243 results about "Defect repair" patented technology

The invention discloses a defect repair method based on digital twinning and friction stir welding technologies, and relates to the technical field of intelligent manufacturing and digital twinning, and the defect repair method comprises the following steps: synchronously capturing full-dimensional data of a welding area through a multi-mode sensor array integrated by an actuator; secondly, segmenting defect boundaries by adopting a deep learning algorithm, constructing a dynamic twin model in combination with thermal-force field coupling simulation, and accurately mapping defect three-dimensional features; generating a repair track according to the twinborn model, converting the repair track into a robot joint instruction through a curved surface parameterization mapping algorithm, and implanting real-time anti-collision constraint; in the repairing process, based on reinforcement learning control of the material rheological resistance and the temperature gradient, the rotating speed, the advancing speed and the down force of the tool are dynamically adjusted; and after repairing, micro-focus CT scanning is started immediately, actually measured data is compared with twinborn prediction, and when the deviation exceeds a threshold value, a re-repairing process is triggered automatically. The method solves the problems that a traditional method depends on manual intervention and the precision of a sensor is easily interfered by the environment.

The embodiment of the invention discloses a circuit board defect automatic detection method and system based on machine vision, which are used for improving the precision and efficiency of circuit board defect detection and repair, and the method comprises the following steps: obtaining a surface image data set of a target circuit board; performing image preprocessing operation on the surface image data set to generate a preprocessed image data set; inputting the preprocessed image data set into a pre-trained defect feature extraction model, and performing multi-level feature fusion processing on each piece of image data through the defect feature extraction model to generate a multi-dimensional defect feature set; determining a defect positioning information set of the target circuit board according to a matching degree calculation result between the multi-dimensional defect feature set and a preset defect feature template; and transmitting the defect positioning information set to a defect repair control terminal, and triggering the defect repair control terminal to generate a repair path planning instruction according to the defect positioning information set.

The present invention discloses to a selective-laser-melting (SLM) printing defect detection and repair method and system based on a deep learning network and belongs to the technical field of additive manufacturing. The method includes: training a first neural network model through a defect dataset to obtain a defect recognition model; in a printing process of a part to be detected, performing online defect recognition through the defect recognition model; if a current layer has no defects, continuing to perform powder spreading and printing on a next layer; if a defect is recognized in the current layer, selecting whether to repair the defect according to a defect type; if defect repair is needed, after the current layer is printed, suspending powder spreading once, predicting laser remelting parameters by adopting a pre-trained second neural network model, and performing laser remelting repairing until the current layer has no defects; and repeating the processes of online defect recognition and laser remelting repair until the part to be detected is printed. In the present invention, the online defect recognition and repair are realized, and the real-time performance of the defect repair is improved.

The invention provides a welding structural part monitoring method and system based on a vibration acoustic method, and relates to the technical field of welding monitoring, and the method comprises the steps: constructing a defect classification model and a defect strategy model, employing an ultrasonic phased array to detect a to-be-detected welding line of a welding structural part, obtaining a first image, inputting the first image into the defect classification model, and obtaining a second image; a second image is obtained, whether welding defects exist in the second image or not is judged, and if not, the welding quality evaluation result of the to-be-detected weld joint is set to be optimal and output; and if yes, inputting the second image into the defect strategy model to obtain a defect repairing strategy of the second image. According to the method, the defects of the welding structural part are monitored in real time, the welding defects are classified on the basis of the defect classification model, then the welding defects are evaluated, the targeted repairing strategy is obtained through the defect strategy model on the basis of the evaluation result, and the purpose that the welding technology is adjusted in time for repairing on the basis of the welding defects occurring in the welding process is achieved.

The invention discloses an RFID tag production line defect real-time detection system and compensation method based on machine vision, and belongs to the technical field of intelligent manufacturing and quality control. The system comprises an image acquisition module, a defect detection module, a compensation execution module and a feedback control module, based on defect types and positions, a grading compensation strategy is triggered, the grading compensation strategy comprises real-time printing parameter adjustment, laser accurate repair of a defect area or automatic rejection of a mechanical arm, and a process parameter library is synchronously updated through an industrial Internet of Things platform. The innovation points are as follows: (1) a multi-modal defect data fusion method is provided, and the detection robustness under complex working conditions is improved; (2) a lightweight double-branch detection network is designed, and the detection speed and precision are both considered; and (3) developing a compensation instruction cooperative control algorithm to realize seamless connection between defect repair and production line rhythm. According to the scheme, the defect omission ratio can be reduced to 0.5% or below, the compensation response time is shortened to be within 200 ms, and the production line yield and the production efficiency are remarkably improved.

