CN112433852B - Internet of things edge calculation control method, device, equipment and storage medium - Google Patents
Internet of things edge calculation control method, device, equipment and storage medium Download PDFInfo
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Abstract
The embodiment of the application discloses a method, a device, equipment and a storage medium for controlling the calculation of the edge of an Internet of things. The technical solution provided by the embodiment of the present application compares the computing resource requirement of the graphics processing request sent by the user terminal with the local computing resource carrying capacity, when the local computing resource carrying capacity can meet the computing resource demand, the graphics processing request is processed locally, when the computing resource demand exceeds the local computing resource bearing capacity, distributing the subtasks to the processing terminals of other nodes of the internet of things in the same internet of things to obtain the subtask processing result information returned by each processing terminal, and the task result information is obtained according to the subtask processing result information, and the task result information is returned to the user terminal, so that the quick and effective response to the graphic processing request is realized, the edge computing capability of graphic processing is provided, the problems of graphic processing failure or overlong processing time caused by insufficient local computing resources are reduced, and the user experience is effectively optimized.
Description
Technical Field
The embodiment of the application relates to the technical field of Internet of things, in particular to a method, a device, equipment and a storage medium for controlling the edge calculation of the Internet of things.
Background
Along with the cross promotion of computer technology and related subjects, computer graphics are more and more widely applied in various fields, the requirements of people on the graphic processing effect are gradually improved, and the requirements on the computing capacity of a computer are higher and higher. However, the computational resources of the computer are limited, and when the computational resources required for graphics processing exceed the computational resources that the computer can provide, the problems that the graphics processing request cannot be responded to or the graphics processing time is too long easily occur, and the user experience is seriously affected.
Disclosure of Invention
The embodiment of the application provides a method, a device, equipment and a storage medium for controlling the edge computing of an Internet of things, so as to provide the edge computing capability for graphic processing and optimize user experience.
In a first aspect, an embodiment of the present application provides an edge calculation control method for an internet of things, including:
determining a computing resource requirement based on a graph processing request of a user terminal, wherein the user terminal is connected to an Internet of things network;
if the computing resource demand does not exceed the local computing resource bearing capacity, responding to the graph processing request based on local computing resources to obtain task result information, otherwise, performing task division according to the graph processing request to obtain a plurality of subtasks, and determining a plurality of processing terminals with graph processing capacity in the same Internet of things network;
acquiring transmission effect parameters of a plurality of processing terminals and available computing resources of each processing terminal, distributing subtasks to the plurality of processing terminals based on the transmission effect parameters and the available computing resources, and processing the subtasks by the plurality of processing terminals;
and acquiring subtask processing result information returned by the plurality of processing terminals, acquiring task result information based on the plurality of subtask processing result information, and sending the task result information to the user terminal.
Further, the determining the computing resource requirement based on the graphic processing request of the user terminal comprises:
responding to a graphic processing request sent by a user terminal, and determining a task type and a data volume to be processed corresponding to the graphic processing request, wherein the task type comprises a plurality of subentry task types;
determining a computing resource requirement based on the task type and the amount of data to be processed.
Further, the determining the computing resource requirement based on the task type and the amount of the data to be processed includes:
determining the type proportion of each subentry task type in the task types;
and inputting the type ratio and the data volume to be processed into a task resource mapping model according to the task resource mapping model, and outputting the required computing resource requirement by the task resource mapping model.
Further, the determining the computing resource requirement based on the task type and the amount of the data to be processed includes:
determining the type proportion of each subentry task type in the task types;
and determining the type ratio and the computing resource requirement corresponding to the data volume to be processed according to a task resource mapping table.
Further, the dividing the task according to the graphics processing request to obtain a plurality of subtasks includes:
and dividing the tasks according to the graph processing request to obtain a plurality of subtasks corresponding to different itemized task types.
Further, the distributing the subtasks to the plurality of processing terminals based on the transmission effect parameter and the available computing resource includes:
determining the processing efficiency information of the available computing resources on different itemized task types;
calculating a processing power score based on the transmission effectiveness parameter and the processing efficiency information;
distributing the subtasks to the plurality of processing terminals based on the processing capability scores.
Further, the distributing the subtasks to the plurality of processing terminals based on the processing capability scores includes:
for each subtask, distributing to a plurality of the processing terminals based on the processing capability scores;
the obtaining of task result information based on a plurality of the subtask processing result information includes:
determining a final subtask processing result of each subtask based on a consensus mechanism for a plurality of subtask processing result information corresponding to each subtask;
and determining task result information according to the final subtask processing result of each subtask.
