CN111556469A - Computing system and computing method for artificial intelligence edge - Google Patents
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Abstract
The invention relates to an artificial intelligence edge computing system, which comprises at least one Internet of things device, an edge acquisition computing device and a mobile edge server, wherein the Internet of things device is connected with the edge acquisition computing device through a network; the Internet of things equipment is used for collecting environmental information and converting the environmental information into the data to be processed; the edge acquisition computing equipment is used for acquiring monitoring characteristic data acquired by a plurality of Internet of things equipment accessed to the edge acquisition computing equipment so as to predict whether the specified area is abnormal or not; the mobile edge server is used for data acquisition and data arrangement, and comprises a display unit, a storage unit, a wireless network interface unit, a judgment module and an alarm instruction generation unit. The invention also discloses a corresponding method. The invention can improve the data processing efficiency, reduce the calculation pressure and the data delay, and improve the safety of the edge calculation.
Description
Technical Field
The invention relates to the field of data processing of edge calculation, in particular to a calculation system and a calculation method of an artificial intelligence edge.
Background
The front-end data acquisition point (namely, edge device) in the internet of things only has the functions of data acquisition and transmission initially, and because the integration level of the existing sensor is high, the data volume acquired by the edge device is increased, and the speed of the demand of the boundary side where a client is located on a calculation result is also continuously increased, so that the generation of an edge calculation theory is promoted.
Edge computing refers to an open platform integrating network, computing, storage and application core capabilities at one side close to an object or a data source to provide nearest-end services nearby. The application program is initiated at the edge side, so that a faster network service response is generated, and the basic requirements of the industry in the aspects of real-time business, application intelligence, safety, privacy protection and the like can be met. Edge computing is relative to cloud computing, which is essentially a distributed computing.
The edge computing theory relates to a large amount of data processing, and the requirement on the data processing is higher and higher, but at present, the general data processing adopts cloud computing, and the specific cloud computing is performed in a way that all data are uploaded to a central server for processing after passing through each node server through a network.
Traditional edge computing is a method for physically approaching the position of data generation and processing data, for example, sensing nodes of the internet of things, and some sensing nodes are not only responsible for collecting and transmitting data, but also realize functions of partial data processing and result output. However, no corresponding edge computing industry standard, specification and protocol exist, and the provided edge computing scheme lacks technology and product commonality and is difficult to be generally used for industrial communication, namely, the cloud computing pressure cannot be relieved and the problem of data delay cannot be solved to a certain extent.
Disclosure of Invention
In order to solve the problems in the prior art, the invention provides a computing system and a computing method for an artificial intelligence edge, which can improve the data processing efficiency, reduce the computing pressure and the data delay, and improve the computing safety of the edge.
In order to solve the above technical problem, an aspect of the present invention provides an artificial intelligence edge computing system, which includes at least one internet of things device, an edge acquisition computing device, and a mobile edge server; wherein,
the Internet of things equipment is used for collecting environmental information and converting the environmental information into the data to be processed;
the edge acquisition computing equipment is used for acquiring monitoring characteristic data acquired by a plurality of Internet of things equipment accessed to the edge acquisition computing equipment so as to predict whether the specified area is abnormal or not; the edge acquisition computing equipment comprises an AIOS operating system unit, a data acquisition unit, a data conversion unit, a data cleaning unit and a data aggregation computing unit;
the mobile edge server is used for data acquisition and data arrangement, and comprises a display unit, a storage unit, a wireless network interface unit, a judgment module and an alarm instruction generation unit.
Preferably, the data acquisition unit is used for acquiring illumination, temperature, humidity and PM2.5 environmental data, and the data acquired by the data acquisition unit is sent to the data conversion unit after being classified and processed.
The data conversion unit is used for receiving the data from the data acquisition unit, performing format conversion and sending the data to the data cleaning unit;
the data cleaning unit is used for cleaning the data received from the data conversion unit and sending the data to the data aggregation calculation unit;
the data aggregation calculation unit is used for performing aggregation calculation on the data received from the data cleaning unit to form sorted data; and inputting the sorted data into a cloud deep learning model in an AIOS operating system, and outputting a result.
Preferably, the AIOS operating system unit comprises a bus and a device driver for connecting the data acquisition unit, the data conversion unit, the data cleaning unit and the data aggregation calculation unit, and a platform layer for providing a deep learning algorithm framework, a neural network model, each algorithm SDK and services; the AIOS operating system unit can support a single linux operating system and a linux + RTOS heterogeneous operating system.
