CN115208755A - Internet of things equipment resource-friendly feature extractor deployment method and system - Google Patents
Internet of things equipment resource-friendly feature extractor deployment method and system Download PDFInfo
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- CN115208755A CN115208755A CN202210821641.8A CN202210821641A CN115208755A CN 115208755 A CN115208755 A CN 115208755A CN 202210821641 A CN202210821641 A CN 202210821641A CN 115208755 A CN115208755 A CN 115208755A
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
- H04L41/08—Configuration management of networks or network elements
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
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- H04L67/10—Protocols in which an application is distributed across nodes in the network
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/01—Protocols
- H04L67/12—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
Abstract
The invention provides a resource-friendly feature extractor deployment method and system for equipment of the Internet of things, belonging to the technical field of the Internet of things, and the method comprises the steps of calling stored data set information and generating a feature extractor by a feature extractor generation algorithm; generating a single-capacity non-redundant multifunctional feature extractor; sending the generated single-capacity multifunctional feature extractor to the Internet of things equipment; when the data collected by the Internet of things equipment is preprocessed, a feature extractor is selected according to the current available resources to extract the principal component features of the data, and then the extracted principal component feature data is uploaded to an edge server. The invention reduces the storage resource consumption of the Internet of things equipment and improves the switching efficiency of the feature extractor; redundant feature extractors are removed, non-redundant feature extractors are nested together in a parameter sharing mode, and storage resources consumed by deploying a plurality of feature extractors are reduced; invalid feature extractor switching is reduced, and feature extractor switching efficiency is improved.
Description
Technical Field
The invention relates to the technical field of Internet of things, in particular to a resource-friendly feature extractor deployment method and system for Internet of things equipment.
Background
With the advent of the world of everything interconnection, hundreds of millions of internet-of-things devices will be connected into the network and will generate massive data. Due to the limited resources of the internet of things devices, it is difficult to provide sufficient computing resources to complete all data processing tasks. Therefore, it has become mainstream to deploy a feature extractor on the internet of things device to first extract features of the collected data, and then upload the extracted feature data to an edge server/cloud server for further processing.
The internet of things equipment has the characteristic of dynamic change of available resources, and in order to ensure that the internet of things equipment provides uninterrupted feature extraction service under the condition that the available resources of the internet of things equipment dynamically change, a plurality of feature extractors with different capacities need to be deployed on the internet of things equipment. However, the resources of the internet of things equipment are limited, and a large amount of storage resources of the internet of things equipment are occupied by deploying a plurality of feature extractors.
The mass data generated by the internet of things equipment are uploaded to the edge/cloud server, so that the core network bandwidth pressure is high, and the requirements of a user on low-delay and high-performance application service are difficult to meet. In order to reduce the data volume uploaded to the edge/cloud server by the internet of things device, and considering that the internet of things device has the characteristics of dynamic change and limitation of available resources, it has become mainstream to deploy a plurality of feature extractors with different capacities on the internet of things device at the same time. The existing method proposes to deploy a plurality of feature extractors on the internet of things device, and proposes to store a plurality of feature extractors with different capacities in a parameter sharing manner. However, the method ignores that the feature extractor deployed on the internet of things device comprises a large number of redundant sub-feature extractors, which results in waste of storage resources of the internet of things device.
Disclosure of Invention
The invention aims to provide a method and a system for deploying a feature extractor, which are resource-friendly and are oriented to Internet of things equipment, and avoid the waste of storage resources of the Internet of things equipment by removing redundant sub-feature extractors, so as to solve at least one technical problem in the background technology.
In order to achieve the purpose, the invention adopts the following technical scheme:
in one aspect, the invention provides a resource-friendly feature extractor deployment method for equipment of the internet of things, which comprises the following steps:
calling stored data set information and a feature extractor generation algorithm to generate a feature extractor;
the method comprises the steps that a single-capacity non-redundant multifunctional feature extractor is utilized on the Internet of things equipment to split the feature extractor into a plurality of non-redundant sub-feature extractors, and the non-redundant sub-feature extractors are used for generating the single-capacity non-redundant multifunctional feature extractor in a parameter sharing mode;
sending the generated single-capacity multifunctional feature extractor to the Internet of things equipment; when the internet of things equipment collects data, preprocessing the data, selecting a feature extractor to extract principal component features of the data according to current available resources of the internet of things equipment, and uploading the extracted principal component feature data to an edge server.
