155 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 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 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 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.

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 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 discloses a software code defect automatic detection and repair system based on deep learning, and relates to the technical field of deep learning technology.A data acquisition module acquires multiple source codes and metadata and adapts to a mainstream programming language; the preprocessing module cleans codes, extracts features and divides data sets; the detection model is used for identifying 12 types of defects by using a Bi-LSTM (Bidirectional Long Short Term Memory) and Transform mixed architecture; the repair model generates a repair scheme based on CodeBERT; defect verification is integrated with static / dynamic verification; the system management realizes configuration, user, task and log management, and supports RBAC authority control. According to the method, the code defect detection accuracy and repair efficiency are improved, and multiple defect types and programming languages are covered; the repairing scheme adapts to the scene and is explained, so that the labor cost is reduced; model dynamic updating and cross-language adaptation are supported, a development process can be integrated, and the comprehensive effects of code quality improvement, efficiency improvement and maintenance cost reduction are achieved.

The invention relates to the technical field of defect repairing, in particular to a defect repairing method, device and equipment for hot end components of a gas turbine, a storage medium and a product. Carrying out synchronous registration on the surface image and the temperature field distribution data; inputting the surface image and the temperature field distribution data after synchronous registration into a trained defect detection model for defect detection to obtain the defect type of each hot end component; and performing defect repair on each hot end component according to a defect repair strategy corresponding to the defect type, and identifying possible defects in the hot end component through a surface image of the hot end component and temperature field distribution data in multiple dimensions so as to determine a specific damage type in the hot end component. And corresponding defect repair strategies are selected based on different types of defects to perform defect repair on each hot end component, so that the defect repair effect is improved in a targeted manner.

The invention discloses a two-dimensional code recognition and defect repair method and device, and the method achieves the effective integration of visible light, infrared and ultraviolet image features through the collection and preprocessing of a multi-modal image, greatly improves the feature recognition degree of a two-dimensional code under a complex background, and solves a problem that a conventional method is poor in adaptability to the complex background. According to the lightweight convolutional neural network defect detection model, pixel-level defect segmentation and multi-dimensional classification are realized, a defect severity grading mechanism is combined, an accurate basis is provided for intelligent defect evaluation, and the defect of lack of intelligent evaluation in the prior art is made up; for self-adaptive repair strategies of defects of different types and levels, the advantages of multiple algorithms such as texture synthesis, error correction coding and geometric correction are fused, and the repair precision of the two-dimensional code is greatly improved; a closed-loop verification mechanism of repair and recognition realizes linkage optimization of a repair process and a recognition result by dynamically adjusting repair parameters, and the problem of low efficiency caused by separation of repair and recognition is effectively solved.

The application discloses a multi-position defect repairing method based on repairing sequence scheduling and multi-agent cooperation. First, the defect analysis agent is used to identify the code error root cause and generate a defect analysis report. Then, the report, error code, local context, failed test cases and test error information are combined to build a repair context. Then, the multi-agent architecture is used to iteratively generate patches: the coordinator agent analyzes the dependency relationship between code blocks and schedules the repair sequence, the proposer agent generates a local candidate patch, and the coordinator evaluates and selects the optimal patch. Repeat the process until all repairs are completed and a complete patch is assembled. Finally, according to the compilation error and test failure report, the patch is executed in two stages of syntax and semantics refinement. The application overcomes the defects of the existing large model repairing method, such as lack of sequence scheduling and difficulty in handling code block dependencies, and realizes efficient repair of complex multi-position defects.

The invention discloses a defect repairing device and method for a metal component, and the device comprises a closed container which comprises a cabin body, a cabin door, an insulating cylinder, and an insulating storage platform; the bin door and the bin body are opened and closed to form a sealed cavity; the insulating cylinder is arranged in the sealing cavity, and an opening in one side faces the bin door; the insulating object placing table is arranged in the insulating cylinder and is used for placing a metal component; the gas pressurization system is located outside the closed container, communicates with the sealing cavity and is used for providing pressurized gas, so that the interior of the sealing cavity is pressurized to preset process pressure; the pulse current system comprises a pulse power supply and a current applying part, the pulse power supply is located outside the closed container, the current applying part is located in the insulating cylinder, the pulse power supply is conductively connected with the current applying part through a wire, and the current applying part is used for making contact with the metal component and applying pulse current. The application of the electric pulse treatment technology in defect repair of the metal component is realized, and the metallurgical-grade repair of the defects of the metal component is efficiently, quickly and accurately realized.

