2553 results about "False detection" patented technology

The invention discloses a glass lens surface scratch detection method and system, relates to the technical field of precision optical detection, and aims to solve the problems of scratch false detection, leak detection and poor algorithm adaptability caused by interference fringes, noise coupling and poor form adaptability in a high-reflection / complex coating process scene in the prior art. According to the scheme, an orthogonal polarization state composite light field is generated based on a multi-angle polarization light source array and a near-infrared compensation light source, and candidate regions are extracted through dynamic threshold segmentation and a direction gradient tensor matrix; gaussian pyramid multi-scale feature fusion and refraction angle consistency verification are utilized to eliminate artifact interference; constructing a direction constraint convolution kernel group to decompose scratches and background textures, and dynamically allocating computing resources in combination with a cascade network; feeding back closed-loop calibration light source wavelength and convolution kernel parameters in real time through coating parameters; according to the method, the precision and robustness of high-reflectivity surface scratch detection are remarkably improved, and meanwhile, the requirements for high-resolution image processing and real-time performance in a high-speed production line are balanced.

The invention discloses a deep learning-based small target defect recognition model training method, relates to the technical field of defect recognition model training, and aims at meeting small defect detection requirements, starting with high-resolution diversified data construction and accurate labeling, highlighting weak targets through multi-scale feature fusion and spatial attention, and realizing high-resolution target defect recognition. A hard case scene is processed in cooperation with layer-by-layer screening and secondary intensified training, real-time iterative optimization is achieved through multi-model fusion and online dynamic adjustment and optimization, finally, multi-mode and time sequence dimensions are expanded to capture deeper and dynamic defect information, the missing detection and false detection rate is greatly reduced, and the detection efficiency is improved. The detection efficiency and adaptability of micron-sized defects under a complex process background are improved; furthermore, by means of multi-source data such as infrared, X-ray or 3D morphology and a time sequence modeling means, multiple dimensions are fused, and hidden or early cracks are brought into a detection and prediction range, so that a high-reliability and evolvable intelligent recognition system for the tiny target defects is constructed.

A system and a method for automatically discovering and managing actions in an application is disclosed. The system includes a data ingestion layer for receiving application data from multiple sources, a scanning and systematic traversal engine for interacting with UI elements and capturing network calls, an action mapping and generation module for correlating UI actions with API calls and categorizing actions, an AI-driven icon and description generator for creating visual representations and textual descriptions of actions, a user interface for displaying and modifying discovered actions, and a continuous monitoring component for triggering re-scanning based on coverage metrics, error detection, or version updates. The system employs synthetic data generation and AI-driven exploration to uncover hidden or undocumented APIs, enabling comprehensive mapping of an application's capabilities at the API level.

The invention relates to the technical field of industrial nondestructive testing and computer vision, and particularly discloses an intelligent detection method for surface defects of a container bottom plate based on deep learning. The method comprises the following steps: synchronously acquiring data through a laser radar and a line scanning camera, complementing a shielding area of a point cloud, and executing coordinate normalization to generate preprocessed data; constructing a lightweight feature alignment network to realize cross-modal feature mapping and pixel-level error correction; segmenting a defect area by adopting an improved PointNet + + network and reconstructing a three-dimensional model; extracting defect geometric parameters and combining with material attributes to perform stress simulation and life prediction; aggregating multi-port safety life data to construct a federated framework to update model parameters; and synthesizing physically real defect samples based on false detection cases, and injecting the defect samples into the network for training. According to the method, the bottleneck of missing detection of internal defects in traditional two-dimensional detection is overcome, the defect detection rate and quantification precision are remarkably improved, full-life-cycle safety evaluation of the container is supported, and the efficient requirement of automatic port inspection is met.

