization method, device and equipment and a medium, and the method comprises the steps: collecting operation data, host performance data, network state data and historical task data of a target data source, and constructing an analysis model in combination with recovery parameters and strategy preference, predicting a task load state, resource consumption and execution duration, generating a task execution strategy, completing task scheduling and execution monitoring, and collecting execution feedback data for iterative optimization of the analysis model. According to the method, an analysis model is constructed by fusing multi-source system data and historical task information, a task execution strategy is generated in combination with a dynamic prediction result and a strategy weight, intelligent task scheduling and process monitoring are realized, and feedback data is used for model iterative optimization. The task execution efficiency is improved, the resource use rationalization is realized, and the model adaptive capability is enhanced.

The invention relates to an AI-based automatic adaptation method for a work approval process, and the method comprises the steps: collecting multi-source approval data, and carrying out the preprocessing of the multi-source approval data, and forming standardized approval data; analyzing the system rule text by using the pre-trained AI large model, and extracting a structured approval rule comprising a trigger condition, an approval role and a process node sequence; matching a basic examination and approval template according to the form field and the user permission information, and dynamically generating an adaptive process comprising multiple stages of examination and approval nodes, aging parameters and an additional examination and approval link; and through resource scheduling optimization and time domain correlation analysis, establishing an optimized mapping relation based on flow execution characteristics such as approval timeliness deviation and node skipping frequency, and outputting a visual flow chart and execution parameters. A business logic writing mode is replaced by automatic analysis of an AI large model on an unstructured rule; the dynamic template matching and continuous iterative optimization mechanism can quickly respond to business changes, and resource scheduling optimization and automatic process generation shorten the implementation period and reduce the operation and maintenance cost.

The invention discloses a cut workpiece defect detection method and system based on image feature feedback, and relates to the technical field of image processing.The method comprises the steps that cut workpiece technological characteristics are obtained, a preset defect type library is constructed, a hardware system is built, and parameters are initialized; synchronously acquiring a multi-view original image, and storing and associating annotation information; de-noising the original image, enhancing the contrast, and extracting a region of interest ROI; extracting texture, shape, edge and gray features from the ROI, and screening through a Relief-F algorithm to obtain an optimal feature subset; inputting into an SVM (Support Vector Machine) model for reasoning, and screening to obtain an effective defect detection result; and calculating an evaluation index and generating a feedback signal, and performing iterative optimization after adjusting parameters. The system comprises an acquisition module, a master control module, a data processing module and a display module. Through the precise design and closed-loop feedback of the whole process, the precision, efficiency and long-term adaptability of defect detection of the complex cutting workpiece are improved, and the industrial quality management and control requirements are met.

The invention discloses a power grid real-time optimization scheduling system and method based on digital twinning, and relates to the technical field of power grid scheduling. A sensor is deployed to collect environmental parameters of key nodes in real time, a dynamic environmental condition coefficient is constructed, a power grid state is analyzed in combination with frequency stability and a relative strength index, and a multi-model fusion prediction mechanism is established, so that space-time two-dimensional accurate prediction of load and power generation is realized. A multi-objective optimization model is adopted to take'maximization of new energy consumption + minimization of scheduling cost 'as a core objective, a genetic algorithm is introduced to solve an optimal scheduling scheme, and a feasible solution is screened in combination with forward simulation of a digital twin model. Through abnormal early warning triggering, environment correlation analysis and model iterative optimization, a scheduling strategy is dynamically adjusted, and the power supply efficiency and the emergency response capability in an extreme scene are improved. According to the method, multi-source heterogeneous data are effectively fused, and real-time sensing of a power grid operation state, collaborative optimization of multiple energy resources and adaptive iteration of a scheduling model are realized.