The present application relates to the technical field of patching material mix ratio determination, and particularly relates to an underwater patching material mix ratio determination method. A plurality of initial underwater patching material mix ratios are obtained; initial compressive strength, initial splitting tensile strength and initial fluidity corresponding to each initial underwater patching material mix ratio are determined; a multi-objective fitness function is constructed based on the initial compressive strength, the initial splitting tensile strength and the initial fluidity corresponding to each initial underwater patching material mix ratio; and each initial underwater patching material mix ratio is optimized based on the multi-objective fitness function to obtain a target underwater patching material mix ratio. The traditional trial-and-error test method is replaced, the test workload is greatly reduced, and the mix ratio research and optimization efficiency is improved. The engineering adaptability and application reliability of the target underwater patching material mix ratio are ensured, and the core engineering goals of the underwater grouting repair, defect repair and bearing capacity recovery are achieved.

The utility model relates to the technical field of stamping production devices, in particular to a stamping part surface defect repairing device which comprises a control system and a repairing device body and further comprises a self-adaptive clamp, the self-adaptive clamp comprises a base plate, sliding blocks, vacuum suction cups and a locking assembly, the base plate is provided with sliding grooves in a honeycomb array, the sliding blocks are arranged in the sliding grooves in a sliding mode, and the vacuum suction cups are arranged in the sliding grooves. A spring is arranged between the sliding block and the ground surface of the sliding groove, the upper end of the sliding block is connected with a vacuum chuck through a ball-and-socket joint, an annular groove is formed in the side face of the sliding block, an elastic sealing ring is arranged in the annular groove, and a communicating hole is formed in the ground of the annular groove and communicated with an external high-pressure air source through a cavity formed in the sliding block. The spring is flexibly matched with the curved surface of the workpiece, then high-pressure gas acts to drive the locking assembly to act to position the sliding block, and finally the sliding block and a stamping part are connected through the vacuum suction cup, so that the rigid-flexible contradiction of a traditional clamp is solved, the curved-surface workpiece is effectively fixed, and meanwhile the surface of the workpiece is prevented from being damaged.

The invention discloses an additive process parameter planning method and device for defect repair welding. The method comprises the following steps: acquiring point cloud information of a repair welding position of a target workpiece and a pre-planned automatic repair welding path; and based on the automatic repair welding path, generating a segmentation surface passing through the path point, and based on the point cloud information, recovering the lost step format digital model by using a reverse reconstruction method, so that the generated segmentation surface is used for segmented cutting, and then the welding process parameters are dynamically matched according to each cut sub-model. According to the method, the complicated pretreatment simulation and training process is avoided, the process response speed is remarkably increased, good adaptability is achieved, and variable, non-standard and high-complexity repair welding objects in remanufacturing can be effectively handled.

This invention provides a method, system, device, and storage medium for automatic panel defect repair, relating to the field of automatic panel defect repair technology. The method comprises: using a pre-trained defect detection model to detect defects in a panel image to obtain defect target boxes; performing periodic matching and periodic migration of the panel image based on a preset matching template to locate repair anchor boxes in the panel image; performing intersection calculation and threshold verification on the defect target boxes and repair anchor boxes in the panel image to obtain repair boxes; automatically repairing panel defects based on the repair boxes and acquiring a panel defect repair image; and using a pre-trained defect repair effect detection model to detect defect repair in the panel defect repair image to obtain defect repair detection results. This invention utilizes deep learning technology and computer vision technology for defect detection, defect repair, and defect repair effect detection, meeting the needs of panel manufacturers for automatic defect detection and repair.

The application discloses a defect code generation method and related device, and the method comprises the steps of: obtaining at least one code segment pair from a code library; analyzing the modification reason between the first code segment before modification and the second code segment after modification in each code segment pair through a first language model, and identifying the defect type of the first code segment in each code segment pair indicating defect repair; obtaining at least one code segment pair corresponding to each defect type as a target code segment pair, and calling a second language model to learn each target code segment pair with the generation of defect code as a learning target to obtain a learned second language model; and generating a defect code segment corresponding to the target code segment through the learned second language model. The method can realize the automatic generation of defect code, improve the generation efficiency of defect code, improve the generation quality of defect code, and enhance the comprehensiveness and authenticity of the generated defect code.

This invention relates to the field of surface defect detection technology, specifically to a vision-based scanning surface defect detection device for oil drill pipes. It includes a drive unit, a displacement assembly, a laser probe, and a control feedback module. This solution uses a clamping assembly to fix the drill pipe's position and provide position status feedback. A laser receiver and laser emitter interact to detect and capture abnormal positional distributions of the drill pipe, avoiding the influence of irrelevant defects or abnormal placement on the surface defect detection. Simultaneously, the displacement assembly and drive unit perform defect detection at different locations on the drill pipe's outer surface, achieving comprehensive defect detection. The control feedback module collects positional parameters of the detection area in real time, constructing a location-based defect re-inspection route for irregularly placed drill pipes, precisely capturing the repair status of each defect repair point, avoiding repeated position corrections, and improving re-inspection efficiency.