In a second aspect, an embodiment of the present application provides an edge computing control device for an internet of things, including a resource computing module, a local processing module, a task dividing module, a task distributing module, and a result feedback module, where:
the resource calculation module is used for determining the calculation resource requirement based on the graphic processing request of a user terminal, and the user terminal is connected to the Internet of things network;
the local processing module is used for responding the graphic processing request based on the local computing resource to obtain task result information when the computing resource demand does not exceed the bearing capacity of the local computing resource;
the task dividing module is used for dividing tasks according to the graph processing request to obtain a plurality of subtasks and determining a plurality of processing terminals with graph processing capacity in the same Internet of things network when the computing resource demand exceeds the local computing resource bearing capacity;
the task distribution module is used for acquiring transmission effect parameters of a plurality of processing terminals and available computing resources of each processing terminal, distributing subtasks to the processing terminals based on the transmission effect parameters and the available computing resources, and processing the subtasks by the processing terminals;
and the result feedback module is used for acquiring the subtask processing result information returned by the plurality of processing terminals, acquiring task result information based on the plurality of subtask processing result information, and sending the task result information to the user terminal.
In a third aspect, an embodiment of the present application provides a computer device, including: a memory and one or more processors;
the memory for storing one or more programs;
when the one or more programs are executed by the one or more processors, the one or more processors implement the method for controlling computation of an edge of an internet of things according to the first aspect.
In a fourth aspect, embodiments of the present application provide a storage medium containing computer-executable instructions, which when executed by a computer processor, are configured to perform the method for controlling an edge computing of an internet of things according to the first aspect.
According to the embodiment of the application, the computing resource requirement of the graph processing request sent by the user terminal is compared with the local computing resource bearing capacity, when the local computing resource bearing capacity can meet the computing resource requirement, the graph processing request is processed locally, and when the computing resource requirement exceeds the local computing resource bearing capacity, subtasks are distributed to the processing terminals of other nodes of the Internet of things in the same Internet of things, subtask processing result information returned by each processing terminal is obtained, task result information is obtained according to the subtask processing result information, and the task result information is returned to the user terminal, so that quick and effective response to the graph processing request is achieved, the edge computing capacity of graph processing is provided, the problems of graph processing failure or overlong processing time caused by insufficient local computing resources are solved, and user experience is optimized effectively.
Drawings
Fig. 1 is a flowchart of an edge calculation control method for an internet of things according to an embodiment of the present disclosure;
fig. 2 is a flowchart of another method for controlling computation of an edge of an internet of things according to an embodiment of the present application;
fig. 3 is a schematic structural diagram of an edge computing control device of the internet of things according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of a computer device according to an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, specific embodiments of the present application will be described in detail with reference to the accompanying drawings. It is to be understood that the specific embodiments described herein are merely illustrative of the application and are not limiting of the application. It should be further noted that, for the convenience of description, only some but not all of the relevant portions of the present application are shown in the drawings. Before discussing exemplary embodiments in more detail, it should be noted that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although a flowchart may describe the operations (or steps) as a sequential process, many of the operations can be performed in parallel, concurrently or simultaneously. In addition, the order of the operations may be re-arranged. The process may be terminated when its operations are completed, but may have additional steps not included in the figure. The processes may correspond to methods, functions, procedures, subroutines, and the like.
Fig. 1 is a flowchart of an edge calculation control method for an internet of things according to an embodiment of the present disclosure, where the edge calculation control method for an internet of things according to the embodiment of the present disclosure may be executed by an edge calculation control device for an internet of things (e.g., an internet of things base station), and the edge calculation control device for an internet of things may be implemented in a hardware and/or software manner and integrated in a computer device.
The following description will be given by taking an example of the method for the edge computing control device of the internet of things to execute the edge computing control method of the internet of things. Referring to fig. 1, the method for controlling the edge calculation of the internet of things includes:
s101: computing resource requirements are determined based on a graph processing request of a user terminal, and the user terminal is connected to an Internet of things network.