The invention relates to the technical field of power system automation, in particular to a substation management system and method based on the Internet of Things technology, and the system comprises a ubiquitous Internet of Things sensing matrix, an electric power intelligence brain evolution center, a space-time fusion twin module, a nonlinear optimization decision module, and a multi-level vibration risk management and control model. Wherein the ubiquitous internet-of-things sensing matrix acquires substation equipment, environment and power grid data in real time; the electric power intelligence brain evolution center carries out health assessment, life prediction and abnormity positioning on the equipment; the space-time fusion twinborn module constructs a digital twinborn body, fuses equipment space-time data and historical data, and performs operation and maintenance simulation and fault reproduction; the nonlinear optimization decision-making module is used for generating a response regulation and control strategy aiming at the nonlinear and uncertain problems in the operation of the power grid; and the multi-level vibration risk management and control model carries out dynamic assessment and strategy optimization on equipment faults, power grid safety and environmental risks. Therefore, the problems of low inspection efficiency, high false detection risk, poor transmission stability and the like in the prior art are solved.

The invention belongs to the technical field of target detection and recognition, and particularly relates to a thinking chain-based large and small model collaborative target detection and recognition method. According to the method, the small model is responsible for most of easy-to-detect targets, the calculation pressure of the large model is reduced, the large model is responsible for suspected samples, vision and language multi-mode reasoning is combined, the overall false detection rate and the omission ratio are both reduced, confidence evaluation is conducted through the joint probability, automatic screening and manual rechecking of uncertain results are achieved, the reliability of key results is guaranteed, and the method is suitable for large-scale popularization and application. According to the'pseudo thinking chain + pseudo label 'method, by means of reasoning and labels generated by the model, data dependence on manual labeling is reduced, only low-confidence samples are manually confirmed, the human intervention range is narrowed, the human cost is remarkably saved, and semantic information with finer granularity is provided for the model by introducing phrase-level feature descriptors. And the identification capability of complex target attributes and states is improved.

The invention relates to a self-localization and motion perception method based on deep learning, and the method comprises the following steps: multi-modal perception: collecting RGB frames and event streams through employing a DAVIS346 event camera, obtaining texture and depth information through employing an RGB-D camera, and supplementing 3D structure data through a laser radar; hybrid optical flow driven motion perception: adopting a double-branch architecture of an improved RAFT network and an event optical flow private network; multi-modal 3D detection and trajectory management: fusing multi-modal data based on VoxelNeXt-Lite to generate a 3D detection frame, filtering false detection by combining point cloud density clustering and an event density threshold, then constructing a space-time diagram associated trajectory through a Spatio-Temporal Graph Transformer, and optimizing pedestrian trajectory prediction continuity by using an LSTM (Long Short Term Memory) model perceived by a gait cycle; multi-view semantic graph matching: constructing a 3D semantic voxel map containing vertical features, calculating scene similarity in combination with top view NetVLAD features and deformable graph matching, and dynamically adjusting semantic weight by using a situation encoder and knowledge graph reasoning; and carrying out multi-sensor fusion and robust positioning.

The invention relates to a building prefabricated part quality detection method based on multi-modal vision. According to the method, multi-modal data including 2D image data and 3D point cloud data are obtained, an improved YOLOv8 model is used for performing defect coarse positioning on the 2D image data, defect parameters are calculated, defect areas such as cracks and exposed ribs can be quickly locked, and the parameters of the defect areas can be obtained. And by means of SIFT feature matching and ICP point cloud registration technologies, comparing with a two-dimensional template and three-dimensional geometric parameters of the BIM standard component model to obtain a two-dimensional registration difference chart and a three-dimensional deviation thermodynamic chart. And finally, according to the defect confidence coefficient, the two-dimensional registration difference chart and the three-dimensional deviation thermodynamic diagram, a preset dynamic weighting rule is adopted to carry out joint decision making, and a quality detection result is obtained. According to the method, through the multi-modal data, the improved YOLOv8 model, the point cloud registration technology and the preset dynamic weighting rule, the false detection problem can be effectively solved, the detection reliability and accuracy under the complex working condition are improved, and the quality management level of the building prefabricated part is improved.

