Abstract: 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.

Abstract: The present disclosure relates to an image recognition method and system for a display panel, a training method, and an electronic device and a non-volatile computer-readable storage medium. The image recognition method includes: acquiring an image of a display panel, wherein the image includes gate lines extending in a first direction and data lines extending in a second direction, the gate lines and the data lines intersecting to define a plurality of sub-pixel regions, and the image further includes a defect pattern; and recognizing the defect pattern in the image by using an image recognition model to obtain defect information, wherein the defect information includes at least one of a defect type or a defect position of the defect pattern, the image recognition model comprises a first attention model configured to learn a weight proportion of a feature of the defect pattern in the image.

Abstract: 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.

Abstract: 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.

Abstract: Provided is a method and device for detecting defect, a computer readable storage medium and an electronic device, the method including: acquiring (S310) a detection task, and acquiring various types of images corresponding to the detection task; acquiring (S320) defect detection models trained by a same initial model corresponding to the types of the images respectively; and obtaining (S330) defect detection results by performing defect detection on respective type of images using the defect detection model corresponding to the type of the images.

Abstract: 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.

Abstract: The present application discloses a method and apparatus for device configuration parameter processing, a method and apparatus for data analysis, a computing device, a computer-readable storage medium and a computer program product. The method for device configuration parameter processing includes: acquiring collection data of a collection device located at a target position, wherein the collection data is configured to be input into a content analysis model for the target position so as to obtain a content analysis result of the collection data, determining, based on the collection data, whether a current configuration parameter of the collection device has changed, sending, in response to the current configuration parameter of the collection device having changed, a preset configuration parameter for the target position to the collection device so as to adjust the current configuration parameter of the collection device based on the preset configuration parameter.

Abstract: 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.

Abstract: A method and device for detecting a defect and method for training a model are provided. The method for detecting the defect includes: acquiring a sample data set and identifying feature information of the sample data set; acquiring an initial model; configuring a training parameter based on the feature information; obtaining a target model by training, according to the training parameter, the initial model with the sample data set; and obtaining defect information of a product by inputting real data of the product into the target model. The training parameter includes at least one of a learning rate descent strategy, a total number of training rounds and a test strategy, the learning rate descent strategy includes a number of learning rate descents and a round number when a learning rate descends, and the test strategy includes a number of tests and a round number when testing.

Abstract: 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.

Abstract: 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.

Abstract: The present disclosure provides a method and device for detecting an image category. The method includes: acquiring a sample data set including a plurality of sample images labeled with a category, the sample data set including a training data set and a verification data set; training a deep learning model using the training data set to obtain, according to different numbers of training rounds, at least two trained models; testing the at least two trained models using the verification data set to generate a verification test result; generating, based on the verification test result, a verification test index; determining, according to the verification test index, a target model from the at least two trained models; and predict a to-be-tested image of the target object using the target model to obtain the category of the to-be-tested image.

Abstract: 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.

Abstract: 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.

Abstract: A system and method for workflow orchestration for use with a cloud computing environment. Cloud environments, such as Oracle Public Cloud (OPC), provide a suite of applications, middleware, and database offerings that can be delivered to tenants in a self-service, elastically scalable, and secure manner. In accordance with an embodiment, the cloud environment can include a Platform as a Service (PaaS) environment, which provides a variety of services such as virtual assembly creation. A workflow orchestrator can be used to orchestrate operations between the cloud environment and the PaaS environment, e.g., by receiving a request from a tenant automation system, and coordinating the provisioning and deployment of virtual assemblies or applications. A customer can interact with the PaaS environment, e.g., to request a service, deploy to the service, or monitor the service.

Abstract: A system and method for workflow orchestration for use with a cloud computing environment. Cloud environments, such as Oracle Public Cloud (OPC), provide a suite of applications, middleware, and database offerings that can be delivered to tenants in a self-service, elastically scalable, and secure manner. In accordance with an embodiment, the cloud environment can include a Platform as a Service (PaaS) environment, which provides a variety of services such as virtual assembly creation. A workflow orchestrator can be used to orchestrate operations between the cloud environment and the PaaS environment, e.g., by receiving a request from a tenant automation system, and coordinating the provisioning and deployment of virtual assemblies or applications. A customer can interact with the PaaS environment, e.g., to request a service, deploy to the service, or monitor the service.

Abstract: A system and method for workflow orchestration for use with a cloud computing environment. Cloud environments, such as Oracle Public Cloud (OPC), provide a suite of applications, middleware, and database offerings that can be delivered to tenants in a self-service, elastically scalable, and secure manner. In accordance with an embodiment, the cloud environment can include a Platform as a Service (PaaS) environment, which provides a variety of services such as virtual assembly creation. A workflow orchestrator can be used to orchestrate operations between the cloud environment and the PaaS environment, e.g., by receiving a request from a tenant automation system, and coordinating the provisioning and deployment of virtual assemblies or applications. A customer can interact with the PaaS environment, e.g., to request a service, deploy to the service, or monitor the service.

Abstract: A system and method for workflow orchestration for use with a cloud computing environment. Cloud environments, such as Oracle Public Cloud (OPC), provide a suite of applications, middleware, and database offerings that can be delivered to tenants in a self-service, elastically scalable, and secure manner. In accordance with an embodiment, the cloud environment can include a Platform as a Service (PaaS) environment, which provides a variety of services such as virtual assembly creation. A workflow orchestrator can be used to orchestrate operations between the cloud environment and the PaaS environment, e.g., by receiving a request from a tenant automation system, and coordinating the provisioning and deployment of virtual assemblies or applications. A customer can interact with the PaaS environment, e.g., to request a service, deploy to the service, or monitor the service.

Abstract: Instructions are generated to manipulate target objects and relationships in a relational database when a source object having a one-to-many relationship of privately owned type with the target objects is manipulated. To generate instructions, mapping meta-data is used which contains information as to how object classes of the object model map to tables in the database and how relationships map to foreign keys.

Abstract: Instructions are generated to manipulate target objects and relationships in a relational database when a source object having a one-to-many relationship of privately owned type with the target objects is manipulated. To generate instructions, mapping meta-data is used which contains information as to how object classes of the object model map to tables in the database and how relationships map to foreign keys.