The user terminal is connected to the internet of things network, an internet of things edge computing control device is arranged in each internet of things node of the internet of things network and serves as an internet of things base station (gateway), and the user terminal is in communication connection with the internet of things edge computing control device in a wired and/or wireless mode, so that the user terminal is accessed to the internet of things network. It can be understood that a plurality of internet of things edge computing control devices may be arranged in the internet of things network, and the internet of things edge computing control devices are in communication connection in a wired and/or wireless manner, one internet of things edge computing control device is connected with a plurality of computer devices, and when one of the computer devices serves as a user terminal to send a graphic processing request, other computer devices in the internet of things network will serve as processing terminals. In one possible embodiment, the nodes in the internet of things network communicate based on a DDS (Data Distribution Service for Real-Time Systems, Real-Time system oriented Data Distribution Service) protocol, so as to realize distributed high-reliability Real-Time transmission device Data communication.
Illustratively, when a graphics processing task required to be executed by the user terminal exceeds own computing resource or the expected execution time exceeds a set time threshold, the user terminal sends a graphics processing request to the edge computing control device of the internet of things.
Further, upon receiving a graphics processing request, computing resource requirements are determined based on the graphics processing request. For example, the computer resource requirement of the current graphics processing request is estimated according to the task type, the data volume and the historical processing resource use condition corresponding to the graphics processing request.
S102: and if the computing resource demand does not exceed the local computing resource bearing capacity, responding the graph processing request based on the local computing resource to obtain task result information, otherwise, performing task division according to the graph processing request to obtain a plurality of subtasks, and determining a plurality of processing terminals with graph processing capacity in the same Internet of things network.
For example, after determining a computing resource requirement corresponding to the graphics processing request, comparing the computing resource requirement with a local computing resource currently available to the edge computing control device of the internet of things, and when the computing resource requirement is smaller than the local computing resource, considering that the computing resource requirement does not exceed a local computing resource carrying capacity, processing the graphics processing task based on the local computing resource of the edge computing control device of the internet of things to obtain task result information in response to the graphics processing request. After the task result information is obtained, the task result information can be sent to the corresponding user terminal, the user terminal can quickly obtain the task result information of the corresponding graphic processing task so as to carry out the next work, the waiting time of graphic processing is reduced or the condition of task processing failure occurs, and the response step of the current graphic processing request is finished.
And when the computing resource demand is greater than or equal to the local computing resource, the computing resource demand is considered to exceed the local computing resource bearing capacity, and the task division is carried out on the graphic processing task according to the graphic processing request to obtain a plurality of subtasks.
Further, each processing terminal with the graphic processing capability in the current Internet of things network is determined. The determination of whether the processing terminal has the graphics processing capability may be to send a computing capability obtaining instruction to each processing terminal, and the processing terminal feeds back the processing capability of the processing terminal itself to the internet of things edge computing control device or feeds back whether the processing terminal has the graphics processing capability, or may be to record a processing capability record table of each processing terminal in the internet of things edge computing control device, and may determine a plurality of processing terminals having the graphics processing capability according to the processing capability record table.
S103: the method comprises the steps of obtaining transmission effect parameters of a plurality of processing terminals and available computing resources of the processing terminals, distributing subtasks to the processing terminals based on the transmission effect parameters and the available computing resources, and processing the subtasks by the processing terminals.
For example, after determining a plurality of processing terminals with graphics processing capability in the same internet of things network, the transmission effect parameters of the processing terminals and the available computing resources of the processing terminals are obtained. The transmission effect parameter may be one or more of Reference Signal Received Power (RSRP), Signal to Interference and Noise Ratio (SINR), Reference Signal Receiving Quality (RSRQ), and Rank Indication (RI). The communication quality information is obtained by the processing terminal based on the channel quality measurement, and is uploaded by the processing terminal.
Furthermore, a plurality of processing terminals are determined, the transmission effect parameters of which meet the set effect requirements and the available computing resources of which both meet the subtask resource requirements, and the subtasks are distributed to the processing terminals, and the plurality of processing terminals process the distributed subtasks respectively to obtain subtask processing result information.
S104: and acquiring subtask processing result information returned by the plurality of processing terminals, acquiring task result information based on the plurality of subtask processing result information, and sending the task result information to the user terminal.
Illustratively, after obtaining the subtask processing result information, the processing terminal returns the subtask processing result information to the edge computing control device of the internet of things. And after receiving the subtask processing result information corresponding to each subtask, the IOT edge calculation control device determines the task result information corresponding to the whole graphic processing task according to the subtask processing result information and sends the task result information to the user terminal.