The invention discloses a failure chain quantitative analysis and risk assessment method and system based on a multi-level security model, and aims to solve the defects that accident cause analysis of a complex social technology system is inaccurate, and a risk assessment result is lack of effective verification. According to the method, a multi-level causal model is systematically constructed, a multi-dimensional failure chain (MDFC) is extracted, multi-dimensional risk quantification is performed on the MDFC, a directed weighted failure propagation network is constructed based on the multi-dimensional risk quantification, and structural features of the directed weighted failure propagation network are analyzed to identify key risk factors. The core innovation of the method is that reverse accident reason tracing and forward risk propagation path analysis based on the weighted network are fused, mutual verification and iterative optimization are realized by comparing analysis results of the two paths, so that the understanding of an accident evolution mechanism is deepened, and the reliability of evaluation is improved. The system vulnerability can be revealed more comprehensively, powerful support is provided for formulating accurate risk control measures, and the overall safety level of a complex system is improved.

The invention discloses a data center operation and maintenance fault prediction system and method based on deep learning. The system comprises a multi-source heterogeneous data acquisition module, a data preprocessing module, a deep learning prediction model module and the like. The method comprises the following steps: acquiring multi-dimensional operation data of a data center through full-quantity acquisition of multi-source data, and inputting a CNN-LSTM-Attention hybrid model to realize fault prediction after preprocessing and feature enhancement; fault grades are divided in combination with fault grading, early warning is pushed in multiple channels, a coping strategy is intelligently generated, the effect is verified in a closed loop mode, and finally the model is iteratively optimized. According to the scheme, the fault prediction precision and real-time performance are improved, the operation and maintenance response time is shortened, the service interruption risk caused by faults is reduced, and the method is suitable for efficient operation and maintenance of large-scale data centers.

The invention relates to the field of building intellectualization, in particular to a digital twinborn mixed cloud edge collaborative intelligent house building group operation and maintenance intelligent system, which establishes a digital twinborn scene database by collecting building basic information, equipment operation data and environmental parameters, synchronizes the digital twinborn scene database to edge equipment, and uses BIM, GIS, Internet of Things, 5G and AI technologies to establish a digital twinborn scene database, so as to realize the intelligent operation and maintenance of a building group. A virtual-real combined digital intelligent building scene is constructed, real-time synchronization of a virtual scene and a physical environment is realized through AR / VR equipment, and an operation and maintenance module comprises multi-source heterogeneous data fusion, edge intelligent analysis decision, adaptive model training and iterative optimization, a predictive maintenance algorithm of virtual-real mapping and a multi-level collaborative decision and autonomous scheduling mechanism. And the monitoring module monitors the state and operation condition of the edge equipment, provides data service and supports visualization of management decisions, and the system effectively improves the intelligence and digitization level of operation and maintenance of the building group.

The invention relates to the technical field of computers, and discloses a computing power resource multi-dimensional scheduling method and system based on dynamic weight, and the method comprises a data perception step, a weight generation step, an intelligent decision-making step and a scheduling optimization step. The system corresponds to the method. The method comprises the following steps: a data sensing step: collecting multi-dimensional state parameters of computing power nodes and carrying out feature modeling to construct a global feature space; a weight generation step: dynamically adjusting the weight of each dimension based on a machine learning model and a rule engine; an intelligent decision-making step of screening candidate nodes in the global feature space and evaluating priorities, generating an optimal node cluster and performing resource dynamic slice distribution; and a scheduling optimization step: monitoring an execution effect and performing closed-loop feedback so as to iteratively optimize a weight strategy and decision logic. The problems that in the prior art, the sensing dimension is single, and decision-making weight is rigid are solved, and multi-dimensional accurate sensing, dynamic weight decision making and elastic resource allocation of computing power resources are achieved.

The invention discloses a multi-table combined natural language query SQL (structured query language) generation method, which comprises the following steps of: 1, constructing a database meta-knowledge graph, and establishing a triple storage comprising a table structure, a primary and foreign key relationship and business description for each data table; 2, receiving a natural language query request, and calculating the topic relevancy between query semantics and each database table through a pre-trained topic matching model; 3, dynamically constructing a view, and logically associating the database tables of which the theme relevancy exceeds a threshold value to form a temporary view; 4, generating a context enhancement prompt, and combining the temporary view structure, the field semantic description of the view and the view content sample to form a structured prompt; 5, inputting the natural language query and the structured prompt into the large language model to generate candidate SQL statements; and step 6, executing verification and iterative optimization on the candidate SQL statements, verifying logic correctness through SQL execution plan analysis and result sampling, and triggering and prompting a reconstruction mechanism when detection is abnormal.

