CN111327677B - Industrial Internet of things resource scheduling system and method based on edge calculation - Google Patents
Industrial Internet of things resource scheduling system and method based on edge calculation Download PDFInfo
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- CN111327677B CN111327677B CN202010065354.XA CN202010065354A CN111327677B CN 111327677 B CN111327677 B CN 111327677B CN 202010065354 A CN202010065354 A CN 202010065354A CN 111327677 B CN111327677 B CN 111327677B
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Abstract
The invention provides an industrial Internet of things resource scheduling system based on edge computing, which comprises a terminal data acquisition layer, a data transmission layer and a data transmission layer, wherein the terminal data acquisition layer is used for acquiring industrial data detected in real time and transmitting an optimization request of the industrial Internet of things; the edge data processing layer is used for transmitting the optimization request of the terminal data acquisition layer to the cloud processing center, optimizing the industrial data transmitted by the cloud computing center and then returning the optimization result to the terminal data acquisition layer; the cloud computing center is used for storing and analyzing the optimization request transmitted by the terminal data acquisition layer, searching the edge device most suitable for processing the request in the contained edge data processing layer, and transmitting the physical address of the terminal data acquisition layer to the edge data processing layer. According to the method, the optimization request of the industrial Internet of things is deployed to the cloud computing center, and the optimal edge device suitable for the request is searched according to the application scenes and the service types of different optimization requests, so that the resource utilization efficiency of an edge platform layer is improved, and the bandwidth and the total time delay of the whole industrial Internet of things system are reduced.
Description
Technical Field
The invention relates to an industrial Internet of things resource scheduling system and method based on edge computing.
Background
At present, the integration and innovation of the Internet of things and industry are trending, and it is estimated that the proportion of the industry Internet of things in the whole Internet of things industry reaches 25% by 2020, and the scale of the industry Internet of things breaks through 4500 hundred million yuan. With the rapid development of the industrial internet of things, the number of terminal devices and the amount of data generated by the terminal devices are rapidly increased, and it is difficult for a data processing model taking cloud computing as a core to efficiently process massive data generated by edge terminal devices. Edge computing has been generally recognized by the industry as one of the major trends in next generation digital transformation.
Edge computing is an emerging technology in the field of internet of things. Traditional cloud computing data transmission has limited network performance, and centralized cloud computing architectures have become inefficient for analyzing and processing large amounts of data collected from internet of things devices. Edge computing offloads computing tasks from the centralized cloud to edges near the internet of things devices, so the preprocessing process greatly reduces the transmitted data. The edge calculation can be performed well when the intermediate data size is smaller than the input data size.
In addition, the edge calculation fully utilizes the limited calculation capacity of the network edge equipment to process the original data to obtain an intermediate result, and the data transmission amount is reduced. Therefore, the requirements of real-time response, safety privacy, high bandwidth and the like of the user are met. Therefore, the industrial internet of things resource scheduling system based on edge computing has important significance for development of industrial internet of things.
Through search, the Chinese patent application with the publication number of CN108924228A provides an industrial internet optimization system based on edge calculation, which comprises an edge access layer used for collecting real-time monitored industrial data; the industrial interconnection edge platform layer is used for optimizing and transmitting the collected industrial data, returning the optimized industrial data to the edge layer, and transmitting an optimization request and industrial data of the unprocessed industrial Internet of things to the core cloud; and the core cloud is used for processing the unprocessed optimization request and the industrial data and returning a processing result to the edge access layer. The above documents are basic patents about industrial internet, and the core of the invention is to process part of content in industrial internet optimization requests and industrial data at the current edge layer, and transmit the rest to the core cloud for processing. However, application scenarios and service types of different tasks have diversity, and the current edge platform layer is not necessarily suitable for processing optimization requests of the terminal. For example, some tasks have a strong local, real-time nature, requiring optimization requests to be processed at neighboring edge devices; some tasks have certain computing resource requirements, and meanwhile, have larger interaction frequency and transmission data volume, and need edge equipment with strong computing power to optimize processing. Therefore, there are probably optimal edge devices in the entire internet system for different task types. The direct processing at the current edge platform layer may cause a higher time delay, and is also a waste for the resources of the edge platform layer.