By comparing the computing resource requirement of the graph processing request sent by the user terminal with the local computing resource bearing capacity, when the local computing resource bearing capacity can meet the computing resource requirement, the graph processing request is processed locally, and when the computing resource requirement exceeds the local computing resource bearing capacity, subtasks are distributed to the processing terminals of other nodes of the internet of things in the same internet of things, subtask processing result information returned by each processing terminal is obtained, task result information is obtained according to the subtask processing result information, and the task result information is returned to the user terminal, so that quick and effective response to the graph processing request is realized, the edge computing capacity for graph processing is provided, the problems of graph processing failure or overlong processing time caused by insufficient local computing resources are reduced, and the user experience is optimized effectively.
On the basis of the foregoing embodiment, fig. 2 is a flowchart of another method for controlling edge calculation of an internet of things according to an embodiment of the present application, where the method for controlling edge calculation of an internet of things is an embodiment of the method for controlling edge calculation of an internet of things. Referring to fig. 2, the method for controlling the edge calculation of the internet of things includes:
s201: responding to a graphic processing request sent by a user terminal, and determining a task type and a data volume to be processed corresponding to the graphic processing request, wherein the task type comprises a plurality of subentry task types.
Specifically, when a user terminal needs to process a graphics processing task by using the edge computing capability of the internet of things network, a graphics processing request is generated according to a task type of the graphics processing task and a data volume to be processed, the task type includes a plurality of sub-task types (for example, sub-task types corresponding to different data processing types, data processing modes, algorithm types or processing tools), and when the graphics processing request sent by the user terminal is received, the graphics processing request is analyzed to obtain a carried task type and a data volume to be processed. It is understood that a task type may contain a plurality of identical itemized task types.
S202: determining a computing resource requirement based on the task type and the amount of data to be processed.
Specifically, after determining the task type and the amount of data to be processed corresponding to the graphics processing request, the computing resource requirement of the graphics processing request is further determined according to the task type and the amount of data to be processed.
In one possible embodiment, the determination of computing resource requirements includes steps S2021-S2022:
s2021: and determining the type proportion of each subentry task type in the task types.
S2022: and inputting the type ratio and the data volume to be processed into a task resource mapping model according to the task resource mapping model, and outputting the required computing resource requirement by the task resource mapping model.
Specifically, the type ratio of each type of the itemized task types in the task types is calculated, the type ratio and the data volume to be processed are input into the task resource mapping model, and the task resource mapping model analyzes and processes the type ratio and the data volume to be processed and outputs the corresponding required computing resource requirements.
The task resource mapping model is built based on a neural learning network, type proportion, data volume to be processed and corresponding computing resource requirements are obtained based on experimental data and historical experience data, a training set is built based on the type proportion, the data volume to be processed and the corresponding computing resource requirements, the type proportion and the data volume to be processed in the training set are used as input, the corresponding computing resource requirements are used as output to train the task resource mapping model, and the task resource mapping model with the accuracy rate meeting the set requirements is obtained.
In one possible embodiment, the determination of computing resource requirements includes steps S2023-S2024:
s2023: and determining the type proportion of each subentry task type in the task types.
S2024: and determining the type ratio and the computing resource requirement corresponding to the data volume to be processed according to a task resource mapping table.
Specifically, the type ratio of each type of the itemized task types in the task types is calculated, and the current type ratio and the corresponding calculation resource requirement under the combination of the data amount to be processed are determined according to the task resource mapping table. The task resource mapping table records the corresponding computing resource requirements under the combination of different types of proportion ranges and the range of the data amount to be processed.
S203: determining whether the computing resource demand exceeds a local computing resource carrying capacity. If so, go to step S205, otherwise, go to step S204.
Specifically, after the computing resource requirement of the graphics processing request is determined, the computing resource requirement is compared with the local computing resource of the computing control device at the edge of the internet of things, and when the computing resource requirement exceeds the local computing resource bearing capacity, the step jumps to step S205, and when the computing resource requirement does not exceed the local computing resource bearing capacity, the step jumps to step S204.
S204: and responding the graphics processing request based on local computing resources to obtain task result information.
Specifically, when the computing resource demand does not exceed the local computing resource carrying capacity, the internet of things edge computing control device directly responds to the graph processing request based on the local computing resource, and retrieves task data required by the graph processing task from the user terminal to execute the graph processing task, so as to obtain task result information. After the task result information is successfully obtained, the process goes to step S211.
