A video scheduling engine device and method based on distributed management

Abstract

The present invention provides a video dispatching engine device and method based on distributed management. The device includes: a task parsing module, which is used to obtain the parameters of the storage task opening request and the parameters of the camera that needs to be opened for the task; determine the minimum abstraction of the camera The frame period stores the minimum frame drawing period that meets the frame drawing requirements of all tasks; the task distribution module is used to submit the cameras that are mounted with tasks but not turned on to the camera management module for management in the form of a message queue; the camera management module, It is used for distributed management of the cameras submitted by the task management module; the camera execution module is used to enable the camera stream to draw frames, and distribute pictures to the business system queue that processes the task according to the execution cycle and routing parameters of different tasks of the camera, and at the same time Report exception information to the camera management module. The invention can realize video scheduling in which resources are utilized more efficiently.

Description

technical field [0001] The present invention relates to the technical field of video scheduling, and in particular, to a video scheduling engine device and method based on distributed management. Background technique [0002] At present, the general video scheduling engine mainly adopts the following scheme: configure fixed parameters for each camera, and after the camera task is started, the camera sends the video stream to the business system that needs to be processed according to the parameters. The above-mentioned existing solutions have the following deficiencies in practical application: 1) Since the camera sends the video stream to each service system according to the pre-configured fixed parameters, it will cause the same video stream to be pulled repeatedly by different service systems. The network bandwidth resources (IO) of the server cause great waste; 2) Since the parameters of the camera are pre-configured, it cannot be dynamically adjusted during application....

AI Technical Summary

Problems solved by technology

The above existing solutions have the following deficiencies in practical application: 1) Since the camera sends the video stream to each business system according to the pre-configured fixed parameters, it will cause the same video stream to be repeatedly pulled by different business systems. The network bandwidth resources (IO) of the server cause a large waste; 2) Since the parameters of the camera are pre-configured, they cannot be dynamically adjusted during application. When new services are added to the camera, the system needs to turn off all tasks and It is not efficient to reconfigure the updated parameters and then restart; 3) The video scheduling engine is strongly coupled with the business system, and needs to be adapted between different business systems; 4) It is impossible to implement different business systems and different cameras. Stream priority processing; 5) It is impossible to dynamically adjust system resources according to the number of tasks and the number of cameras

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