The invention provides a software code defect detection method and system based on program code feature fusion, and the method comprises the steps: carrying out the grammar and structure inspection of a code through a static analysis tool, and extracting defect features related to control flow chaos, exception handling and resource leakage; running a preset test case through a dynamic analysis tool, capturing an execution path and an abnormal behavior in a runtime state, and obtaining defect features which are not covered by static analysis; if the output result of the decision tree model is that defects exist, grouping comprehensive defect feature vectors by adopting a density-based clustering algorithm, determining the types and distribution ranges of the defects, and selecting the density clustering algorithm to process defect features in non-spherical distribution; and according to a clustering result, generating a defect repairing suggestion, combining code logic and execution path information, and determining a specific position and an influence range of the defect. According to the method, the stealth defect in the code can be effectively identified and positioned, the software quality and reliability are improved, and timely and accurate defect repairing guidance is provided for developers.

The invention provides a cranio-maxillofacial hard tissue deletion repair method based on a general repair model, and relates to the technical field of jaw defect reconstruction, the method comprises the following steps: constructing a cranio-maxillofacial hard tissue general repair model by using a plug-and-play conditional attention mechanism and a query generator based on curvature perception; inputting the cranio-maxillofacial hard tissue point cloud data into a cranio-maxillofacial hard tissue general repair model, and training by using a weighted loss function to obtain a trained cranio-maxillofacial hard tissue general repair model; performing de-noising processing on the missing repair point cloud features to obtain de-noised missing repair point cloud features; and carrying out point cloud gridding modeling processing by using the denoised deletion repair point cloud features to obtain a cranio-maxillofacial hard tissue deletion repair result, and completing the deletion repair of the cranio-maxillofacial hard tissue. The problem that repair and reconstruction of hard tissues at the head part are difficult to solve uniformly is solved.

The invention relates to the technical field of software testing, and discloses a software defect detection method, a software defect repair method and a software defect repair system, and the detection method performs defect detection on software based on a large language model. The problem that software defects are difficult to efficiently and accurately detect in the prior art is solved.

The invention relates to the technical field of defect repair, and discloses a fabric texture defect repair method and device based on a generative adversarial network. The method comprises the following steps: decomposing a defect fabric image to be repaired to obtain a fabric texture direction feature matrix and a defect area positioning mask; performing cross-domain feature unified processing on the fabric texture direction feature matrix to obtain a unified feature code; performing similarity retrieval to obtain an optimal matching texture reference feature; inputting the defect area positioning mask and the defect fabric image into a first-stage coarse repair generator for structural reconstruction to obtain an initial fabric repair image; and inputting the initial fabric repair image into a second-stage fine repair generator for wavelet multi-scale detail optimization and boundary fusion correction to obtain a target fabric repair image. According to the method, visual mutation of the repaired area and the original area is effectively eliminated, the natural transition effect of seamless connection is achieved, and the calculation efficiency is optimized while the repair quality is guaranteed.

The invention provides a defect repairing method and device based on a large model, electronic equipment and a medium, and relates to the field of defect repairing, and the method comprises the following steps: obtaining original log data generated in real time; analyzing the original log data through a preset model, and determining defect logs and user operation parameters; determining a target code file corresponding to the defect log; repairing the target code file through a preset model, and generating a repairing scheme to determine a repaired code file; and submitting the repair code file to a code warehouse to cover the target code file, determining an updated code warehouse, and determining a production system instance according to the updated code warehouse. According to the method, the original log of the deployed system is obtained in real time, the defect log and the associated user operation parameters are automatically analyzed and determined, the target code file is positioned, the repair scheme adjustment code is generated, then the repair code is submitted to the warehouse for updating, the production system instance is determined, and automation of the whole process of defect repair is achieved.