Preferably, in the edge-capture computing device, the AIOS operating system unit further includes an AI core processor, and the AI core processor includes a multi-core ARM processor, a multi-core GPU, a multi-core neural network processor NNIE, and a multi-core DSP.
Preferably, in the mobile edge server, the display unit is one of an LED, an LCD or an OLED, and the display unit displays an operating state or configuration information of the device;
the storage unit comprises an onboard storage medium and an external storage medium, wherein the storage unit comprises an EMMC (embedded multi-card memory), an E2PROM (electrically erasable programmable read-only memory), a FLASHSD (flash secure digital memory), an SSD (solid state disk) and a hard disk, and the storage unit stores data written by the intelligent block chain processing unit or the routing processing unit or data required to be read;
the wireless network interface unit comprises a serial port, an IO port, an RS485, a USB, an audio interface and a video interface, the wireless network interface unit realizes the input of external information, and the wireless network interface unit is responsible for inputting and outputting data in other forms except network data.
Preferably, the edge collecting computing device further includes a security protection unit, and the security protection unit has a public key authentication mechanism and a private key authentication mechanism, and realizes providing a single or batch edge device authentication verification process.
Preferably, the mobile edge server further comprises a judging module and an alarm instruction generating module, wherein the wireless network interface unit collects data related to the execution end; the judging module is used for judging whether the data related to the execution end is abnormal or not; the alarm instruction generating module is used for generating an alarm instruction when the judgment result is yes; the wireless network interface unit sends an alarm instruction to a display unit, and the display unit executes the alarm instruction.
As another aspect of the present invention, there is also provided a method for calculating an artificial intelligence edge, which is implemented in the foregoing system, and includes the following steps:
step S10: the mobile edge server and the edge acquisition computing equipment perform deep learning model training by using a large amount of collected historical data to finally generate a cloud deep learning model; issuing the deep learning model to an AIOS operating system unit;
step S11: the method comprises the steps that the Internet of things equipment collects environment information and converts the environment information into data to be processed;
step S12: the method comprises the steps that monitoring characteristic data acquired by a plurality of Internet of things devices connected to the edge acquisition computing device are acquired by the edge acquisition computing device, processed and sent to a cloud deep learning model in an AIOS operating system, and a result is output to predict whether an appointed area is abnormal or not;
step S13, the wireless network interface unit of the mobile edge server collects the data related to the execution end; judging whether data related to the execution end and the cloud deep learning model are abnormal or not through a judging module; when the data are judged to be abnormal, the alarm instruction generating module generates an alarm instruction;
step S14, the wireless network interface unit sends the alarm instruction to a display unit, and the display unit executes the alarm instruction.
Preferably, the step S12 further includes:
the data acquisition unit acquires illumination, temperature, humidity and PM2.5 environmental data, wherein the acquired data are analog data or digital data, and the data acquisition unit transmits the acquired data to the data conversion unit after classification processing;
the data conversion unit converts the format of the data and sends the data to the data cleaning unit;
the data cleaning unit is used for cleaning data, and then the data aggregation calculation unit is used for performing aggregation calculation to form sorted data;
and inputting the sorted data into a cloud deep learning model in an AIOS operating system, and outputting a result.
Preferably, further comprising: the safety protection unit realizes the authentication and verification process of single or batch edge equipment according to the public key authentication mechanism and the private key authentication mechanism.
The embodiment of the invention has the following beneficial effects:
firstly, in the embodiment of the invention, the data conversion unit is connected with the data acquisition unit, the data conversion unit is connected with the data cleaning unit, the data cleaning unit is connected with the data aggregation calculation unit, and the data conversion unit, the data cleaning unit and the data aggregation calculation unit are cooperatively used for sorting the data acquired by the data acquisition unit, so that the data processing efficiency is improved, the calculation pressure is reduced and the data delay is reduced.
Secondly, in the embodiment of the invention, the edge acquisition computing device further comprises a safety protection unit, and the safety protection unit is provided with a public key authentication mechanism and a private key authentication mechanism, so that the single or batch edge device authentication and verification process is realized, and the safety of edge computing is improved.
In addition, in the embodiment of the invention, the wireless network interface unit comprises a serial port, an IO port, an RS485, a USB, an audio interface and a video interface, the wireless network interface unit realizes the input of external information, and the wireless network interface unit is responsible for inputting and outputting data in other forms except network data; the method has multiple data access protocols, can support multiple equipment access functions, and can further process and operate the acquired data besides the traditional routing function.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention.