Disclosure of Invention
The invention aims to solve the technical problem that the problem of low efficiency of an industrial internet edge layer in the prior art is overcome, and an industrial internet of things resource scheduling system and method based on edge computing are provided.
The invention provides an industrial Internet of things resource scheduling system based on edge computing, which comprises a terminal data acquisition layer, an edge data processing layer and a cloud computing center, wherein the terminal data acquisition layer is connected with the edge data processing layer;
the terminal data acquisition layer is used for acquiring industrial data detected in real time and transmitting an optimization request of the industrial Internet of things;
the edge data processing layer is used for transmitting the optimization request of the terminal data acquisition layer to the cloud computing center, optimizing the industrial data transmitted by the cloud computing center and then returning the optimization result to the terminal data acquisition layer;
the cloud computing center is used for storing and analyzing the optimization request transmitted by the terminal data acquisition layer, searching the edge equipment most suitable for processing the request in the contained edge data processing layer, and transmitting the physical address of the terminal data acquisition layer to the edge data processing layer.
The invention also provides an industrial Internet of things resource scheduling method based on edge computing, which comprises the following steps:
and 6, analyzing and optimizing the rest undeployed tasks in the cloud computing center, and returning an optimization result to the corresponding terminal data acquisition layer.
In step 3, a specific operation method for finding the optimal edge device is as follows:
step 301, after receiving an optimization request of a terminal data acquisition layer, a cloud computing center determines a physical address of the terminal data acquisition layer according to a task Si to obtain a routing hop count between the terminal data acquisition layer and an optimal edge device, determines whether the routing hop count is smaller than a given routing hop count (assuming that the given routing hop count is 5), if the routing hop count is smaller than the given routing hop count, marks the task Si as being capable of finding the optimal edge device, and goes to step 302; otherwise, marking the task as that the best edge device is not found;
step 302, the cloud computing center sequentially judges whether the computing time delay and the service capability of the edge data processing layer meet the requirements of the task according to the proximity from low to high; go to step 303;
step 303, if the computation delay and the service capability of the edge data processing layer meet the task requirement, the cloud computing center further determines whether the environment sensing capability of the edge data processing layer meets the task requirement, otherwise, the routing hop count is added by 1 and then the cloud computing center returns to step 301 to re-determine whether the current routing hop count is smaller than the given routing hop count;
and 304, if the environment perception capability of the edge data processing layer meets the task requirement, recording the edge data processing layer as the optimal edge device of the task, otherwise, adding 1 to the routing hop count, and returning to 301 to judge whether the current routing hop count is less than the given routing hop count again.
In the above method, the proximity means the proximity of the terminal and the edge layer, which includes two layers. First, the logical proximity represents the number of route hops between the terminal and the edge layer, and the more the number of hops is, the higher the probability of congestion in the transmission process is, and the higher the time delay is. Second, the physical proximity depends on the physical distance of the terminal from the edge layer and the computing power of the edge data processing layer. If a large number of terminals are deployed around an edge data handling layer and the service capacity of the edge handling layer is exceeded, the edge handling layer will refuse to accept optimization requests.
The computation delay and the service capability are directly dependent on the computation capability of the edge data processing layer, and are important parameters of the edge data processing layer.
In step 301, all tasks that do not find the best edge device form a set T, and for a task Ti that does not find the best edge device, the following operations are performed:
step a, the cloud computing center counts idle edge devices, performs descending order arrangement according to the data volume which can be processed by the idle edge devices, distributes tasks in a task set T which does not find the optimal edge device to the corresponding idle edge devices according to the data volume and the priority, and then transmits the physical address of a terminal data acquisition layer to which the tasks which do not find the optimal edge device belong to the idle edge devices; turning to the step b;
and step b, after the idle edge equipment receives the physical address transmitted by the cloud computing center, searching equipment of the terminal data acquisition layer according to the physical address, acquiring detailed industrial data for analysis and optimization, returning an optimization result to the corresponding terminal data acquisition layer, and turning to step 6.