The invention discloses an electric power capital construction site operation environment data monitoring and safety management method, which belongs to the field of intelligent decision technology and electric power safety management, and comprises the following steps: constructing a semantic network framework according to a construction plan; collecting and calibrating multi-source environment data to generate a trusted data set; generating a real-time risk network graph based on the semantic framework and the trusted data set; calculating a robust risk index and performing sensitivity deconstruction; generating a closed-loop intervention instruction when the risk indicator exceeds a safety threshold; and finally, collecting, feeding back, iteratively optimizing the whole system, and generating a cross-project multiplexing intelligent template library. According to the method, a comprehensive technical path of semantic modeling, causal inference and closed-loop adaptive optimization is adopted, the operation situation can be deeply analyzed, potential risks can be quantified and attributed prospectively, the optimal intervention strategy is intelligently generated, and the intelligence, precision and prospective level of safety management of the electric power capital construction site is remarkably improved.

The invention relates to the technical field of intelligent questioning and answering, in particular to a domain intelligent questioning and answering method and system based on a multi-modal knowledge graph and RAG, and the method comprises the steps: constructing a concept layer knowledge graph based on a directory structure of a domain multi-modal document, and constructing an instance layer knowledge graph based on document content; obtaining a user question, pruning and positioning the user question in combination with the concept layer knowledge graph and the thinking chain, and determining a target chapter; splitting the question into sub-questions through intention analysis, and performing semantic retrieval in the instance layer knowledge graph corresponding to the target chapter to obtain a graph retrieval result; optimizing the original problem based on the atlas retrieval result, and executing semantic retrieval in a vector database to obtain a vector retrieval result; and fusing the atlas retrieval result and the vector retrieval result to generate a preliminary answer, and performing iterative optimization until a final answer is generated. According to the method, the semantic coverage, the expression accuracy and the response efficiency of the vertical domain question-answering system are remarkably improved by constructing the multi-modal knowledge graph and optimizing the retrieval process.

The invention relates to the technical field of natural language processing, in particular to an enterprise number asking system and method based on combination of a large language model and an NL2SQL, and the method comprises the following steps: filling a prompt project template with a natural language query request and a structured metadata context, and generating a standard prompt; the standard prompt is input into the large language model, deep semantic analysis is carried out, and an SQL query draft is generated; self-evaluation is carried out on the questions, and when the questions are recognized, clarified questions are generated and returned to the user; receiving feedback of a user, and iteratively optimizing the SQL query draft based on the feedback; performing grammar verification and security verification on the final SQL query draft to generate an executable SQL statement; sending the executable SQL statement to a target enterprise database for execution; and the information is visually displayed to a user. The problem that the SQL generated in the prior art deviates from the real intention of a user and cannot meet the requirements of enterprise-level applications for accuracy and reliability can be solved.

The invention discloses a dynamic vehicle scheduling intelligent decision-making system based on big data, and relates to the technical field of intelligent traffic and logistics scheduling, and the system comprises a data collection module, an event analysis module, a knowledge graph construction module, a hypergraph modeling module, a constraint processing engine, an optimization decision-making module, a strategy verification module and an output interaction module. The system has the advantages that multi-source data such as government announcement texts and social media information are acquired in real time through the data acquisition module, event key information is extracted through the event analysis module by utilizing a natural language processing technology, and an event knowledge graph of an incidence relation is constructed through the knowledge graph construction module; the strategy verification module simulates and verifies the strategy effect through the digital twinning technology and iteratively optimizes the strategy effect, the whole process does not need manual intervention to adjust rules, the limitation that a traditional system depends on manual processing is broken through, and the problems that dynamic strategy adjustment is time-consuming, labor-consuming and error-prone in an extreme scene are solved.