The invention discloses a steel plate surface defect detection method based on improved YOLOv8, and belongs to the technical field of stainless steel plate defect identification, and the method comprises the following steps: S1, data collection and division: obtaining steel plate surface defect picture data, and dividing the data into a training set and a test set; s2, performing data enhancement and preprocessing to obtain a sample training set; s3, constructing a defect detection model based on the improved YOLOv8 network; s4, designing a loss function, and decoding a prediction result; and S5, repeatedly inputting the to-be-detected sample data in the sample training set into the defect detection model for training until the training frequency reaches a preset epoch, outputting a final defect detection model, then inputting the to-be-detected sample data in the test set into the final defect detection model, and obtaining a steel plate surface defect detection result. According to the invention, the detection precision and accuracy of the small target can be effectively improved, and the missing detection and false detection phenomena of small target defects are reduced.

The invention relates to the technical field of fabric detection, and discloses a fabric defect intelligent detection method and system based on AI visual identification. According to the method, motion blur is quantized through motion state data, optical blur caused by fabric motion is eliminated through deconvolution solution, so that motion interference in the fabric transmission process is processed in a targeted mode, self-adaptive balance of the deblurring capacity and the feature retention capacity is achieved, and then based on the optical interference principle, the deblurring capacity and the feature retention capacity are improved. Through a dynamic calibration system combining hardware-level real-time compensation and multi-dimensional optical parameter calibration, dynamic optical parameter calibration of primary correction data is realized, then fabric defect characterization data is extracted to accurately obtain defect features, and finally, a detection-production line control closed loop is constructed through a quality quantitative index and a comprehensive risk value, so that fabric defect detection is realized. The fabric defect detection precision can be improved, so that the problem of high defect missing detection and false detection rate caused by optical data distortion due to movement and environment interference in a traditional method is effectively solved.

The invention provides a machine vision defect real-time detection and classification method and system based on deep learning, and relates to the field of machine vision detection.The method comprises the steps that regional enhancement weights are determined by calculating local entropy and gradient direction consistency, and regional self-adaptive enhancement is carried out; establishing a feature transfer sequence and progressively fusing features; generating and correcting a defect area probability distribution diagram; and constructing a dynamic decision matrix to calculate a comprehensive score for defect grading. According to the method, the defect detection accuracy under a complex background can be improved, false detection and missing detection are reduced, and real-time defect positioning and accurate classification are realized.

The invention discloses a light guide plate defect detection method and system based on a neural network, and particularly relates to the technical field of machine vision detection, and the method comprises the following steps: aiming at the problem of image instability of a light guide plate in a dynamic transmission or rotation process, continuously collecting an image sequence and extracting time domain features; and performing interference judgment in combination with the inter-frame consistency prediction coefficient and a first threshold to realize accurate identification of the abnormal image frame. For an abnormal image frame, further correcting the recognition credibility of the abnormal image frame by adopting a confidence adjustment and fusion mode, and meanwhile, introducing a frequency domain transformation and image enhancement strategy to compensate detail loss caused by motion blur; according to the method, inter-frame consistency analysis, confidence fusion regulation and control and frequency domain fuzzy recognition and compensation mechanisms are introduced, abnormal judgment and image quality restoration of the light guide plate image in the dynamic scene are realized, the recognition accuracy and stability of the neural network model on the defect type, position and confidence are improved, and the false detection and omission ratio is effectively reduced.

The invention relates to the technical field of artificial intelligence, and discloses an AI-based composite insulator internal defect ultrasonic detection method, which comprises a multi-mode ultrasonic probe array module, a signal preprocessing module, an AI defect analysis module, a dynamic parameter optimization module, an edge calculation module and a visual report module, the method comprises the following steps: acquiring a full-dimensional signal through a multi-modal ultrasonic probe array, and inputting the full-dimensional signal into a deep space-time convolutional neural network for defect recognition after adaptive noise reduction and feature fusion; the detection precision is improved by combining dynamic waveform matching and multi-physics coupling analysis; model lightweight and real-time processing are realized by adopting transfer learning and edge calculation. The system integrates the functions of parameter adaptive optimization, three-dimensional visualization and Internet of Things cooperation, solves the problems of low efficiency and high false detection rate of a traditional detection method, and improves the intelligent level and engineering applicability of composite insulator defect detection.