The invention discloses a paper product packaging defect real-time detection system based on edge calculation, and relates to the technical field of packaging defect detection. Comprising a multi-modal data acquisition module for acquiring packaging multi-dimensional image data by using a camera and a sensor; the edge intelligent processing unit and the heterogeneous architecture are combined with dynamic scheduling to realize rapid data processing; the defect feature enhancement network is used for improving an algorithm to accurately extract defect features; the edge decision optimization engine is used for reinforcing learning to optimize a detection strategy; the industrial-grade communication module is used for guaranteeing data transmission through 5G and TSN hybrid; the closed-loop repair execution system is used for realizing automatic repair of defects; in addition, auxiliary modules such as a traceability module and an energy optimization module are further arranged, and all the modules cooperate to achieve efficient detection and repair of packaging defects. The paper product packaging detection level is greatly improved, the speed is improved by 2.5 times, energy consumption is reduced, detection and closed loop repairing are achieved, intelligent upgrading of a production line is promoted, and the labor cost and the product reject ratio are reduced.

A method of determining a repair method for a defect in a bladed rotor can comprise comparing the defect in a three-dimensional model of an inspected integrally bladed rotor (IBR) to a plurality of known defects from a repair database; determining the defect is matching a known defect in the plurality of known defects in the repair database; and generating a repair process associated with the known defect in response to determining the defect is matching the known defect. A method can also comprise determining a potential repair process; performing a structural simulation of a finite element model for the inspected IBR with the potential repair; performing an aerodynamic simulation of an aerodynamic model for the inspected IBR with the potential repair; and generating the potential repair process for the defect in response to determining the inspected IBR with the potential repair meets structural and the aerodynamic criteria.

The invention relates to the technical field of friction stir repair, and discloses a multi-dimensional defect friction stir solid-phase repair planning system and a defect repair method. The system comprises a defect acquisition unit for acquiring multi-dimensional defect data of a workpiece and generating a time-space correlation data set covering defect depth distribution, a material heat affected zone range, a friction tool wear state and environment temperature and humidity parameters; the feature reconstruction unit decomposes the data set and separates core and abnormal feature tensors; the repair planning unit generates a multi-region cooperation scheme containing a tool path, a pressure sequence and a rotating speed strategy in combination with the core features and the process constraints; the dynamic adjusting and optimizing unit monitors abnormal characteristics and adjusts the scheme when conditions are met; the collaborative strategy unit responds to cross-regional defect linkage abnormity, allocates a standby tool and updates a track offset; and the feature fusion unit fuses the multi-scale monitoring features to generate a repair quality evaluation feature set. The system enhances the comprehensiveness and adaptability of complex defect repair.

The invention discloses an AI software system defect repairing method, device and equipment of a government affair platform and a medium, and relates to the technical field of computers, and the method comprises the steps that target data of a current platform AI software system in the running process is obtained through an eBPF probe loaded by a K8s cluster of the government affair platform, and defect fingerprints are determined based on the target data; determining a target defect classifier based on the graph neural network, the teacher model, the student model, a preset distillation mechanism and a KL divergence loss function, determining a target strategy composer by using reinforcement learning and a preset strategy library, and determining a target defect repair strategy based on the defect fingerprint, the target defect classifier, the business data and the target strategy composer; and performing defect repair on the current platform AI software system by using the target defect repair strategy, verifying the repaired AI software system, and if the verification is passed, completing the defect repair operation. And the AI system defect automatic detection and repair efficiency is improved.

The invention relates to the technical field of pipeline internal defect detection and repair, and particularly discloses a pipeline repair method based on defect detection and three-dimensional reconstruction, which comprises the following steps of: 1, scanning the inner wall of a pipeline through a pipeline internal defect detection module, and confirming the position and the type of a pipeline defect; 2, judging whether a pipeline defect is a repairable type or not through a pipeline internal defect detection module; 3, determining three-dimensional information coordinates of pipeline defects relative to the pipeline through a pipeline internal defect detection module; 4, if the pipeline defects can be repaired, image information of the pipeline defects is collected through a pipeline internal defect repairing module, three-dimensional reconstruction and trajectory planning are carried out, and repairing is completed. According to the method, the pipeline defects are recognized and detected through the methods of defect detection, three-dimensional reconstruction and trajectory planning, intelligent recognition detection and automatic welding repair of the pipeline defects are achieved, and the recognition efficiency and accuracy are high.