The invention relates to the technical field of smart power grids and artificial intelligence, in particular to a substation autonomous intelligent inspection method and system based on multi-modal data, and the method comprises the steps: obtaining inspection data of multiple modals, and generating fusion features; identifying system alarm information; performing intention recognition and task classification to generate an executable task sequence; generating a multi-device cooperative scheduling scheme; executing the multi-device cooperative scheduling scheme; iterative optimization is carried out; according to the intelligent inspection method provided by the invention, more accurate and more robust multi-mode perception and diagnosis are realized, and deep understanding of complex instructions and safe and efficient cooperation of multiple devices are also realized; and meanwhile, through dynamic re-planning and a verification type feedback learning mechanism, high real-time performance and robustness are ensured, and meanwhile, the system is endowed with the capability of iterative optimization.

The invention relates to the technical field of computer vision and industrial automation, in particular to a shoe sole three-dimensional gluing contour extraction system and method based on vision single-line laser, and aims to solve the problems that virtual calibration target spots cannot be accurately generated based on shoe sole geometry, the positions and sizes of the target spots are difficult to determine by combining curvature extreme values and principal component analysis in the prior art, and the production cost is low. The problem that a double-branch deep learning model cannot be adopted to fuse feature prediction transformation, and the re-projection error is increased is solved; a virtual calibration target spot is automatically generated based on sole geometry through a feature fusion calibration module, a grid is generated through point cloud processing and Poisson reconstruction, the position and size of the target spot are determined by combining a curvature extreme value and principal component analysis, a corresponding relation is established by utilizing two-dimensional and three-dimensional feature matching, initial alignment is realized through ICP and re-projection error optimization, and the target spot position and size are determined. A double-branch deep learning model is adopted to be fused with feature prediction transformation, iterative optimization is carried out through space consistency errors, and re-projection errors are reduced.

The invention discloses an intelligent management system and method applied to a radio frequency energy output device, and belongs to the technical field of intelligent management of the radio frequency energy output device.An integrated sensor array is deployed on a radio frequency host and a hand tool electrode, and load voltage, output current, electrode temperature and tissue impedance are synchronously collected; a dynamic thermal impedance collaborative analysis model is constructed after time synchronization alignment, a three-dimensional thermal field simulation map is generated, and a thermal accumulation trend is predicted; a double-layer control framework is constructed, a first control layer generates a dynamic power adjustment rule base based on a preset treatment target and a safety threshold value and outputs an instruction, a second control layer receives the instruction and adjusts output parameters in real time, and a strategy is adjusted by combining a model prediction result in the execution process; meanwhile, the operation state is continuously monitored, a fault classification model is constructed for anomaly detection, a fault source is positioned, and a visual report is generated; and finally, constructing a mapping relation based on historical data, and generating an energy control scheme adapted to individualized requirements through iterative optimization.

The embodiment of the invention provides an optimization method and system for a manipulator control system, and the method comprises the steps: firstly obtaining an operation data set containing a control instruction sequence and corresponding motion state data in a historical operation period of a manipulator, carrying out the feature extraction of the operation data set, and obtaining an associated motion feature set; calling a pre-trained control strategy optimization model to analyze and generate strategy adjustment parameters, adaptively updating the control instruction sequence of the current operation task according to the strategy adjustment parameters to obtain a target control instruction sequence, and finally inputting the target control instruction sequence into a control system to execute operation. And real-time motion state data is collected for subsequent iterative optimization, so that the performance of the manipulator control system can be continuously improved, and the operation precision and efficiency are improved.

The invention provides a bearing fault identification method based on a dynamic generative adversarial network and expert feedback, and relates to the field of bearing fault diagnosis, and the method comprises the steps: generating a high-fidelity fault vibration signal through employing a condition generator and a triple discriminator generative adversarial network; verifying and generating sample quality through a 1D residual verification network and adding the sample quality into a training set; segmenting the vibration signals passing the test by using layered adaptive sampling, and keeping high-frequency impact characteristics in the vibration signals; a dynamic sparse attention mechanism is adopted to reduce unnecessary attention calculation and improve calculation efficiency, and different types of faults are accurately recognized in combination with a hybrid expert system classifier; and detecting the confidence of the diagnosis result, and triggering a feedback mechanism to regenerate a sample to complete autonomous iterative optimization when the confidence is low. According to the method, a generative adversarial network, a fault diagnosis model and a feedback mechanism are fused, accurate diagnosis of bearing faults is achieved through multi-level data enhancement and screening feedback, the diagnosis precision is continuously improved in continuous iteration, and the method is suitable for solving the problem that a traditional method is poor in performance under data scarcity and noise interference. The innovative closed-loop evolutionary logic of generation-diagnosis-feedback is provided, and the robustness and accuracy of fault recognition are remarkably improved.