The invention relates to the technical field of semiconductor manufacturing, in particular to a semiconductor defect detection method and system based on deep learning. The method comprises the following steps: step 1, multiband cooperative imaging and dynamic scanning control; 2, performing multi-scale feature fusion and defect identification; 3, dynamic threshold judgment and multi-scale feature fusion network optimization: dynamically adjusting a judgment threshold according to probability distribution characteristics of a current batch defect thermodynamic diagram, applying an offset to the threshold in combination with a wafer process type, and determining the offset according to a balance relationship between a false drop rate and an omission rate in historical data; critical samples and misjudgment samples with classification confidence close to a threshold value in historical detection are periodically screened, incremental learning is performed on the last layer of the multi-scale feature fusion network, and upstream network weight is frozen to prevent feature drift. Through fine image processing, multi-band information fusion, dynamic threshold adjustment, incremental learning and other mechanisms, the defect detection efficiency and accuracy in the semiconductor manufacturing process can be effectively improved, and the detection performance is continuously optimized.

The invention provides a data flow monitoring method and system based on a large model, and the method comprises the steps: obtaining a data flow record set generated by a to-be-monitored system in a continuous operation period, carrying out the correlation path construction of the data flow record set, generating a data flow topological graph containing a node interaction relation and a time sequence dependency relation, and carrying out the correlation path construction of the data flow record set; calling a pre-trained circulation behavior analysis large model to perform node sequence pattern recognition on the data circulation topological graph, and generating behavior abnormal confidence and abnormal pattern labels of each node in the data circulation topological graph; and according to the abnormal behavior confidence and the abnormal mode label, screening an abnormal interaction node cluster in the data flow topological graph. According to the method, relevance between abnormal nodes and time sequence relevance are considered, missing detection or false detection is avoided, and the reliability of the monitoring effect is improved.

The invention relates to the field of insulator nondestructive testing, and discloses an insulator product surface defect nondestructive testing method based on AI identification, and the method comprises a data acquisition module, a preprocessing module, an AI analysis module, a decision output module, a self-optimization module, and an edge calculation node. Through multi-modal data fusion and a deep convolutional neural network technology, accurate detection of surface defects such as cracks, dirt and damage is realized, the omission ratio and the false detection rate are reduced, and the detection precision is improved compared with the traditional manual inspection efficiency; visible light, infrared thermal imaging, ultrasonic waves and hyperspectral data are combined, the surface and internal defects of the insulator are comprehensively covered, the detection rate of tiny cracks and hidden dirt is increased, and the technical limitation of a single sensor is broken through.

The invention relates to a multi-stage filtering road spilled object detection method based on dynamic difference analysis, which is suitable for automatic identification of unstructured foreign matters in video monitoring. The method comprises the following steps: firstly, extracting a reference image road mask, eliminating vehicle and pedestrian interference by using YOLOv8 detection, and extracting a motion candidate area through a frame difference method and background modeling; and then context expansion and super-resolution reconstruction are carried out on the candidate region, the candidate region is converted into an HSV space, multi-dimensional features such as color similarity, structural similarity and shadow determination are synthesized for screening, false detection is further removed in combination with inter-frame time sequence consistency, and finally a stable detection result is output. The method provided by the invention has the advantages of strong anti-interference capability, high adaptability, high detection precision and the like, and is suitable for the intelligent recognition task of the expressway thrown objects in a complex environment.