The invention belongs to the technical field of display panel repairing, and discloses a display panel laser repairing method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining a panel image of an OLED display panel, and carrying out the defect detection of the panel image, obtaining defect positions, defect sizes, defect types and repair formulas of all defects capable of being repaired by laser in the OLED display panel, and performing repair path planning according to a coordinate sequence repair mode, a same formula sequence repair mode and a comprehensive repair mode according to the defect positions, the defect sizes, the defect types and the repair formulas. And obtaining an optimal repair path with the minimum defect repair time, and performing laser repair on the defect capable of being subjected to laser repair in the OLED display panel based on the optimal repair path. The OLED display panel is subjected to laser repair by comprehensively considering the optimal repair path planned by multiple repair sequences and time cost, so that the laser repair efficiency of the display panel is improved.

The utility model provides a novel reinforced concrete defect repairing structure, which aims at a tunnel in which defects of a reinforced concrete lining are large and are difficult to process and secondary damage is possible to process according to a conventional scheme, and comprises a steel lining and a grouting pipe, steel pipe joints are arranged between the steel pipes and are connected in an annular welding mode, a grout inlet and a grout outlet of the grouting pipe are led out of the first section of steel pipe and the tail section of steel pipe respectively, and a plurality of open holes are formed in the middle section of the grouting pipe. The utility model has the advantages that the original defective concrete lining section is directly replaced by the steel lining, a practical, effective and enforceable repairing scheme which occupies a small area on the concrete section is provided, and the secondary damage to the project caused by the secondary damage of the conventional defect repairing scheme is not needed to be worried.

According to the mask defect in-situ repairing method, the femtosecond laser is adopted for defect repairing, and all the advantages of the femtosecond laser are absorbed. Femtosecond laser is ultrashort pulse laser with'femtosecond 'as a time unit, the pulse duration time of the femtosecond laser is extremely short (femtosecond magnitude), and the peak power of the femtosecond laser is extremely high (the maximum can reach pat watt). Therefore, according to the repairing method, the repairing precision can be reduced to 15 nm, the heat affected zone is smaller than 1 micron, the repairing time can be shortened to 100 ms for the defect of 100 nm, the roughness of the repaired surface is smaller than or equal to 0.5 nm, meanwhile, the good effects of high repairing precision, low heat affected zone, high repairing speed, low equipment cost and simple operation are achieved, and the rejection rate of the mask is reduced. The method is especially suitable for repairing advanced process masks, such as EUV and ArF, and display panel masks, such as OLED and FMM, and MEMS device masks.

This invention provides a comprehensive digital and intelligent operation and maintenance management method and system for power distribution networks, relating to the field of computer data processing technology. The method includes: S1, obtaining pole and tower information collected by drones during line inspections, performing defect mining and analysis on the pole and tower information, and integrating the analyzed defect information with the pole and tower information to form a pole and tower information collection table; S2, selecting and extracting corresponding data from the pole and tower information collection table, and visualizing the data on a GIS map containing the lines obtained from the pole and tower information collection table using preset markers to generate an inspection status map; S3, statistically classifying the analyzed defect information and matching it through a big data platform to generate a defect repair and management summary table; S4, analyzing the faults using an AI model and a big data platform, combining the inspection status map and the defect repair and management summary table, obtaining priority-ranked repair tasks, and proposing corresponding solutions.

The invention discloses a program defect repairing method and device, equipment and a storage medium. The method comprises the steps that a defect code segment is obtained from a first program code which is reported to be wrong, and a similar defect code segment and a similar repair patch corresponding to the defect code segment are inquired in a historical code library; constructing a first prompt word based on the defect code segment, the similar defect code segment and the similar repair patch, generating a repair patch based on the first prompt word, and repairing the first program code through the repair patch to obtain a second program code; and under the condition that the verification of the second program code is not passed, regenerating a repair patch based on a defect code segment appearing in the verification process of the second program code, and repairing the second program code through the newly generated repair patch. According to the method, the big language model is guided to generate the high-credibility repair patch through the historical similar defects and the repair patch thereof, and the problem that in the prior art, the repair patch is unreliable due to the fact that the big language model is separated from an actual repair case is solved.