Systems and methods for constructing layered prompts to operate as input into a pre-trained large language model (LLM). The method involves obtaining a set of application domains in which the LLM will be used. Using these application domains, a set of guidelines is determined, defining operation boundaries for the LLM. A set of layers is determined, each associated with the guidelines and including variables representing attributes identified within those guidelines. Using these layers, a first layered prompt is constructed to test the initial operation boundaries of the guidelines and is supplied to the LLM to generate a set of responses. Based on the responses, a second layered prompt is dynamically constructed to test additional operation boundaries, ensuring iterative refinement and contextual relevance.

The embodiment of the invention provides a computer communication method and system based on the Internet of Things, and the method comprises the steps: constructing a multi-level system architecture, carrying out the preprocessing and feature extraction of an original data flow, recognizing the data characteristics through a time sequence analysis method, and constructing a dynamic data model; deploying a monitoring agent at a transmission node to collect network performance indexes in real time, and constructing a network quality evaluation model; for an incomplete sensor data flow, prior probability distribution is constructed based on a dynamic data model and a network quality grade, and an optimal estimation value of missing data is calculated by adopting a Bayesian reasoning framework and an iterative algorithm; establishing a mapping relation between a network state and an optimal parameter through reinforcement learning to realize self-adaptive adjustment and optimization; and grading the data according to reliability, extracting high-reliability data points as anchor points, designing an iterative refinement algorithm to realize information propagation, and fusing to obtain a complete sensor data stream. According to the method, the problems of poor data recovery accuracy, static parameter configuration and insufficient adaptability in a complex network environment are solved.

The invention relates to the technical field of geological exploration, and discloses a water-rich sandy stratum tunnel base settlement prediction method and system, and the method comprises the steps: constructing a multi-source cooperative exploration system; deploying a sanding-seepage-stress coupling model at an edge computing node, and mining the relevance of data of different dimensions through a space-time attention fusion network; based on the prediction matrix and mineral exploitation working condition parameters; when the predicted settlement exceeds a safety threshold value or the sanding expansion rate is abnormal, a high-precision remeasurement mechanism is triggered, and a cross-hole radar and acoustic logging combined verification module is activated; and iteratively optimizing coupling model parameters by adopting a transfer learning algorithm through deviation analysis of measured data and a prediction result. According to the method, a scientific basis is provided for stratum stability evaluation and prevention and control measure formulation in the mineral exploitation process, and the probability of engineering risks caused by base settlement of the water-rich sandy stratum is effectively reduced.

The invention provides a spacecraft full-cycle monitoring and operation and maintenance decision-making system based on digital twinning, and belongs to the field of spacecraft full-life-cycle intelligent operation and maintenance. Structural data and environmental data are monitored in real time through a multi-source sensor network deployed on a spacecraft structure; fusing physical entity monitoring data and damage evolution rule virtual data in a twin database, constructing a structure damage resistance and mechanical property evaluation model, and executing geometric modeling, physical attribute analysis, behavior response simulation and damage evolution rule modeling; based on a simulation result of the virtual model, life prediction and reusability evaluation are carried out, and an operation and maintenance decision instruction is generated; and feeding back the operation and maintenance decision instruction to the physical entity to execute control, and updating state data in the twin database to perform iterative optimization. According to the method, full-scene coverage from design demonstration to scrap recovery is supported, the problem of stage splitting in the traditional technology is solved, and a closed-loop optimization mechanism is formed through real-time interaction between the physical entity and the virtual model.