The application provides a kind of for printed wiring board product defect automatic repair equipment, belong to the technical field of circuit board defect automatic repair equipment, the for printed wiring board product defect automatic repair equipment includes marble base plate;Mounting plate, mounting plate is located on the upside of marble base plate by Y-axis moving mechanism, the movement of mounting plate is controlled by Y-axis moving mechanism;U-shaped frame, U-shaped frame is fixedly connected to the upper end of marble base plate;Sliding plate, sliding plate is located on the side of U-shaped frame by X-axis moving mechanism, by the device, the defect of circuit board surface is positioned by CCD defect detector when repairing the printed circuit board with defect, improve the precision of circuit board defect repair, the repair effect of circuit board is detected by CCD defect detector after being milled by milling cutter to repair the surface of circuit board, the surface of the circuit board that is repaired does not exist defect, guarantee the precision of circuit board repair, improve the repair efficiency of printed wiring board.

The utility model discloses a defect repairing device for a baked porcelain restoration, which belongs to the field of baked porcelain restoration and comprises a handle, an anti-slip sleeve is fixedly sleeved on the outer wall of the handle, an adsorption component is arranged on the side wall of the handle and comprises a sliding seat fixedly connected to the side wall of the handle, and a limiting block is fixedly connected to the side wall of the sliding seat. Through cooperative use of all the devices, in the polishing process of the polishing head, tooth powder generated during polishing can be rapidly adsorbed through cooperation of the suction rod, a suction box, a vacuum generator and a negative pressure pipe and high-speed adsorption force, the powder is adsorbed through a filter plate and an adsorption plate, and the tooth powder is adsorbed through the suction box, the suction box, the vacuum generator and the negative pressure pipe; tooth powder is prevented from remaining at the tooth grinding position to affect the adhesion strength of porcelain repair, the repair efficiency can be improved through adsorption in the grinding process, the cleaning assembly is matched, the grinding efficiency can be improved through mutual cooperation of a flushing box, a nozzle and an air pipe and water flow, and the repair effect is further improved.

The application discloses a monorail track defect detection system and method, and relates to the track detection technical field.The system comprises a vehicle selection module, a data acquisition module, a data judgment module, a secondary acquisition module and a defect division module.The vehicle selection module is used for acquiring vehicle trial operation data, and comparing the trial operation data with a standard threshold interval to judge whether the vehicle meets the requirements.The technical points are as follows: the vehicle is detected before detection, the benchmark of the detection is used as the benchmark for judging whether defects exist on the monorail track, the subsequent judgment of defects is more accurate, small defects can be identified, the defect position is further acquired and analyzed, the content and grade of the defects are analyzed, the corresponding defect repair plan can be formulated according to the defect grade, the data amount processed in the whole process is small, the defect position can be analyzed in real time, and the system has a good application prospect.

One or more embodiments of the present disclosure describe an artificial intelligence assisted substrate defect repair apparatus and method. The AI assisted defect repair apparatus employs an object detection algorithm. Based on the plurality of images taken by detectors located at different respective positions, the detectors capture various views of an object including a defect. The composition information as well as the morphology information (e.g., shape, size, location, height, depth, width, length, or the like) of the defect and the object are obtained based on the plurality of images. The object detection algorithm analyzes the images and determines the type of defect and the recommends a material (e.g., etching gas) and the associated information (e.g., supply time of the etching gas, flow rate of the etching gas, etc.) for fixing the defect.

The invention discloses a dental arch map defect repair method and system. The method comprises the following steps: S1, extracting an edge girdle Band region; s2, performing multi-scale decomposition on the dental arch chartlet to obtain a low-frequency sub-graph Ilow and a high-frequency sub-graph Ihigh; s3, filling a defect area omega of the low-frequency sub-graph Ilow by adopting a gradient driving filling algorithm; s4, repairing the defect area omega of the high-frequency sub-graph Ihigh by adopting a structure constraint matching algorithm; s5, carrying out inverse reconstruction to obtain a complete map Iout; and S6, mapping the complete map Iout back to the 3D dental arch model through UV. According to the method, the repairing color of the dental arch map is smooth, texture details are considered, the texture is natural, and false textures are avoided; natural transition between the defect area and the peripheral area is realized; according to the embodiment, training data and neural network reasoning are not needed, and the method has real-time performance and cross-device adaptability and is suitable for clinical rapid repair.

The invention relates to the technical field of intelligent manufacturing, in particular to a ceramic surface defect repairing method and system. The method comprises the following steps: acquiring a ceramic workpiece scanning image with defects and a trained defect repairing network; inputting a scanned image into the network, and outputting a defect segmentation mask through a defect detection sub-network; based on the mask, fusing the scanned image and the standard three-dimensional model through a three-dimensional reconstruction sub-network, and generating a geometric patch of the defect area and matched texture features; finally, geometric patches and texture features are input into a repair planning sub-network, a repair path and deposition parameters are planned through an optimization algorithm, and a control instruction sequence is generated; according to the invention, full-process intelligent integration of defect detection, three-dimensional reconstruction and repair planning is realized, and the precision, efficiency and consistency of ceramic workpiece repair are improved.