The invention relates to the technical field of artificial intelligence, and discloses an application software code defect automatic detection method and system based on artificial intelligence, and the method comprises the steps: obtaining a source code of to-be-detected application software, and carrying out the deep structure semantic coding of the source code, and obtaining a code semantic feature vector; performing context weight distribution on the code semantic feature vector to obtain weighted context aggregation representation; performing defect feature collaborative analysis on the weighted context aggregation representation to obtain defect types and position information; performing semantic association traversal on the defect type based on a preset defect knowledge graph to obtain an association repair information group; performing logic priority arrangement on the repair strategies in the associated repair information group to obtain a defect repair suggestion sequence; generating a code defect detection report according to the defect type, the position information and the defect repair suggestion sequence; the code defect detection efficiency can be improved.

The invention relates to the technical field of generative image restoration, in particular to a defect restoration method for a three-dimensional reconstruction model of a porous structure, which comprises the following steps: generating a basic three-dimensional model of a target object according to a target resolution; scanning a target object and determining a corresponding to-be-repaired area; according to the target depth of the to-be-repaired area, determining a corresponding repair focus and a repair resolution; generating a repair container, and placing the geometric center of the repair container on the repair focus; filling the repair container with the repair resolution according to a preset sequence, and generating a plurality of repair models; identifying feature points of each repair model and the basic three-dimensional model, and merging the repair models into the basic three-dimensional model according to the similarity of the feature points; according to the method, the corresponding repairing features are selected, the model for repairing is merged into the target model according to the features, and edge smoothing processing is performed, so that the integrity of the model with the holes is effectively improved while the quality of the model is improved.

The invention relates to the field of building detection, and discloses a defect repairing method, device and equipment for a building outer wall, and the method comprises the steps: building an outer wall surface image set of a target building; identifying defect type, size and depth information of each defect in the outer wall surface image; establishing a target three-dimensional model of the target building, and generating a position coordinate corresponding to each defect; and generating a repair path of each defect according to the type, the size, the depth and the position coordinates of the defect. The building outer wall defect detection efficiency and accuracy can be effectively improved, a detailed report of the defects can be automatically generated, analysis and future defect development state prediction can be performed according to the actual situation and historical data of the building, reasonable maintenance suggestions are provided, the safety of the building outer wall is ensured, and the method is suitable for popularization and application. And long-term maintenance of the outer wall is facilitated.

The present invention relates to the technical field of pipeline data processing and health status assessment, and discloses a pipeline internal detection method and system based on multi-sensor fusion. The method includes: calculating the health index of pipeline status assessment data to obtain pipeline health status indicators, performing risk prediction on pipeline health status indicators to obtain risk levels; performing pipeline defect feature identification to obtain defect feature data, performing multi-category classification processing on defect feature data to obtain defect type data; performing precise position analysis on defect type data to obtain defect spatial distribution data and perform structural integrity assessment to obtain defect severity quantification data set; performing multi-criteria decision analysis on defect severity quantification data set and risk level to obtain defect repair priority ranking data, performing intelligent repair plan generation on defect repair priority ranking data to obtain repair suggestion data. The present invention can achieve a more comprehensive and accurate assessment of pipeline status.

The invention discloses a monorail crane track defect detection system and method, and relates to the technical field of track detection, the monorail crane track defect detection system comprises a vehicle selection module, a data acquisition module, a data judgment module, a secondary acquisition module and a defect division module, comparing the pilot operation data with a standard threshold interval to judge whether the vehicle meets requirements or not; the technical key points are as follows: a vehicle is detected before detection, and the reference of the detection is taken as the reference for subsequently judging whether a defect exists on a monorail crane track or not, so that the subsequent judgment on the defect is more accurate, smaller defects can be identified, and then the defect position is further acquired and analyzed; defect content and defect levels are analyzed, a corresponding defect repair plan is conveniently formulated according to the defect levels subsequently, the amount of data processed in the whole process is small, defect positions can be analyzed in real time, and the method has a good use prospect.

