WO2018170683A1

WO2018170683A1 - Task allocation method and system for cloud service in monitoring system

Info

Publication number: WO2018170683A1
Application number: PCT/CN2017/077330
Authority: WO
Inventors: 张北江; 胡君健
Original assignee: 华平智慧信息技术（深圳）有限公司
Priority date: 2017-03-20
Filing date: 2017-03-20
Publication date: 2018-09-27

Abstract

Disclosed in the present invention are a task allocation method and system for a cloud service in a monitoring system, the method comprising the following steps: a monitoring system cloud platform receives a first task to be processed; the monitoring system cloud platform calculates the total time for each cloud platform device to process the first task, the total time comprising: waiting time and delivery time; and the monitoring system selects the device having the best total time as the device allocated the first task. The technical solution provided in the present invention has the advantage of high efficiency.

Description

Task assignment method and system for cloud service in monitoring system

Technical field

The present invention relates to the field of monitoring, and in particular, to a task allocation method and system for a cloud service in a monitoring system.

Background technique

The monitoring system consists of 5 parts: camera, transmission, control, display, and record registration. The camera transmits the video image to the control host through the coaxial video cable, and the control host distributes the video signal to each monitor and recording device, and simultaneously records the voice signal to be transmitted into the recorder. By controlling the host, the operator can issue commands to control the up, down, left, and right movements of the pan/tilt and focus the zoom on the lens, and can be implemented in the multi-channel camera and the pan/tilt by the control host. Switch between. With special recording processing mode, images can be recorded, played back, processed, etc., so that the recording effect is optimal.

The cloud service of the existing monitoring system reduces the cost of the monitoring system, but the task allocation method of the existing monitoring system generally allocates tasks based on the load balancing method, which may cause the task processing time to be too long, so the efficiency is low. .

technical problem

The application provides a task allocation method for a cloud service in a monitoring system. It solves the shortcomings of the prior art technical solutions.

Technical solution

In one aspect, a method for task allocation of a cloud service in a monitoring system is provided, the method comprising the following steps:

The monitoring system cloud platform receives the first task to be processed;

The monitoring system cloud platform calculates a total time for each cloud platform device to process the first task, the total time including: waiting time and delivery time;

The monitoring system selects the device with the best total time as the device assigned by the first task.

Optionally, the waiting time is specifically: t1=M1/S1; wherein M1 is the total amount of processing tasks of the device, and S1 is the task amount processed by the device per second.

Optionally, the delivery time is specifically: t2=M2/S2, where M2 is the data volume of the first task, and S2 is a network delay that the cloud platform transmits the first task to the device.

In a second aspect, a task distribution system for monitoring a cloud service in a system is provided, the system comprising:

a receiving unit, configured to receive a first task that needs to be processed;

a calculating unit, configured to calculate a total time for each cloud platform device to process the first task, where the total time includes: a waiting time and a delivery time;

An allocating unit for selecting the device with the best total time as the device assigned by the first task.

In a third aspect, a monitoring system is provided, including: a processor, a wireless transceiver, a memory, and a bus, wherein the processor, the wireless transceiver, and the memory are connected by a bus.

The wireless transceiver is configured to receive a first task that needs to be processed;

The processor is configured to calculate a total time that each cloud platform device processes the first task, where the total time includes: a waiting time and a delivery time; and selecting a device with the best total time as the device assigned by the first task.

Optionally, the processor is specifically configured to calculate a waiting time, specifically: t1=M1/S1; wherein M1 is a total amount of processing tasks of the device, and S1 is a task quantity processed by the device per second.

Optionally, the processor is specifically configured to calculate a transit time, specifically: t2=M2/S2, where M2 is the data volume of the first task, and S2 is a network delay that the cloud platform transmits the first task to the device. .

Beneficial effect

The technical solution provided by the present invention calculates the time of data transmission corresponding to the task and the processing time, so it has the advantage of allocating tasks according to the amount of time and improving the efficiency of task processing.