In one possible embodiment, if the graphic processing task fails to process, it jumps to step S205.
S205: and dividing the tasks according to the graph processing request to obtain a plurality of subtasks corresponding to different itemized task types.
Specifically, when the computing resource demand exceeds the local computing resource carrying capacity, the task splitting is performed on the graphics processing task according to each item task type corresponding to the graphics processing request, and a plurality of subtasks corresponding to different item task types are obtained.
S206: determining a plurality of processing terminals with graphic processing capability in the same Internet of things network, and acquiring transmission effect parameters of the plurality of processing terminals and available computing resources of each processing terminal.
S207: and determining processing efficiency information of the available computing resources for different project task types, and computing a processing capacity score based on the transmission effect parameter and the processing efficiency information.
Specifically, processing terminals with graphic processing capability in the current internet of things network are determined, and transmission effect parameters of the processing terminals and available computing resources of the processing terminals are obtained.
Further, the processing efficiency information corresponding to different sub-task types processed by the processing terminals under the corresponding available computing resources is determined. The processing efficiency information can be dynamically updated on the basis of processing the graphic processing tasks (or subtasks) each time, that is, default processing efficiency information is set in the range of available computing resources and corresponding to different itemized task types of each processing terminal, and according to the processing efficiency corresponding to the graphic computing task and the corresponding available computing resources at each time, addition and subtraction calculation is performed on the processing efficiency information according to the set updating amplitude, so that dynamic updating of the processing efficiency information is realized, and a processing efficiency recording table is formed and recorded in a local storage or an internet of things edge computing control device. Processing efficiency information corresponding to different itemized task types processed by the processing terminals under the current available computing resources can be determined based on the processing efficiency record table.
Further, a processing power score is calculated based on the transmission effect parameter and the processing efficiency information. For example, the throughput fraction is k1, the transmission effect parameter + k2, the processing efficiency information + α, where k1 and k2 are respectively set scaling coefficients corresponding to the transmission effect parameter and the processing efficiency information, and α is a set correction coefficient. It will be appreciated that the higher the processing power score, the more efficient the processing terminal will be in responding to the itemized task types.
S208: for each subtask, distributing to a plurality of the processing terminals based on the processing capability scores, the subtask being processed by the plurality of the processing terminals.
After determining that each processing terminal processes the processing efficiency information corresponding to different sub-task types under the current available computing resources, distributing the sub-tasks to the multiple processing terminals based on the processing capacity scores, sending task data required by executing the graphic processing tasks called from the user terminals to the processing terminals, and processing the sub-tasks by the processing terminals based on the task data to obtain sub-task processing result information.
Specifically, for each subtask, the subtasks are sorted according to the processing capability scores corresponding to the item task types of the subtasks, a plurality of processing terminals are determined according to the sequence of the processing capability scores from large to small, the subtasks are sent to the processing terminals at the same time, task data required for executing the graphic processing task and called from the user terminal are sent to the processing terminals, and the processing terminals process the same subtasks independently to obtain subtask processing result information.
It will be appreciated that a plurality of sub-tasks may be allocated to a single processing terminal, and that the number of allocated sub-tasks does not exceed a set number or the aggregate computational resource requirements of the sub-tasks do not exceed the available computational resources of the processing terminal, and that there may be instances where a portion of the processing terminals are not allocated to sub-tasks.
S209: and acquiring the subtask processing result information returned by the plurality of processing terminals, and processing the result information of the plurality of subtasks corresponding to each subtask.
S210: and determining the final subtask processing result information of each subtask based on a consensus mechanism, and determining the task result information according to the final subtask processing result of each subtask.
And the processing terminal processes the distributed subtasks to obtain subtask processing result information and sends the subtask processing result information to the corresponding edge calculation control of the Internet of things.
Specifically, after sub-task processing result information returned by each processing terminal is received, the final sub-task processing result information of each sub-task is determined based on a consensus mechanism for a plurality of sub-task processing result information corresponding to each sub-task. For example, a plurality of subtask processing result information corresponding to the same subtask are voted and compared, the subtask processing result information with the highest votes is determined as the final subtask processing result information, or specific data contents of the plurality of subtask processing result information are compared, the data contents with differences are voted, the data contents with differences are updated to the data contents with the highest votes, and the final subtask processing result information of each subtask is finally obtained.
Further, according to the final subtask processing result of each subtask, the subtask processing results are integrated to obtain task result information corresponding to the whole graphic processing task.