The invention relates to an intelligent visual identification technology-based cold solder joint microcrack intelligent discrimination method, which comprises the steps of collecting an initial RGB image of a to-be-detected welding spot, carrying out two-dimensional discrete cosine transform and inverse two-dimensional discrete cosine transform on the initial RGB image to obtain an enhanced image, and fusing the enhanced image with an R channel of the initial RGB image to obtain a fused image; forming a dual-channel feature map; calculating the phase consistency of the dual-channel feature map, and obtaining a suspected candidate region of the pseudo soldering microcrack through an adaptive threshold segmentation method; acquiring an RGB image sequence of a continuous time sequence of the welding spots, and performing anomaly detection to obtain an abnormal region set; and constructing a welding spot thermal diffusion model, and inputting the geometric parameters and the environmental parameters in the abnormal region set into the welding spot thermal diffusion model to obtain a final judgment result of the pseudo soldering microcracks. According to the method, through multi-dimensional feature fusion and continuous time sequence dynamic tracking, the detection precision of the pseudo soldering microcracks is remarkably improved, the false detection rate is reduced, and the final judgment result is more accurate.

The invention provides a small target identification method and system based on YOLOv5, and the method comprises the steps: collecting a multi-scale image in a target range, and forming an original data set; performing image enhancement on the original data set through superpixel segmentation and adversarial enhancement operation to obtain an enhanced data set; performing multi-scale frequency domain aliasing enhancement on the enhanced data set through frequency domain decomposition and frequency band exchange operation to obtain a to-be-detected data set; improving the YOLOv5 model to obtain a small target recognition model; and performing small target identification on the to-be-detected data set through the small target identification model to obtain an identification result. According to the method, small target recognition is realized through the dynamic feature pyramid and the double-path detection head in combination with cross-level kernel sharing and a space-frequency double-domain attention mechanism, the problem of detail loss caused by a traditional static feature pyramid is solved, the recognition precision is improved, and the false detection rate is reduced.

The invention relates to the technical field of image processing, and discloses a distribution network line multi-scale target defect identification method and device, equipment and a medium. The method comprises the following steps: acquiring a distribution network line inspection image, and respectively inputting the distribution network line inspection image into a large-scale identification model and a small-scale identification model to obtain a plurality of large-scale defect rectangular frames and a plurality of large-scale defect types, and a plurality of small-scale defect rectangular frames and a plurality of small-scale defect types; performing hierarchical filtering fusion on each large-scale defect rectangular frame and each large-scale defect type as well as each small-scale defect rectangular frame and each small-scale defect type by utilizing a preset target scale deletion rule to obtain a multi-scale target defect recognition result; and judging whether the multi-scale target defect identification result is null, if so, recording the multi-scale target defect list as null, and if not, recording the multi-scale target defect identification result into the multi-scale target defect list. The working efficiency of electric power inspection is greatly improved, and the phenomena of missing inspection and false inspection are effectively prevented.

The invention relates to a multi-element microphone array sound source localization method based on multistage signal preprocessing and subspace spectrum optimization, and belongs to the technical field of acoustic detection. Aiming at the problems of poor noise immunity, weak multi-sound-source resolution capability and low calculation efficiency of the existing sound source positioning technology, a triple signal preprocessing and subspace collaborative optimization scheme is provided; firstly, incoherent noise is suppressed through phase coherent filtering, a signal is reconstructed through principal component analysis, and phase deviation is calibrated through fundamental frequency; then constructing a guiding matrix and decomposing a noise subspace, and extracting a coarse positioning result; and finally, high-precision angle optimization is realized based on a chaos initialization differential evolution algorithm, and the efficiency is improved by combining a dynamic search range and an early stop mechanism. According to the method, the anti-interference capability in a low signal-to-noise ratio environment is remarkably enhanced, the problems of missing detection and false detection during dense distribution of multiple sound sources are effectively solved, meanwhile, the positioning precision and the real-time performance are considered, and the method is suitable for acoustic fault detection of complex scenes such as power transmission line inspection.