In step 303, the context awareness capability includes link condition, load and network bandwidth of the network.
The environmental perception is to expose the parameters of the network and the location information of the surrounding terminals to the edge layer, the edge processing layer is usually distributed near the network controller, to monitor the link condition, load and network bandwidth of the network in real time, and to receive the location information of the terminals. Therefore, the context awareness of the edge data processing layer is very important.
Compared with the prior art, the invention adopting the technical scheme has the following technical effects: according to the invention, the optimization request and the industrial data of the terminal data acquisition layer are deployed to the cloud computing center, the optimal edge equipment is searched through the analysis and processing of the cloud computing center, the task is deployed to the optimal edge equipment for optimization processing, and the working efficiency and the carrying capacity of the edge processing layer are improved. Meanwhile, the system unloads a part of tasks to the edge layer for optimization processing, and dispatches large tasks which cannot be processed by the edge layer to the cloud computing center for operation, so that the computing tasks of the cloud computing center are reduced, and the time delay of the whole system is greatly reduced.
Drawings
Fig. 1 is a schematic diagram of an industrial internet of things resource scheduling system of the present invention.
Fig. 2 is a flowchart of finding the best edge device in the present invention.
Fig. 3 is a schematic diagram of one embodiment of the present invention.
Detailed Description
The industrial Internet of things resource scheduling system based on edge computing comprises a terminal data acquisition layer, an edge data processing layer and a cloud computing center (shown in figure 1). The terminal data acquisition layer is used for acquiring industrial data detected in real time and transmitting an optimization request of the industrial Internet of things; the edge data processing layer is used for transmitting the optimization request of the terminal data acquisition layer to the cloud computing center, optimizing the industrial data transmitted by the cloud computing center and then returning the optimization result to the terminal data acquisition layer; and the cloud computing center is used for storing and analyzing the optimization request transmitted by the terminal data acquisition layer, searching the edge equipment most suitable for processing the request in the contained edge data processing layer, and transmitting the physical address of the terminal data acquisition layer to the edge data processing layer.
The technical scheme of the invention is further explained in detail by combining the attached drawings: the present embodiment is implemented on the premise of the technical solution of the present invention, and a detailed implementation manner and a specific operation process are given, but the protection authority of the present invention is not limited to the following embodiments.
Example 1
In this embodiment, an industrial internet of things resource scheduling system based on edge computing is provided, as shown in fig. 3, the system includes a cloud computing center, three edge data processing devices (all the edge data processing devices constitute an edge data processing layer), and each edge data processing device includes three terminal data acquisition devices (all the terminal data acquisition devices constitute a terminal data acquisition layer). The parameters of the terminal data acquisition layer are shown in the following table 1:
TABLE 1
| Numbering | Data transmission amount | Time requirement | Degree of |
|
| 1 | 10GB | 5ms | In general | |
| 2 | 200GB | 15ms | In general | |
| 3 | 30GB | | Emergency system | |
| 4 | 40GB | 0.5ms | Very urgent | |
| 5 | 300GB | 12ms | In |
|
| 6 | 60GB | 6ms | In general | |
| 7 | 70GB | 0.4ms | Very urgent | |
| 8 | 80GB | | Emergency system | |
| 9 | 90GB | 14ms | In general |
The parameters of the edge data handling layer are shown in table 2 below:
TABLE 2
| Numbering | Data processing capability | Calculating time delay | Context awareness capability |
| A | 50GB | 5ms | In general |
| B | 100GB | 8ms | In general |
| C | 2TB | 0.2ms | Is excellent in |
The industrial internet of things resource scheduling method based on edge computing comprises the following steps:
And 3, the cloud computing center sequentially analyzes various resources required by the task Si according to the grouping of the subset and the task priority, searches edge equipment which is most suitable for processing the task in a subordinate edge data processing layer, and records the edge equipment as optimal edge equipment.