S211: sending the task result information to the user terminal
Specifically, after the task result information is obtained, the task result information is returned to the user terminal which sends the graphic processing request, so that the user terminal can perform the next work.
By comparing the computing resource requirement of the graph processing request sent by the user terminal with the local computing resource bearing capacity, when the local computing resource bearing capacity can meet the computing resource requirement, the graph processing request is processed locally, and when the computing resource requirement exceeds the local computing resource bearing capacity, subtasks are distributed to the processing terminals of other nodes of the internet of things in the same internet of things, subtask processing result information returned by each processing terminal is obtained, task result information is obtained according to the subtask processing result information, and the task result information is returned to the user terminal, so that quick and effective response to the graph processing request is realized, the edge computing capacity for graph processing is provided, the problems of graph processing failure or overlong processing time caused by insufficient local computing resources are reduced, and the user experience is optimized effectively. Meanwhile, the processing capacity scores of each processing terminal on different item task types are calculated according to the transmission effect parameters and the processing efficiency information, subtasks are distributed to the processing terminals based on the processing capacity scores, the graphic processing efficiency is effectively improved, the final subtask processing result information is determined based on a consensus mechanism, and the graphic processing accuracy is effectively improved.
Fig. 3 is a schematic structural diagram of an edge computing control device of the internet of things according to an embodiment of the present application. Referring to fig. 3, the internet of things edge calculation control device includes a resource calculation module 31, a local processing module 32, a task division module 33, a task distribution module 34, and a result feedback module 35.
The resource calculation module 31 is configured to determine a calculation resource requirement based on a graphics processing request of a user terminal, where the user terminal is connected to an internet of things network; a local processing module 32, configured to, when the computing resource demand does not exceed a local computing resource carrying capacity, obtain task result information based on a local computing resource in response to the graphics processing request; the task dividing module 33 is configured to, when the computing resource demand exceeds the local computing resource carrying capacity, perform task division according to the graphics processing request to obtain a plurality of subtasks, and determine a plurality of processing terminals having graphics processing capacity in the same internet of things network; a task distributing module 34, configured to obtain transmission effect parameters of a plurality of processing terminals and available computing resources of each processing terminal, and distribute subtasks to the plurality of processing terminals based on the transmission effect parameters and the available computing resources, where the subtasks are processed by the plurality of processing terminals; and the result feedback module 35 is configured to obtain subtask processing result information returned by the plurality of processing terminals, obtain task result information based on the plurality of subtask processing result information, and send the task result information to the user terminal.
By comparing the computing resource requirement of the graph processing request sent by the user terminal with the local computing resource bearing capacity, when the local computing resource bearing capacity can meet the computing resource requirement, the graph processing request is processed locally, and when the computing resource requirement exceeds the local computing resource bearing capacity, subtasks are distributed to the processing terminals of other nodes of the internet of things in the same internet of things, subtask processing result information returned by each processing terminal is obtained, task result information is obtained according to the subtask processing result information, and the task result information is returned to the user terminal, so that quick and effective response to the graph processing request is realized, the edge computing capacity for graph processing is provided, the problems of graph processing failure or overlong processing time caused by insufficient local computing resources are reduced, and the user experience is optimized effectively.
The embodiment of the application also provides computer equipment which can integrate the device for controlling the calculation of the edge of the Internet of things provided by the embodiment of the application. Fig. 4 is a schematic structural diagram of a computer device according to an embodiment of the present application. Referring to fig. 4, the computer apparatus includes: an input device 43, an output device 44, a memory 42, and one or more processors 41; the memory 42 for storing one or more programs; when the one or more programs are executed by the one or more processors 41, the one or more processors 41 are enabled to implement the method for controlling the edge calculation of the internet of things according to the embodiment. Wherein the input device 43, the output device 44, the memory 42 and the processor 41 may be connected by a bus or other means, for example, in fig. 4.
The memory 42 is a storage medium readable by a computing device and can be used for storing software programs, computer executable programs, and modules, such as program instructions/modules corresponding to the method for controlling computing of an edge of an internet of things according to any embodiment of the present application (for example, the resource computing module 31, the local processing module 32, the task dividing module 33, the task distributing module 34, and the result feedback module 35 in the apparatus for controlling computing of an edge of an internet of things). The memory 42 may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created according to use of the device, and the like. Further, the memory 42 may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid state storage device. In some examples, memory 42 may further include memory located remotely from processor 41, which may be connected to the device over a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The input device 43 may be used to receive input numeric or character information and to generate key signal inputs relating to user settings and function controls of the apparatus. The output device 44 may include a display device such as a display screen.