DRAWINGS

In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly described below. It is obvious that the drawings in the following description are some embodiments of the present invention. Those skilled in the art can also obtain other drawings based on these drawings without paying any creative work.

1 is a flowchart of a task allocation method of a cloud service in a monitoring system according to a first preferred embodiment of the present invention;

2 is a structural diagram of a task allocation system for a cloud service in a monitoring system according to a second preferred embodiment of the present invention.

FIG. 3 is a hardware structural diagram of a monitoring system according to a second preferred embodiment of the present invention.

Embodiments of the invention

The technical solutions in the embodiments of the present invention are clearly and completely described in the following with reference to the accompanying drawings in the embodiments of the present invention. It is obvious that the described embodiments are a part of the embodiments of the present invention, but not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative efforts are within the scope of the present invention.

Please refer to FIG. 1. FIG. 1 is a schematic diagram of a task allocation method for a cloud service in a monitoring system according to a first preferred embodiment of the present invention. The method is as shown in FIG.

Step S101: The monitoring system cloud platform receives the first task to be processed.

Step S102: The monitoring system cloud platform calculates a total time for each cloud platform device to process the first task, where the total time includes: waiting time and delivery time.

Step S103: The monitoring system selects the device with the best total time as the device assigned by the first task.

The technical solution provided by the embodiment of the present invention not only considers the waiting time of the processing task, but also considers the delivery time of the processing task, so it has the advantages of comprehensive consideration and improved efficiency.

Optionally, the foregoing waiting time is specifically: t1=M1/S1; wherein M1 is the total amount of processing tasks of the device, and S1 is the amount of tasks processed by the device per second.

Optionally, the foregoing delivery time may be specifically: t2=M2/S2, where M2 is the data volume of the first task, and S2 is a network delay that the cloud platform transmits the first task to the device.

Please refer to FIG. 2. FIG. 2 is a schematic diagram of a task distribution system for a cloud service in a monitoring system according to a second preferred embodiment of the present invention. The system includes the following:

The receiving unit 201 is configured to receive a first task that needs to be processed.

The calculating unit 202 is configured to calculate a total time for each cloud platform device to process the first task, where the total time includes: a waiting time and a delivery time.

The allocating unit 203 is configured to select the device with the best total time as the device assigned by the first task.

Referring to FIG. 3, FIG. 3 is a monitoring system 30, including: a processor 301, a wireless transceiver 302, a memory 303, and a bus 304. The wireless transceiver 302 is configured to transmit and receive data with and from an external device. The number of processors 301 can be one or more. In some embodiments of the present application, processor 301, memory 302, and transceiver 303 may be connected by bus 304 or other means. Monitoring system 30 can be used to perform the steps of FIG. For the meaning and examples of the terms involved in the embodiment, reference may be made to the corresponding embodiment of FIG. 1. I will not repeat them here.

The wireless transceiver 302 is configured to receive a first task that needs to be processed.

The program code is stored in the memory 303. The processor 901 is configured to call the program code stored in the memory 903 for performing the following operations:

The processor 301 is configured to calculate a total time for each cloud platform device to process the first task, where the total time includes: waiting time and delivery time, and selecting the device with the best total time as the device assigned by the first task.

It should be noted that the processor 301 herein may be a processing component or a general term of multiple processing components. For example, the processing element can be a central processor (Central) Processing Unit, CPU), or a specific integrated circuit (Application Specific Integrated) Circuit, ASIC), or one or more integrated circuits configured to implement embodiments of the present application, such as one or more microprocessors (digital singnal Processor, DSP), or one or more Field Programmable Gate Arrays (FPGAs).

The memory 303 may be a storage device or a collective name of a plurality of storage elements, and is used to store executable program code or parameters, data, and the like required for the application running device to operate. And the memory 303 may include random access memory (RAM), and may also include non-volatile memory (non-volatile memory) Memory), such as disk storage, flash (Flash), etc.