FIG. 1 is a schematic diagram of an embodiment of a computing system for an artificial intelligence edge provided by the present invention;
FIG. 2 is a schematic structural diagram of the edge capture computing device of FIG. 1;
FIG. 3 is a schematic diagram of the mobile edge server of FIG. 1;
fig. 4 is a main flow diagram of an embodiment of a method for calculating an artificial intelligence edge according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments.
FIG. 1 is a block diagram illustrating an embodiment of a computing system for an artificial intelligence edge provided by the present invention; referring to fig. 2 and fig. 3 together, in the embodiment of the present invention, the computing system of the artificial intelligence edge at least includes an internet of things device 1, an edge collecting computing device 2, and a mobile edge server 3.
The internet of things device 1 is used for collecting environment information and converting the environment information into data to be processed. Wherein, a plurality of thing networking equipment belong to same appointed region.
The edge acquisition computing device 2 is configured to acquire monitoring feature data acquired by the multiple internet of things devices 1 accessing the edge acquisition computing device to predict whether an appointed area is abnormal. More specifically, the artificial intelligence edge-capturing computation device 2 includes a data capturing unit 20, a data conversion unit 21, a data cleansing unit 22, and a data aggregation computation unit 23.
In the edge collecting and computing device 2, the data collecting unit 20 is used for collecting illuminance, temperature, humidity, and PM2.5 environmental data, the data collected by the data collecting unit 20 is analog data or digital data, and the data collected by the data collecting unit 20 is classified and processed by the wireless network interface unit and then sent to the data converting unit 21.
The data conversion unit 21 is configured to perform format conversion on the data from the data acquisition unit 20, and send the data to the data cleaning unit 22;
the data cleansing unit 22 is used for data cleansing (removing unnecessary data) of the data from the data cleansing unit 22;
the data aggregation calculation unit 23 is used for performing aggregation calculation on the data from the data cleaning unit 22 to form sorted data; the collated data is input into the cloud deep learning model in the AIOS operating system 25, and the results are output.
Further, the edge collecting computing device 2 further includes a security protection unit 24, and the security protection unit 24 has a public key authentication mechanism and a private key authentication mechanism, and realizes providing a single or batch edge device authentication verification process.
The edge acquisition computing device 2 further comprises an AIOS operating system unit 25, wherein the AIOS operating system unit 25 comprises a bus and a device driver which are used for connecting the data acquisition unit 20, the data conversion unit 21, the data cleaning unit 22 and the data aggregation computing unit 23, and a platform layer which is used for providing a deep learning algorithm framework, a neural network model, various algorithm SDKs and services; the AIOS operating system unit 25 may support a single linux operating system and a linux + RTOS heterogeneous operating system.
In the edge-capture computing device, the AIOS operating system unit 25 further includes an AI core processor, and the AI core processor includes a multi-core ARM processor, a multi-core GPU, a multi-core neural network processor NNIE, and a multi-core DSP.
The mobile edge server 3 is used for data acquisition and data arrangement, wherein the mobile edge server 3 at least comprises a display unit 30, a storage unit 31, a wireless network interface unit 32, a judgment module 33 and an alarm instruction generation module 34.
The display unit 30 is one of an LED, an LCD, or an OLED, and the display unit 30 displays an operation state or configuration information of the device.
The storage unit 31 comprises an onboard storage medium and an external storage medium, wherein the storage unit 31 comprises an EMMC, an E2PROM, a FLASHSD card, an SSD and a hard disk, and the storage unit 31 stores data written by or needing to be read by the intelligent block chain processing unit or the routing processing unit.
The wireless network interface unit 32 includes a serial port, an IO port, an RS485, a USB, an audio port, and a video port, the wireless network interface unit 32 realizes input of external information, collects data related to the execution end, and the wireless network interface unit 32 is responsible for input and output of data in other forms except for network data.
The judging module 33 is configured to judge whether both the output result of the cloud deep learning model and the data related to the execution end are abnormal, and determine that the data are abnormal if both the output result and the data related to the execution end are abnormal; otherwise, determining that the data is not abnormal; .
The alarm instruction generating module 34 is configured to generate an alarm instruction when the determination result of the determining module 33 is that the data is abnormal. The wireless network interface unit 32 transmits the alarm instruction to the display unit 30, and the display unit 30 executes the alarm instruction.