The invention discloses a consultation method and system based on natural language processing and a legal knowledge graph, and relates to the field of data processing, and the method comprises the steps: receiving a multi-format legal consultation demand of a user, converting the multi-format legal consultation demand into a text, inputting the text into a BERT law NLP model, and analyzing key information through word segmentation, intention recognition and entity extraction; based on a pre-constructed multi-level legal knowledge graph, carrying out accurate and fuzzy retrieval and domain filtering in combination with an analysis result, and obtaining an associated law article, a case and a legal relationship; screening conflict law articles and similar cases, and inputting the conflict law articles and the similar cases into a graph neural network reasoning model to generate a preliminary conclusion; the conclusion is converted into a spoken consultation report through a natural language generation module, and output is customized according to a user scene; and if the user feedback satisfaction degree is less than the threshold value, iteratively optimizing the storage data to the historical library. The method has the advantages that accurate retrieval is realized based on the BERT model and the multi-level knowledge graph in the legal field, the oral personalized conclusion combined with the user scene is generated through GNN reasoning, and iterative optimization is performed through user feedback.

The invention provides a multi-target production scheduling method and system based on reinforcement learning, and the method comprises the steps: carrying out the matching analysis based on a task demand list and an equipment capability baseline, and obtaining a scheduling constraint space containing a task execution dependency relationship and an equipment operation state boundary condition; performing structured feature extraction on the scheduling constraint space through a graph neural network, generating a production scheduling association graph containing node features and edge features, and calling a multi-target reinforcement learning strategy model to perform strategy iteration optimization on the production scheduling association graph, obtaining a scheduling strategy parameter set fusing the task completion timeliness and the resource utilization balance degree; and generating an equipment task time sequence allocation scheme. According to the invention, the overall effectiveness and reliability of production scheduling can be effectively improved.

The invention provides an equipment fatigue crack intelligent detection method and system based on deep learning, and the method comprises the steps: firstly obtaining an equipment surface detection image set, generating a first crack detection thermodynamic diagram through a pre-trained deep learning model, and constructing a crack physical expansion constraint model according to equipment design material parameters and real-time operation condition parameters, then performing crack area iterative optimization based on the first crack detection thermodynamic diagram and the crack physical expansion constraint model to obtain a second crack detection thermodynamic diagram, and analyzing the second crack detection thermodynamic diagram to determine equipment surface crack state parameters such as crack area boundary coordinates, crack expansion direction vectors and crack depth gradient values; and finally, generating an equipment fatigue crack intelligent detection report containing a crack space distribution schematic diagram and a crack propagation risk grade identifier, thereby integrating image data and physical constraints, and improving the accuracy and reliability of crack detection.

The invention relates to the technical field of artificial intelligence, and particularly provides a low-sample NL2SQL intelligent generation method and device, and the method comprises the following steps: S1, enabling a dynamic sample extension module to solve a training data sparse problem in a small sample scene through an intention clarification and data enhancement technology; s2, the clause dependency chain type generation framework converts natural language query into structured query language (SQL) statements with clear structures through semantic analysis, clause generation and dependency modeling; s3, the multi-agent cooperation platform performs iterative optimization through intention recognition, SQL generation and code execution; s4, enabling an automatic evaluation and iteration mechanism to pass a standardized test and continuous optimization; and S5, carrying out field adaptation and security enhancement. Compared with the prior art, the complex query processing capacity can be improved, and the stability and safety of the system are guaranteed through automatic evaluation and a safety mechanism.

The invention relates to the technical field of two-dimensional motion control, and discloses a high-precision two-dimensional motion error prediction compensation iteration method. The method comprises the following steps: on an operation interface of a motion control system, generating a motion track overview containing a target position sequence and corresponding expected motion parameters according to parameters set by a user, and connecting the motion track overview with an actual motion execution device; measuring the deviation between the actual position and the target position of the motion platform at the current sampling moment, and calculating a motion error vector; constructing a prediction model based on the motion error vector and the expected motion parameter, and predicting a two-dimensional motion error at a future moment through iterative optimization; generating a compensation control instruction according to the prediction result and applying the compensation control instruction to the motion execution device; and monitoring the compensated motion state in real time, updating the prediction model and outputting a compensation log. According to the method, by predicting the error in advance and iteratively optimizing compensation, the two-dimensional motion control precision can be effectively improved, the real-time performance and adaptability of compensation are enhanced, and the method is suitable for a high-precision motion control scene.