Bus 304 can be an industry standard architecture (Industry Standard Architecture, ISA) bus, Peripheral Component (PCI) bus or extended industry standard architecture (Extended Industry Standard Architecture, EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, and the like. For ease of representation, only one thick line is shown in Figure 3, but it does not mean that there is only one bus or one type of bus.

The terminal may further include input and output means connected to the bus 304 for connection to other parts such as the processor 301 via the bus. The input/output device can provide an input interface for the operator, so that the operator can select the control item through the input interface, and can also be other interfaces through which other devices can be externally connected.

It should be noted that, for the foregoing various method embodiments, for the sake of simple description, they are all expressed as a series of action combinations, but those skilled in the art should understand that the present invention is not limited by the described action sequence. Because certain steps may be performed in other sequences or concurrently in accordance with the present invention. In addition, those skilled in the art should also understand that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present invention.

In the above embodiments, the descriptions of the various embodiments are different, and the parts that are not described in detail in a certain embodiment can be referred to the related descriptions of other embodiments.

A person skilled in the art may understand that all or part of the various steps of the foregoing embodiments may be performed by a program to instruct related hardware. The program may be stored in a computer readable storage medium, and the storage medium may include: Flash drive, read-only memory (English: Read-Only Memory, referred to as: ROM), random accessor (English: Random Access Memory, referred to as: RAM), disk or CD.

The content downloading method and the related device and system provided by the embodiments of the present invention are described in detail above. The principles and implementation manners of the present invention are described in the specific examples. The description of the above embodiments is only used to help understand the present invention. The method of the invention and its core idea; at the same time, for the person of ordinary skill in the art, according to the idea of the present invention, there are some changes in the specific embodiment and the scope of application. In summary, the content of the specification should not be understood. To limit the invention.

Claims

A task allocation method for a cloud service in a monitoring system, characterized in that the method comprises the following steps:

The monitoring system cloud platform receives the first task to be processed;

The monitoring system cloud platform calculates a total time for each cloud platform device to process the first task, the total time including: waiting time and delivery time;

The monitoring system selects the device with the best total time as the device assigned by the first task.
The method according to claim 1, wherein the waiting time is specifically: t1=M1/S1; wherein M1 is the total amount of processing tasks of the device, and S1 is the amount of tasks processed by the device per second.
The method according to claim 1, wherein the delivery time is specifically: t2=M2/S2, wherein M2 is the data volume of the first task, and S2 is a network extension that the cloud platform transmits the first task to the device. Time.
A task distribution system for a cloud service in a monitoring system, characterized in that the system comprises:

a receiving unit, configured to receive a first task that needs to be processed;

a calculating unit, configured to calculate a total time for each cloud platform device to process the first task, where the total time includes: a waiting time and a delivery time;

An allocating unit for selecting the device with the best total time as the device assigned by the first task.
The method according to claim 4, wherein the waiting time is specifically: t1=M1/S1; wherein M1 is the total amount of processing tasks of the device, and S1 is the amount of tasks processed by the device per second.
The method according to claim 4, wherein the delivery time is specifically: t2=M2/S2, wherein M2 is the data volume of the first task, and S2 is the network extension of the cloud platform transmitting the first task to the device. Time.
A monitoring system includes: a processor, a wireless transceiver, a memory, and a bus, wherein the processor, the wireless transceiver, and the memory are connected by a bus, wherein

The wireless transceiver is configured to receive a first task that needs to be processed;

The processor is configured to calculate a total time that each cloud platform device processes the first task, where the total time includes: a waiting time and a delivery time; and selecting a device with the best total time as the device assigned by the first task.
The monitoring system according to claim 7, wherein the processor is specifically configured to calculate a waiting time, specifically: t1=M1/S1; wherein M1 is the total amount of processing tasks of the device, and S1 is the device. The amount of tasks processed per second.
The monitoring system according to claim 7, wherein the processor is specifically configured to calculate a delivery time, specifically: t2=M2/S2, wherein M2 is the data volume of the first task, and S2 is the cloud platform. The network delay of the first task delivered to the device.