Boe Technology Group Co., Ltd.

Beijing Zhongxiangying Technology Co., Ltd.

Beijing Boe Technology Development Co., Ltd.

Dong Chai

Hong Wang

Zhaoyue Li

Haohan Wu

Yaoping Wang

Meijuan Zhang

Tian Lan

Yuanyuan Lu

Wangqiang He

Yongzhang Liu

Suo Zhang

Xuefeng Kan

Libo Zhang

Qiang Li

Xuewen Lv

Guoliang Shen

Yue Tang

Fei Yuan

Weihe Liu

Nan Liu

Chuan Wang

Jianmin Wu

Haijin Wang

Jianfeng Zeng

Minyang Deng

Lijun Zeng

Junxin Zhao

Xiaohui Zhao

Linzhe Li

A method and a device for processing product manufacturing messages, and an electronic device are disclosed. The method for processing product manufacturing messages includes: monitoring a plurality of product manufacturing messages; establishing a product defect analysis task queue based on the plurality of product manufacturing messages; distributing product defect analysis tasks to product manufacturing assisting devices based on the product defect analysis task queue, wherein the product defect analysis tasks include a task of identifying product defect content based on a defect identification model; wherein the product defect content includes any one or more of: product defect type, product defect location, and product defect size.

Method and device for processing product manufacturing messages, electronic device, and computer-readable storage medium

Provided is a method and device for detecting defect, a computer readable storage medium and an electronic device, the method including: acquiring (S) a detection task, and acquiring various types of images corresponding to the detection task; acquiring (S) defect detection models trained by a same initial model corresponding to the types of the images respectively; and obtaining (S) defect detection results by performing defect detection on respective type of images using the defect detection model corresponding to the type of the images.

Method and device for detecting defect, storage medium and electronic device

The present disclosure provides a detection method. The detection method includes inputting an image to be detected into a detection model being pre-constructed and detecting the image to be detected. The detection model includes a defect classification identification sub-model configured to identify a classification of a defect in the image to be detected, and the defect classification identification sub-model comprises a plurality of base models and a secondary model. The present disclosure further provides an electronic device and a non-transitory computer-readable storage medium.

Detection method, electronic device and non-transitory computer-readable storage medium

An inference computing apparatus comprises at least one processor and a memory with program instructions stored therein, the program instructions can be executed by the at least one processor to cause the inference computing apparatus to perform the following operations: receiving a first inference model from a model training apparatus, the first inference model being obtained through a model training by the model training apparatus based on a first training sample library, the first training sample library comprising training samples from historical data generated in a manufacturing stage; performing an inference computing on data to be processed generated in the manufacturing stage based on the first inference model to obtain the inference result which is sent to a user-side device; and evaluating performance of the first inference model to determine whether the first inference model needs to be updated, and if yes, updating the first inference model.

Inference computing apparatus, model training apparatus, inference computing system

The present disclosure relates to an image data classification method, device and system, and relates to the field of computer technology. The method includes: inputting test image data into a neural network model trained by using an original training sample set for classification, and determining an image type to which the test image data belongs and a membership probability of the image data belonging to the image type; establishing an easy-to-classify data set, according to test image data with a membership probability greater than a first threshold; adding test image data in the easy-to-classify data set that has a classification accuracy rate less than or equal to a second threshold and a correct classification result to the original training sample set to generate an augmented training sample set; and using the augmented training sample set to train the neural network model so as to determine an image class.

Image data classification method, device and system

An image marking method, apparatus and system, which relates to the technical field of image processing. The present disclosure includes, when the working mode of the first client is a first mode, receiving a first marking task assigned by a second client, on the condition that the image marking approach is the first marking approach, according to a neural network model, determining a first marking result corresponding to the first original image; on the condition that the image marking approach is the second marking approach, according to an unsupervised algorithm model, determining a second marking result corresponding to the first original image; on the condition that the image marking approach is the third marking approach, receiving a third marking result inputted by a user into the first original image; and sending a target marking result to the second client.