The processor 41 executes various functional applications and data processing of the device by running software programs, instructions and modules stored in the memory 42, that is, implements the above-described method for controlling the edge calculation of the internet of things.
The device, the equipment and the computer for controlling the calculation of the edge of the internet of things can be used for executing the method for controlling the calculation of the edge of the internet of things provided by any embodiment, and have corresponding functions and beneficial effects.
An embodiment of the present application further provides a storage medium containing computer-executable instructions, where the computer-executable instructions are executed by a computer processor to perform the method for controlling edge calculation of an internet of things provided in the foregoing embodiment, and the method for controlling edge calculation of an internet of things includes: determining a computing resource requirement based on a graph processing request of a user terminal, wherein the user terminal is connected to an Internet of things network; if the computing resource demand does not exceed the local computing resource bearing capacity, responding to the graph processing request based on local computing resources to obtain task result information, otherwise, performing task division according to the graph processing request to obtain a plurality of subtasks, and determining a plurality of processing terminals with graph processing capacity in the same Internet of things network; acquiring transmission effect parameters of a plurality of processing terminals and available computing resources of each processing terminal, distributing subtasks to the plurality of processing terminals based on the transmission effect parameters and the available computing resources, and processing the subtasks by the plurality of processing terminals; and acquiring subtask processing result information returned by the plurality of processing terminals, acquiring task result information based on the plurality of subtask processing result information, and sending the task result information to the user terminal.
Storage medium-any of various types of memory devices or storage devices. The term "storage medium" is intended to include: mounting media such as CD-ROM, floppy disk, or tape devices; computer system memory or random access memory such as DRAM, DDR RAM, SRAM, EDO RAM, Lanbas (Rambus) RAM, etc.; non-volatile memory such as flash memory, magnetic media (e.g., hard disk or optical storage); registers or other similar types of memory elements, etc. The storage medium may also include other types of memory or combinations thereof. In addition, the storage medium may be located in a first computer system in which the program is executed, or may be located in a different second computer system connected to the first computer system through a network (such as the internet). The second computer system may provide program instructions to the first computer for execution. The term "storage medium" may include two or more storage media that may reside in different locations, such as in different computer systems that are connected by a network. The storage medium may store program instructions (e.g., embodied as a computer program) that are executable by one or more processors.
Of course, the storage medium provided in the embodiments of the present application and containing computer-executable instructions is not limited to the method for controlling the edge calculation of the internet of things described above, and may also perform related operations in the method for controlling the edge calculation of the internet of things provided in any embodiments of the present application.
The device, the apparatus, and the storage medium for controlling the edge calculation of the internet of things provided in the foregoing embodiments may execute the method for controlling the edge calculation of the internet of things provided in any embodiments of the present application, and refer to the method for controlling the edge calculation of the internet of things provided in any embodiments of the present application without detailed technical details described in the foregoing embodiments.
The foregoing is considered as illustrative of the preferred embodiments of the invention and the technical principles employed. The present application is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present application has been described in more detail with reference to the above embodiments, the present application is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present application, and the scope of the present application is determined by the scope of the claims.
Claims (7)
1. An edge calculation control method of an internet of things is characterized by comprising the following steps:
responding to a graphic processing request sent by a user terminal, and determining a task type and a data volume to be processed corresponding to the graphic processing request, wherein the task type comprises a plurality of subentry task types; determining the computing resource requirement based on the task type and the data volume to be processed, wherein the user terminal is connected to the Internet of things network;
if the computing resource demand does not exceed the local computing resource bearing capacity, responding to the graph processing request based on local computing resources to obtain task result information, otherwise, performing task division according to the graph processing request to obtain a plurality of subtasks corresponding to different itemized task types, and determining a plurality of processing terminals with graph processing capacity in the same Internet of things network;
acquiring transmission effect parameters of a plurality of processing terminals and available computing resources of each processing terminal, and determining processing efficiency information of the available computing resources on different itemized task types; calculating a processing power score based on the transmission effectiveness parameter and the processing efficiency information; distributing subtasks to a plurality of the processing terminals based on the processing capability scores, the subtasks being processed by the plurality of the processing terminals; the processing terminal is provided with default processing efficiency information corresponding to different subentry task types and available computing resource ranges, and performs addition and subtraction calculation on the processing efficiency information according to the processing efficiency corresponding to the graph computing task and the corresponding available computing resource and the set updating amplitude, and dynamically updates the processing efficiency information;
and acquiring subtask processing result information returned by the plurality of processing terminals, acquiring task result information based on the plurality of subtask processing result information, and sending the task result information to the user terminal.