The invention provides an environment detection method and system based on a sample target detection model. The method comprises the following steps: synchronously acquiring an environment image, a video stream and physical parameters by using a multi-mode sensor; decomposing the data into image features and environmental parameter components through a dual-time sequence control signal, and realizing space-time alignment by adopting a linear phase filter; constructing a foreground region template based on the depth information, and generating target recognition feature representation containing an abnormal blurred target; adversarial training is carried out on the lightweight target detection network in combination with a transfer learning strategy, the network integrates convolutional features and a Transform attention mechanism, and the weight is dynamically adjusted through environmental parameters; fusing a target result and sensor data in real-time detection, and inputting a decision tree model for risk grading; and after the early warning is triggered, reconstructing a false detection sample through an online learning mechanism and iteratively optimizing the model. The system correspondingly comprises a multi-modal data acquisition module, a data enhancement and annotation module, a model training module, a real-time detection and fusion module and an early warning and optimization module. According to the invention, through multi-source data fusion, dynamic data enhancement and an adaptive compensation mechanism, the small target detection precision, the environmental adaptability and the real-time early warning capability are significantly improved.

The invention provides an end-to-end tiny target detection method, and aims to solve the problems of missing detection and false detection of tiny targets caused by interference of sparse features, halo, noise and the like. According to the method, a TINYDETR model is constructed, and the TINYDETR model is composed of an HGNetv2 backbone network, an LGFSI module, an SO-CSFF module and a decoder with an auxiliary prediction head. Wherein the LGFSI module realizes global-local information interaction through joint modeling of a frequency domain and a spatial domain, and background interference is effectively suppressed; the SO-CSFF module enhances the fusion of shallow details and deep semantics through a bidirectional feature flow mechanism, and enhances the feature expression of a tiny target. After the model is trained and optimized, high-precision detection of a tiny target can be realized.

The invention discloses a transformer substation defect identification method and system based on multi-mode open set associative reasoning. The method comprises the steps that transformer substation oily equipment defect images are collected and marked; constructing a multi-modal data set of image-text matching; yOLOv8 is adopted to extract visual features, and a CLIP model is adopted to process text features; designing a re-parameterized visual language path aggregation network for feature fusion; training the model based on a contrast loss function; the applications are deployed after the performance is evaluated through multiple indexes; through the multi-modal feature fusion and the re-parameterization path aggregation network, the problem that the oil leakage defect is difficult to mark due to liquid flowability and form variability is effectively solved, the false detection condition under multi-device cross interference is remarkably improved, and by combining multi-scale feature learning and an attention mechanism, the oil leakage detection accuracy is improved. And the identification accuracy of the oil stain defect is greatly improved.

The invention provides an evidence obtaining method and system based on image processing, and the method comprises the following steps: S1, generating a pixel-level depth-of-field distribution diagram of an input image through a multi-scale encoder-decoder network, employing an edge perception optimization layer in a decoding stage, and improving the depth-of-field boundary precision through minimizing a local gradient consistency loss function; s2, performing depth-of-field rationality verification based on an optical imaging physical model, and triggering a first-level tampering alarm by calculating a defocusing fuzzy radius and a gradient direction of a selected region when a difference between the defocusing gradient directions of a target region and a background region exceeds a preset threshold value; and S3, dynamically positioning a key pixel region, identifying a depth-of-field mutation boundary by using an edge detector, calculating by combining local texture complexity, screening a pixel set of which the entropy value is higher than a threshold value and which is located at the mutation boundary, correlating metadata to verify the rationality of the physical size and the spatial position of an object, and eliminating false detection caused by perspective transformation.

The invention discloses an industrial image anomaly detection method and device, equipment and a storage medium, and relates to the field of image recognition. Obtaining a defect-free product graph, making a comparison data set based on the defect-free product graph, and performing pre-storage processing; performing feature point shape matching on the comparison data set based on the received to-be-detected image and the extracted feature point data, and determining a comparison image of the to-be-detected image; taking the to-be-detected image as a target object, correcting a product image area in the image, and aligning the product image area with the product image area of the to-be-detected image according to a pixel shift operation; and comparing the aligned product image areas, and identifying abnormal defects in the to-be-detected image. According to the scheme, the comparison data set of the defect-free product image is constructed, the feature point matching and pixel-level alignment technologies are combined, the problem of false detection caused by product position deviation or form change in a traditional method is effectively solved, the adaptability to illumination change is improved through brightness adjustment and difference matrix analysis, and the detection accuracy is improved. The method has the advantages of high detection precision, high environmental adaptability and high processing efficiency.