The invention discloses a welding seam defect detecting and repairing method which comprises the following steps: performing defect detection on a welding seam structure based on a plurality of defect detection modes to obtain a defect detection result; performing classification identification processing on the defect detection result to obtain corresponding defect information; and generating a corresponding repair strategy according to the defect information, and performing defect repair treatment on the weld joint structure based on the repair strategy. In the aspect of detection, detection and classification identification are carried out through multiple defect detection modes, so that weld defects are captured more comprehensively and accurately, omission and misjudgment are reduced, and the detection accuracy of a complex weld structure is improved; in the aspect of repairing, a targeted repairing strategy is generated based on accurate defect information, the quality problem caused by blind operation in traditional repairing can be effectively solved, the repairing failure risk is reduced, it is ensured that the quality of a repaired welding seam is more reliable, and the requirements of nuclear power equipment for the safety and reliability of the welding seam are met; therefore, the welding seam detection precision and the repair quality are remarkably improved.

The invention relates to an epitaxial growth temperature detection method, which comprises the following steps: providing a test wafer which comprises a calibration wafer and a temperature measurement wafer; performing defect treatment on the calibration wafer and the temperature measurement wafer so as to introduce crystal defects into the calibration wafer and the temperature measurement wafer; performing defect repair treatment on the calibration wafer at different target calibration temperatures, and obtaining defect repair information at corresponding temperatures; according to the target calibration temperature and the corresponding defect repair information, establishing a temperature measurement database used for representing the corresponding relation between the crystal state and the growth temperature; transferring the temperature measurement wafer into epitaxial equipment, and raising the temperature of the epitaxial equipment to an epitaxial temperature; obtaining crystal state information of the temperature measurement wafer at different positions; and obtaining the growth temperature corresponding to each crystal state based on the temperature measurement database and the crystal state information of the temperature measurement wafer. According to the epitaxial growth temperature detection method, temperature distribution in the epitaxial process can be effectively monitored and optimized, and the quality of a silicon carbide epitaxial film and the performance stability of a device are remarkably improved.

The invention discloses a multi-modal alignment and weak supervised learning-based pipeline defect detection and report generation method, which comprises the following steps of: performing data cleaning and standardization processing on pictures in a pre-acquired pipeline defect report and corresponding text information, and labeling the processed pictures, converting the processed picture and text information into high-dimensional semantic vector representation to obtain a text vector and a picture vector, and establishing a data set in combination with the annotation of the picture; based on the data set, training a multi-modal vector aligned large language model, and realizing defect detection and report generation to obtain a defect detection result and a report; performing validity verification and expert feedback correction on the defect detection result and report; the pipeline defect detection and report generation task can be efficiently and accurately completed, and the testing efficiency and the intelligent level in the defect repairing process are improved.

The invention relates to an automatic program defect repairing method combining source code semantics and abnormal feedback, and belongs to the technical field of computer programs. The method comprises the following steps: firstly, selecting a latest released version of a to-be-repaired program, and randomly adding disturbance to the version program to generate a plurality of defect variant programs; secondly, extracting elements such as a defect row and a defect neighbor row of the defect variant program to construct a defect context; compiling errors or function errors after execution of the defect variant program are obtained to serve as abnormal feedback; finally, the defect context and the abnormal feedback are vectorized and spliced into training data, a loss function is designed to minimize the semantic difference between the repair code and the latest issued program, and a defect repair model is trained and generated. Aiming at the problem that a model is difficult to learn normal program characteristics due to the fact that open source training data and to-be-repaired program semantic differences are large in an existing method, the number of correctly repaired defects is increased by constructing proprietary training data and designing a target function to guide the model to learn semantic and contextual information.

A disc changing system for a robotic defect repair system is presented. The system has a first abrasive disc and a second abrasive disc. The first and second abrasive discs are coupled to a liner. The system includes an abrasive disc placement device configured to automatically: remove the first abrasive disc from the liner, transport the first abrasive disc to a robotic tool of the robotic defect repair system, and place the first abrasive disc on a backup pad coupled to the robotic tool. The system also includes an abrasive disc remover configured to automatically remove the first abrasive disc after receiving a removal signal. The system also includes a controller configured to send an instruction to the disc placement device to remove, transport and place the first abrasive disc, instruct the robotic tool to conduct an abrasive operation. The controller is also configured to send the removal signal. The controller is a processor and the instructions are stored on a non-transitory computer-readable medium and executed by the processor.