The specific operation method for finding the optimal edge device is as follows:
step 301, after receiving an optimization request of a terminal data acquisition layer, a cloud computing center determines a physical address of the terminal data acquisition layer according to a task Si to obtain a routing hop count between the terminal data acquisition layer and an optimal edge device, determines whether the routing hop count is smaller than a given routing hop count (assuming that the given routing hop count is 5), if the routing hop count is smaller than the given routing hop count, marks the task Si as being capable of finding the optimal edge device, and goes to step 302; otherwise, marking the task as that the best edge device is not found;
step 302, the cloud computing center sequentially judges whether the computing time delay and the service capability of the edge data processing layer meet the requirements of the task according to the proximity from low to high; go to step 303;
step 303, if the computation delay and the service capability of the edge data processing layer meet the task requirements, the cloud computing center further determines whether the environment sensing capability of the edge data processing layer meets the requirements of the task (the environment sensing capability includes the link condition, the load and the network bandwidth of the network), otherwise, the cloud computing center returns to step 301 after adding 1 to the routing hop count to re-determine whether the current routing hop count is less than the given routing hop count;
and 304, if the environment perception capability of the edge data processing layer meets the task requirement, recording the edge data processing layer as the optimal edge device of the task, otherwise, adding 1 to the routing hop count, and returning to the step 301 to judge whether the current routing hop count is less than the given routing hop count again.
Therefore, if the optimal edge device is found, the physical address of the terminal data acquisition layer to which the task belongs is transmitted to the optimal edge device; if no best edge device is found, the task is recorded as T i All of T i Forming a set T.
And 4, the cloud computing center transmits the physical address of the terminal data acquisition layer to which the task of finding the optimal edge device belongs to the found optimal edge device.
And 5, after the optimal edge equipment receives the physical address transmitted by the cloud computing center, searching equipment of the terminal data acquisition layer according to the physical address, acquiring detailed industrial data (recorded as Hi) for analysis and optimization, and returning an optimization result (recorded as Pi) to the corresponding terminal data acquisition layer.
And 6, analyzing and optimizing the rest undeployed tasks in the cloud computing center, and returning an optimization result to the corresponding terminal data acquisition layer.
In addition, all tasks that do not find the best edge device form a set T, and for a task Ti that does not find the best edge device, the following operations are performed:
step a, the cloud computing center counts idle edge devices, performs descending order arrangement according to the data volume which can be processed by the idle edge devices, distributes tasks in a task set T which does not find the optimal edge device to the corresponding idle edge devices according to the data volume and the priority, and then transmits the physical address of a terminal data acquisition layer to which the tasks which do not find the optimal edge device belong to the idle edge devices; turning to the step b;
and step b, after the idle edge equipment receives the physical address transmitted by the cloud computing center, searching equipment of the terminal data acquisition layer according to the physical address, acquiring detailed industrial data for analysis and optimization, returning an optimization result to the corresponding terminal data acquisition layer, and turning to step 6.
In summary, according to the conventional resource scheduling system, each edge processing device processes data of the terminal data acquisition device, so the optimization requests of the terminal No. 2 and the terminal No. 5 cannot be processed at the edge layer, and are transmitted to the cloud computing center for optimization processing, and then the result is transmitted back through the edge layer, and a large amount of time is consumed in the transmission process. According to the method of the embodiment, the optimization requests of the No. 2 terminal and the No. 5 terminal can be completed at the edge device C, priority processing can be performed according to the emergency degree, and the working efficiency of the whole system is improved, so that the requirements of real-time response, safety and privacy, high bandwidth and the like of a user are met.
The above description is only an embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can understand that the modifications or substitutions within the technical scope of the present invention are included in the scope of the present invention, and therefore, the scope of the present invention should be subject to the protection scope of the claims.