Fig. 4 is a main flow diagram illustrating an embodiment of a method for calculating an artificial intelligence edge according to the present invention. In this embodiment, it is implemented in the system shown in fig. 1 to fig. 3, and specifically, the method for calculating the artificial intelligence edge includes the following steps:
step S10: the mobile edge server and the edge acquisition computing equipment perform deep learning model training by using a large amount of collected historical data to finally generate a cloud deep learning model; the deep learning model is issued to the AIOS operating system unit.
Step S11: the Internet of things equipment collects the environment information and converts the environment information into data to be processed.
Step S12: the edge acquisition computing equipment acquires monitoring characteristic data acquired by a plurality of Internet of things equipment accessed to the edge acquisition computing equipment, processes the monitoring characteristic data, sends the processed monitoring characteristic data to a cloud deep learning model in an AIOS operating system, and outputs a result so as to predict whether an appointed area is abnormal or not. Specifically, the step S12 includes the following steps:
the data acquisition unit acquires illumination, temperature, humidity and PM2.5 environmental data, wherein the acquired data are analog data or digital data, and the data acquisition unit transmits the acquired data to the data conversion unit after classification processing;
the data conversion unit converts the format of the data and sends the data to the data cleaning unit;
the data cleaning unit is used for cleaning data, and then the data aggregation calculation unit is used for performing aggregation calculation to form sorted data;
inputting the sorted data into a cloud deep learning model in an AIOS operating system, and outputting a result;
step S13, the wireless network interface unit of the mobile edge server collects the data related to the execution end; judging whether data related to the execution end and the cloud deep learning model are abnormal or not through a judging module; when the data are judged to be abnormal, the alarm instruction generating module generates an alarm instruction;
step S14, the wireless network interface unit sends the alarm instruction to a display unit, and the display unit executes the alarm instruction.
Further, the method further comprises the steps of: step S14: the safety protection unit realizes the authentication and verification process of single or batch edge equipment according to the public key authentication mechanism and the private key authentication mechanism.
More details can be combined with the description of fig. 1 to 3, and are not detailed here.
The embodiment of the invention has the following beneficial effects:
firstly, in the embodiment of the invention, the data conversion unit is connected with the data acquisition unit, the data conversion unit is connected with the data cleaning unit, the data cleaning unit is connected with the data aggregation calculation unit, and the data conversion unit, the data cleaning unit and the data aggregation calculation unit are cooperatively used for sorting the data acquired by the data acquisition unit, so that the data processing efficiency is improved, the calculation pressure is reduced and the data delay is reduced.
Secondly, in the embodiment of the invention, the edge acquisition computing device further comprises a safety protection unit, and the safety protection unit is provided with a public key authentication mechanism and a private key authentication mechanism, so that the single or batch edge device authentication and verification process is realized, and the safety of edge computing is improved.
In addition, in the embodiment of the invention, the wireless network interface unit comprises a serial port, an IO port, an RS485, a USB, an audio interface and a video interface, the wireless network interface unit realizes the input of external information, and the wireless network interface unit is responsible for inputting and outputting data in other forms except network data; the method has multiple data access protocols, can support multiple equipment access functions, and can further process and operate the acquired data besides the traditional routing function.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting the same, and although the present invention is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered by the claims.
Claims (10)
1. An artificial intelligence edge computing system is characterized by comprising at least one Internet of things device, an edge acquisition computing device and a mobile edge server; wherein,
the Internet of things equipment is used for collecting environmental information and converting the environmental information into the data to be processed;
the edge acquisition computing equipment is used for acquiring monitoring characteristic data acquired by a plurality of Internet of things equipment accessed to the edge acquisition computing equipment so as to predict whether the specified area is abnormal or not; the edge acquisition computing equipment comprises an AIOS operating system unit, a data acquisition unit, a data conversion unit, a data cleaning unit and a data aggregation computing unit;
the mobile edge server is used for data acquisition and data arrangement, and comprises a display unit, a storage unit, a wireless network interface unit, a judgment module and an alarm instruction generation unit.
2. The computing system of artificial intelligence edges of claim 1, wherein:
the data acquisition unit is used for acquiring illumination, temperature, humidity and PM2.5 environmental data, and the data acquired by the data acquisition unit are sent to the data conversion unit after being classified and processed.