2. The method for controlling computation of an edge of an internet of things according to claim 1, wherein the determining a computing resource requirement based on the task type and the amount of data to be processed comprises:
determining the type proportion of each subentry task type in the task types;
and inputting the type ratio and the data volume to be processed into a task resource mapping model according to the task resource mapping model, and outputting the required computing resource requirement by the task resource mapping model.
3. The method for controlling computation of an edge of an internet of things according to claim 1, wherein the determining a computing resource requirement based on the task type and the amount of data to be processed comprises:
determining the type proportion of each subentry task type in the task types;
and determining the type ratio and the computing resource requirement corresponding to the data volume to be processed according to a task resource mapping table.
4. The method for controlling computation of an edge of an internet of things according to claim 1, wherein the distributing of subtasks to the plurality of processing terminals based on the processing capability scores comprises:
for each subtask, distributing to a plurality of the processing terminals based on the processing capability scores;
the obtaining of task result information based on a plurality of the subtask processing result information includes:
determining a final subtask processing result of each subtask based on a consensus mechanism for a plurality of subtask processing result information corresponding to each subtask;
and determining task result information according to the final subtask processing result of each subtask.
5. The utility model provides an thing networking edge computing control device which characterized in that, includes resource calculation module, local processing module, task division module, task distribution module and result feedback module, wherein:
the resource calculation module is used for responding to a graphic processing request sent by a user terminal, determining a task type and a data volume to be processed corresponding to the graphic processing request, wherein the task type comprises a plurality of subentry task types; determining the computing resource requirement based on the task type and the data volume to be processed, wherein the user terminal is connected to the Internet of things network;
the local processing module is used for responding the graphic processing request based on the local computing resource to obtain task result information when the computing resource demand does not exceed the bearing capacity of the local computing resource;
the task dividing module is used for dividing tasks according to the graph processing request to obtain a plurality of subtasks corresponding to different itemized task types and determining a plurality of processing terminals with graph processing capacity in the same Internet of things network when the computing resource demand exceeds the bearing capacity of local computing resources;
the task distribution module is used for acquiring transmission effect parameters of a plurality of processing terminals and available computing resources of each processing terminal and determining processing efficiency information of the available computing resources on different itemized task types; calculating a processing power score based on the transmission effectiveness parameter and the processing efficiency information; distributing subtasks to a plurality of the processing terminals based on the processing capability scores, the subtasks being processed by the plurality of the processing terminals; the processing terminal is provided with default processing efficiency information corresponding to different subentry task types and available computing resource ranges, and performs addition and subtraction calculation on the processing efficiency information according to the processing efficiency corresponding to the graph computing task and the corresponding available computing resource and the set updating amplitude, and dynamically updates the processing efficiency information;
and the result feedback module is used for acquiring the subtask processing result information returned by the plurality of processing terminals, acquiring task result information based on the plurality of subtask processing result information, and sending the task result information to the user terminal.
6. A computer device, comprising: a memory and one or more processors;
the memory for storing one or more programs;
when executed by the one or more processors, cause the one or more processors to implement the method of internet of things edge computation control of any of claims 1-4.
7. A storage medium containing computer-executable instructions, which when executed by a computer processor, are configured to perform the method for internet of things edge computation control according to any of claims 1 to 4.
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| CN113840010A (en) * | 2021-09-30 | 2021-12-24 | 深圳供电局有限公司 | Data processing system, method, apparatus, device and medium based on edge calculation |
| CN115378826B (en) * | 2022-10-26 | 2023-01-31 | 北京网藤科技有限公司 | Network vulnerability identification method and system for multiple workflows |
| CN115587103A (en) * | 2022-12-07 | 2023-01-10 | 杭州华橙软件技术有限公司 | Algorithm resource planning method, device, terminal and computer readable storage medium |
| CN116016637A (en) * | 2022-12-19 | 2023-04-25 | 先临三维科技股份有限公司 | Task processing method, device, equipment and storage medium |
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