The invention discloses an efficient target detection method in a foggy environment, and relates to the technical field of target detection. According to the method, a lightweight foggy day target detection model is established and called DF-DETR, a defogging module of a double-branch structure is designed through an edge enhancement module, and target features of a foggy day image are effectively captured; then, a dual convolution feature extraction module DualConv-Block is designed, so that feature extraction is enhanced, and meanwhile, the complexity and the calculation amount of the model are remarkably reduced; besides, an EAA attention mechanism is combined with an intra-scale feature interaction module to form an AIFI-EAA module, and the AIFI-EAA module is integrated into the hybrid encoder, so that the attention capability of the model on dense targets is improved, and missing detection and false detection are effectively reduced; finally, a dynamic sampling scale attention feature fusion module is designed, alignment of multi-scale features is achieved through dynamic up-sampling, the flexibility and robustness of feature expression are enhanced, and the fusion and expression ability of the multi-scale features is further optimized.

The invention relates to the technical field of artificial intelligence and the technical field of semiconductor detection, and discloses a wafer defect identification method based on feature fusion, which comprises the following steps: acquiring a first type of image and a second type of image of a wafer detection area, and performing alignment processing; performing feature extraction on the first type image and the second type image to generate a first type feature and a second type feature; fusing the first type of features and the second type of features to generate fused features; filtering the fusion features, and screening target fusion features meeting a confidence threshold condition; and determining a defect position and corresponding process layer information based on the target fusion feature. According to the invention, features of different types of images are fused, so that the characterization capability of wafer surface defects is enhanced, and the recognition precision of a detection system is improved; by screening the target fusion features meeting the confidence threshold, false detection and missing detection are reduced, and the detection reliability is improved.

The invention discloses an insulator defect detection method based on improved YOLOv11n. The insulator defect detection method specifically comprises the following steps of: detecting defects of an insulator; the method comprises the following steps of: 1, acquiring an insulator defect image data set, dividing the data set into a training set, a verification set and a test set, and preprocessing; 2, in the YOLOv11n network, an SCConv module is adopted to replace a C3k2 module, an SPPCSPC module is adopted to replace an SPPF module, an SBA module is adopted to replace an Upsample module, and a new LXMstrip Pool module is adopted, so that an improved YOLOv11n model is obtained; 3, training the improved detection model by adopting the training set and the verification set, and storing the optimal trained model; and 4, carrying out precision test on the optimal model by adopting the test set, and obtaining a final insulator defect detection model when the precision requirement is met. Compared with the prior art, the insulator defect detection method based on the improved YOLOv11n disclosed by the invention has the advantages that the detection precision of the insulator defect can be effectively improved, and the problems of missing detection and false detection of the defect during actual detection are avoided.

The invention discloses an intelligent data query system and method based on natural language processing, and relates to the technical field of natural language processing and database query. According to the method, natural language input and database mode information are received, a historical query log is combined to construct mode knowledge representation, a query skeleton is generated on the basis, a fine-tuned large language model is called to generate candidate SQL statements, error detection and ambiguity recognition are carried out on the candidate statements, and the query result is obtained. And if necessary, triggering interaction clarification and updating a query result according to user feedback. And meanwhile, the wrong clauses are locally repaired through a gating mechanism, and the clauses are returned and regenerated when multiple times of repair fails, so that the correctness of the query statement is ensured. And finally, after the SQL is executed in the database, a result is fed back to the user. Besides, the system records generation and repair tracks in the operation process, and continuously optimizes the model based on reward shaping, comparative learning and self-game training, so as to improve the generalization ability in different business scenes.