The data conversion unit is used for receiving the data from the data acquisition unit, performing format conversion and sending the data to the data cleaning unit;
the data cleaning unit is used for cleaning the data received from the data conversion unit and sending the data to the data aggregation calculation unit;
the data aggregation calculation unit is used for performing aggregation calculation on the data received from the data cleaning unit to form sorted data; and inputting the sorted data into a cloud deep learning model in an AIOS operating system, and outputting a result.
3. The computing system of artificial intelligence edges of claim 2, wherein: the AIOS operating system unit comprises a bus and an equipment driver which are used for connecting a data acquisition unit, a data conversion unit, a data cleaning unit and a data aggregation calculation unit, and a platform layer which is used for providing a deep learning algorithm framework, a neural network model, each algorithm SDK and service; the AIOS operating system unit can support a single linux operating system and a linux + RTOS heterogeneous operating system.
4. The computing system of artificial intelligence edges of claim 3, wherein: in the edge acquisition computing device, the AIOS operating system unit further comprises an AI core processor, and the AI core processor comprises a multi-core ARM processor, a multi-core GPU, a multi-core neural network processor NNIE and a multi-core DSP.
5. The computing system of artificial intelligence edges of any of claims 1 to 4, wherein:
in the mobile edge server, the display unit is one of an LED, an LCD or an OLED, and the display unit realizes the display of the running state or configuration information of the equipment;
the storage unit comprises an onboard storage medium and an external storage medium, wherein the storage unit comprises an EMMC (embedded multi-card memory), an E2PROM (electrically erasable programmable read-only memory), a FLASHSD (flash secure digital memory), an SSD (solid state disk) and a hard disk, and the storage unit stores data written by the intelligent block chain processing unit or the routing processing unit or data required to be read;
the wireless network interface unit comprises a serial port, an IO port, an RS485, a USB, an audio interface and a video interface, the wireless network interface unit realizes the input of external information, and the wireless network interface unit is responsible for inputting and outputting data in other forms except network data.
6. The computing system of artificial intelligence edges of claim 1, wherein: the edge acquisition computing equipment further comprises a safety protection unit, wherein the safety protection unit is provided with a public key authentication mechanism and a private key authentication mechanism, and the single or batch edge equipment authentication and verification process is realized.
7. The computing system of artificial intelligence edges of claim 1, wherein: the mobile edge server also comprises a judging module and an alarm instruction generating module, wherein the wireless network interface unit collects data related to an execution end; the judging module is used for judging whether the data related to the execution end is abnormal or not; the alarm instruction generating module is used for generating an alarm instruction when the judgment result is yes; the wireless network interface unit sends an alarm instruction to a display unit, and the display unit executes the alarm instruction.
8. A method for computing artificial intelligence edges, implemented in a system according to any one of claims 1 to 7, comprising the steps of:
step S10, the mobile edge server and the edge collecting and computing equipment use a large amount of collected historical data to carry out deep learning model training, generate a cloud deep learning model and send the cloud deep learning model to the AIOS operating system unit;
step S11, the Internet of things equipment collects environmental information and converts the environmental information into data to be processed;
step S12, the edge collecting and computing device obtains monitoring characteristic data collected by a plurality of Internet of things devices connected to the edge collecting and computing device, the monitoring characteristic data is sent to a cloud deep learning model in an AIOS operating system after being processed, and a result is output to predict whether an appointed area is abnormal or not;
step S13, the wireless network interface unit of the mobile edge server collects the data related to the execution end; judging whether data related to the execution end and the cloud deep learning model are abnormal or not through a judging module; when the data are judged to be abnormal, the alarm instruction generating module generates an alarm instruction;
step S14, the wireless network interface unit sends the alarm instruction to a display unit, and the display unit executes the alarm instruction.
9. The method of claim 8, wherein the step S12 further comprises:
the data acquisition unit acquires illumination, temperature, humidity and PM2.5 environmental data, wherein the acquired data are analog data or digital data, and the data acquisition unit transmits the acquired data to the data conversion unit after classification processing;
the data conversion unit converts the format of the data and sends the data to the data cleaning unit;
the data cleaning unit is used for cleaning data, and then the data aggregation calculation unit is used for performing aggregation calculation to form sorted data;
and inputting the sorted data into a cloud deep learning model in an AIOS operating system, and outputting a result.
10. The method of claim 9, further comprising: the safety protection unit realizes the authentication and verification process of single or batch edge equipment according to the public key authentication mechanism and the private key authentication mechanism.
Priority Applications (1)
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