Image annotation method, device and system

The present disclosure provides a production programming system based on a nonlinear program model, including: a distributed storage device and an analysis device, wherein the analysis device includes a processor configured to obtain production record information; construct the nonlinear program model based on the production record information; and solve the nonlinear program model to obtain first feasible solutions. The nonlinear program model includes a constraint condition that satisfies process requirements and an objective function indicating pressure equilibrium across the same device set, and each of the first feasible solutions is configured to indicate a production program. The present disclosure further provides a production programming method and a computer-readable storage medium which can improve efficiency and reduce device idleness rate.

System and method for producing display panels based on nonlinear program model, and non-transitory computer-readable storage medium

A computer-implemented method for defect analysis is provided. The computer-implemented method includes calculating a plurality of weight-of-evidence (WOE) scores respectively for a plurality of device operations with respect to defects occurred during a fabrication period, a higher WOE score indicating a higher correlation between a defect and a device operation; and ranking the plurality of WOE scores to obtain a list of selected device operations highly correlated with the defects occurred during the fabrication period, device operations in the list of selected device operations having a WOE score greater than a first threshold score. A respective one of the plurality of device operations is a respective device defined by a respective operation site at which the respective device perform a respective operation.

Computer-implemented method for defect analysis, computer-implemented method of evaluating likelihood of defect occurrence, apparatus for defect analysis, computer-program product, and intelligent defect analysis system

A computer-implemented method for defect analysis is provided. The computer-implemented method includes calculating a plurality of weight-of-evidence (WOE) scores respectively for a plurality of device operations with respect to detects occurred during a fabrication period, a higher WOE score indicating a higher correlation between a defect and a device operation; and ranking the plurality of WOE scores to obtain a list of selected device operations highly correlated with the defects occurred during the fabrication period, device operations in the list of selected device operations having a WOE score greater than a first threshold score. A respective one of the plurality of device operations is a respective device defined by a respective operation site at which the respective device perform a respective operation.

The present disclosure relates to methods and apparatuses for processing an image, training an image recognition network and recognizing an image. The method of processing an image includes: obtaining a plurality of original images from an original image set, where at least one of the plurality of original images includes an annotation area; obtaining at least one first image by splicing the plurality of original images; for each of the at least one first image, adjusting a shape and/or size of the first image based on the plurality of original images to form a second image; obtaining respective positions of the at least one annotation area in the second image by converting respective positions of the at least one annotation area in the plurality of original images.

Methods and apparatuses for processing image, methods and apparatuses for training image recognition network and methods and apparatuses for recognizing image

The present disclosure relates to a task processing method and device based on defect detection, a computer readable storage medium, and a task processing apparatus. The method includes receiving a detection task; determining a task type of the detection task; storing the detection task in a task queue if the task type is a target task type; and executing the detection task in a preset order and generating a feedback signal when a processor is idle. The detection task of the target task type includes an inference task and a training task. Executing the training task includes modifying configuration information according to a preset rule based on product information in the detection task; acquiring training data and an initial model according to the product information; and using the training data to train the initial model according to the configuration information to obtain a target model and store it in memory.

Defect detection task processing method, device, apparatus and storage medium

The present disclosure provides a detection device of a display panel. The detection device includes: an image receiver configured to receive a detection image of a display panel to be detected; a detector configured to input the detection image of the display panel to be detected into a detection model and generate a detection result by the detection model, the detection model is pre-constructed and configured to detect the display panel. The disclosure also provides a detection method of the display panel, an electronic device and a computer readable medium.

Detection device of display panel and detection method thereof, electronic device and readable medium

A distributed computing system for product defect analysis is disclosed. The distributed computing system for product defect analysis includes a computing cluster for processing product manufacturing messages, a computing cluster for identifying product defect, a product image database, and a client device.

Distributed computing system for product defect analysis

According to the embodiments of the present disclosure, there is provided a method and device of detecting fault in production, and a computer readable storage medium. The method includes: determining whether a plurality of production paths in a production line are faultless in one or more production batches, based on production record data; and determining at least one of the plurality of production paths to be faulty, at least partially based on whether the plurality of production paths are faultless in the one or more production batches.

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Beijing, China

Dong Chai