Quanta Computer Incorporated

Wen-Kuang Chen

Chen-Chung Lee

Chun-Hung Chen

Chien-Kuo Hung

En-Chi Lee

Chia-Hung Lin

Tien-Chin Fang

Tsai-Hsing Lu

A process operating system includes a process control platform, a process operation platform and an endpoint task robot. The process control platform is configured to receive operation information, extract a task from the operation information using a semantic analysis method, and publish the task. The process operation platform is configured to receive the task and store the task in a task queue. After receiving the task, the process operating platform defines a processing flow based on the task, and sorts the order of the task in the task queue. The endpoint task robot is configured to automatically obtain the task from the process operation platform, executes the task according to the processing flow. It then writes the execution result into the log queue and transmits the execution result to the process control platform.

Process operation system and process operation method for automatically creating, publishing and executing a task

A character recognition method includes the stages as detailed in the following paragraph. An image is received, wherein the image is one in a plurality of consecutive images. A target object in the image is detected. Object information of the target object is defined according to the area ratio of the target object occupied in the image. Whether the target object in the image is the same as the target object in the previous image is determined according to the object information. Character recognition on the target object is performed to obtain a recognition result. The weighting score of the recognition result is calculated according to the object information and the recognition result. The weighting score of the recognition result of the target object in the consecutive images is accumulated until the weighting score is higher than a preset value, and the recognition result is output.

Character recognition method for dynamic images

A consumption prediction method includes the following steps: calculating a personal preference correlation coefficient; inputting historical environment data, a historical consumption record and the personal preference correlation coefficient into a first neural network model; a training model is generated by the first neural network model; and determining whether the accuracy rate of the training model is higher than the training threshold. When the accuracy rate of the training model is higher than the training threshold, the training model is regarded as a prediction model.

Consumption prediction system and consumption prediction method

A cloud service system includes a hardware resource, a storage device, and a controller. The hardware resource is installed with a system, and a software container is instantiated for cloud service execution on the hardware resource. The storage device stores a plurality of libraries, each of which is associated with a respective version of the system. The controller determines a first version of the system according to a deployment request, determines whether one of the libraries is associated with the first version, and selects one of the libraries for instantiating the software container according to a selection history of the libraries in response to none of the libraries being associated with the first version.

Systems and methods for cloud service provisioning using software containers

A monitoring management system is provided to perform a monitoring operation including: detecting a system configuration of an application server; determining whether the system configuration is compliant with a monitoring rule corresponding to the application server; generating a monitoring configuration when the system configuration is compliant with the monitoring rule; and allocating at least one monitoring server to monitor the application server according to the monitoring configuration.

Monitoring management systems and methods

A management method of cloud resources is provided for use in a hybrid cloud system with first and second cloud systems, wherein the first cloud system includes first servers operating first virtual machines (VMs) and the second cloud system includes second servers operating second VMs, the method including the step of: collecting, by a resource monitor, performance monitoring data of the first VMs within the first servers; analyzing, by an analysis and determination device, the performance monitoring data collected to automatically send a trigger signal in response to determining that a predetermined trigger condition is met, wherein the trigger signal indicates a deployment target and a deployment type; and automatically performing, by a resource deployment device, an operation corresponding to the deployment type on the deployment target in the second cloud system in response to the trigger signal.

Management systems of cloud resources and management methods thereof

A resource adjustment system and method for virtual machines (VMs) are provided. The method includes: obtaining, by a monitoring module, performance and status information of a first server and at least one VM of the first server; estimating, by an estimating module, the first server and each VM according to the performance and status information and an evaluation index and obtaining a server estimation score of the first server and a virtual machine estimation score of every VM; and finding, by a moving module, a second server according to the performance and status information and the evaluation index when a virtual machine estimation score of at least one first VM of the first server or the server estimation score exceeds a threshold, finding at least one VM suitable to be moved according to a moving order, and moving the at least one VM to the second server.

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Taoyuan, Taiwan

Wen-Kuang Chen