Claims (3)
1. An industrial Internet of things resource scheduling method based on edge computing is characterized in that a system related to the method comprises a terminal data acquisition layer, an edge data processing layer and a cloud computing center; the terminal data acquisition layer is used for acquiring industrial data detected in real time and transmitting an optimization request of the industrial Internet of things;
the edge data processing layer is used for transmitting the optimization request of the terminal data acquisition layer to the cloud computing center, optimizing the industrial data transmitted by the cloud computing center and then returning the optimization result to the terminal data acquisition layer;
the cloud computing center is used for storing and analyzing the optimization request transmitted by the terminal data acquisition layer, searching edge equipment most suitable for processing the request in the contained edge data processing layer, and transmitting the physical address of the terminal data acquisition layer to the edge data processing layer;
the method comprises the following steps:
step 1, defining an optimization request of a terminal data acquisition layer and industrial data to be processed as tasks Si, forming a task set S by all the tasks, and deploying the task set S to a cloud computing center through an edge data processing layer; turning to the step 2;
step 2, the cloud computing center analyzes the emergency degree and the task type of each task in the task set S, divides the task set S into N subsets [ S1, S2, S3.. Sn ] according to the task type, and then arranges the subsets in sequence from high to low according to the task priority; turning to the step 3;
step 3, the cloud computing center sequentially analyzes various resources required by the task Si according to the grouping of the subsets and the task priority, searches edge equipment which is most suitable for processing the task in a subordinate edge data processing layer, and records the edge equipment as optimal edge equipment; turning to the step 4;
step 4, the cloud computing center transmits the physical address of the terminal data acquisition layer to which the task of finding the optimal edge device belongs to the found optimal edge device; turning to step 5;
step 5, after receiving the physical address transmitted by the cloud computing center, the optimal edge device searches a terminal data acquisition layer according to the physical address, acquires detailed industrial data for analysis and optimization processing, and returns an optimization result to the corresponding terminal data acquisition layer; turning to step 6;
and 6, analyzing and optimizing the rest undeployed tasks in the cloud computing center, and returning an optimization result to the corresponding terminal data acquisition layer.
2. The industrial internet of things resource scheduling method based on edge computing as claimed in claim 1, wherein in the step 3, the specific operation method for finding the optimal edge device is as follows:
step 301, after receiving an optimization request of a terminal data acquisition layer, a cloud computing center determines a physical address of the terminal data acquisition layer according to a task Si to obtain a routing hop count between the terminal data acquisition layer and an optimal edge device, judges whether the routing hop count is smaller than a given routing hop count, marks the task Si as being capable of finding the optimal edge device if the routing hop count is smaller than the given routing hop count, and returns to step 302; otherwise, marking the task as that the best edge device is not found, forming a set T by all the tasks of which the best edge device is not found, and aiming at the task Ti of which the best edge device is not found, carrying out the following operations:
step a, the cloud computing center counts idle edge devices, performs descending order arrangement according to the data quantity which can be processed by the idle edge devices, distributes tasks in a task set which do not find the optimal edge device to the corresponding idle edge devices according to the data quantity and the priority, and then transmits the physical address of a terminal data acquisition layer to which the tasks which do not find the optimal edge device belong to the idle edge devices; turning to the step b;
step b, after the idle edge equipment receives the physical address transmitted by the cloud computing center, searching a terminal data acquisition layer according to the physical address, acquiring detailed industrial data for analysis and optimization, returning an optimization result to the corresponding terminal data acquisition layer, and turning to step 6;
step 302, the cloud computing center sequentially judges whether the computing time delay and the service capability of the edge data processing layer meet the requirements of the task according to the proximity from low to high; go to step 303;
step 303, if the computation delay and the service capability of the edge data processing layer meet the task requirement, the cloud computing center further determines whether the environment sensing capability of the edge data processing layer meets the task requirement, otherwise, the routing hop count is added by 1 and then the cloud computing center returns to step 301 to re-determine whether the current routing hop count is smaller than the given routing hop count;
and 304, if the environment perception capability of the edge data processing layer meets the task requirement, recording the edge data processing layer as the optimal edge device of the task, otherwise, adding 1 to the routing hop count, and returning to 301 to judge whether the current routing hop count is less than the given routing hop count again.
3. The method for scheduling resources of the internet of things for the industry based on the edge computing as claimed in claim 2, wherein in the step 303, the environment awareness capability includes link conditions, load and network bandwidth of the network.
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