In a second aspect, the present invention provides an internet-of-things-oriented resource-friendly feature extractor deployment system, including:
the calling module is used for calling the stored data set information and generating a feature extractor by a feature extractor generating algorithm;
the generating module is used for splitting the feature extractor into a plurality of non-redundant sub-feature extractors by using the single-capacity non-redundant multifunctional feature extractor on the Internet of things equipment, and generating the single-capacity non-redundant multifunctional feature extractor by using the non-redundant sub-feature extractors in a parameter sharing mode;
the extraction module is used for sending the generated single-capacity multifunctional feature extractor to the Internet of things equipment; when the internet of things equipment collects data, preprocessing the data, selecting a feature extractor to extract principal component features of the data according to current available resources of the internet of things equipment, and uploading the extracted principal component feature data to an edge server.
In a third aspect, the present invention provides a non-transitory computer-readable storage medium for storing computer instructions which, when executed by a processor, implement the internet of things device resource-friendly feature extractor deployment method as described above.
In a fourth aspect, the invention provides a computer program product comprising a computer program for implementing the internet of things device resource-friendly feature extractor deployment method as described above when the computer program is run on one or more processors.
In a fifth aspect, the present invention provides an electronic device, comprising: a processor, a memory, and a computer program; wherein the processor is connected with the memory, the computer program is stored in the memory, and when the electronic device runs, the processor executes the computer program stored in the memory, so as to make the electronic device execute the instructions for implementing the feature extractor deployment method friendly to the internet of things device resources.
The invention has the beneficial effects that: the storage resource consumption of the equipment of the Internet of things is reduced, and the switching efficiency of the feature extractor is improved; by analyzing the relationship between the property of the feature extractor and the feature extractors with different capacities, redundant feature extractors are removed, and non-redundant feature extractors are nested together in a parameter sharing mode, so that the storage resources consumed by deploying a plurality of feature extractors are reduced; redundant feature extractors are removed, a large number of invalid feature extractor switching is reduced, and the feature extractor switching efficiency is improved.
Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a schematic diagram of a resource-friendly feature extraction framework for internet-of-things equipment according to an embodiment of the invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are illustrative only and should not be construed as limiting the invention.
It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
As used herein, the singular forms "a", "an", "the" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
For the convenience of understanding, the present invention will be further explained by the following embodiments with reference to the drawings, and the embodiments are not to be construed as limiting the embodiments of the present invention.
It will be appreciated by those skilled in the art that the drawings are merely schematic representations of embodiments and that the elements in the drawings are not necessarily required to practice the present invention.
Example 1
This embodiment 1 provides a feature extractor deployment system friendly to internet of things device resources, including:
the calling module is used for calling the stored data set information and generating a feature extractor by a feature extractor generating algorithm;
the generating module is used for splitting the feature extractor into a plurality of non-redundant sub-feature extractors by using the single-capacity non-redundant multifunctional feature extractor on the Internet of things equipment, and generating the single-capacity non-redundant multifunctional feature extractor by using the non-redundant sub-feature extractors in a parameter sharing mode;
the extraction module is used for sending the generated single-capacity multifunctional feature extractor to the Internet of things equipment; when the internet of things equipment collects data, preprocessing the data, selecting a feature extractor to extract principal component features of the data according to current available resources of the internet of things equipment, and uploading the extracted principal component feature data to an edge server.
In this embodiment 1, with the above system, a resource-friendly feature extractor deployment method for devices in the internet of things is implemented, including:
calling stored data set information and a feature extractor generation algorithm to generate a feature extractor;
the method comprises the steps that a single-capacity non-redundant multifunctional feature extractor is utilized on the Internet of things equipment to split the feature extractor into a plurality of non-redundant sub-feature extractors, and the non-redundant sub-feature extractors are used for generating the single-capacity non-redundant multifunctional feature extractor in a parameter sharing mode;
sending the generated single-capacity multifunctional feature extractor to the Internet of things equipment; when the data collected by the Internet of things equipment is preprocessed, a feature extractor is selected according to the current available resources to extract the principal component features of the data, and then the extracted principal component feature data is uploaded to an edge server.
Example 2
In this embodiment 2, a resource-friendly feature extractor deployment method is provided, which solves a problem that a large amount of storage resources of the internet of things device are occupied by feature extractors with different storage capacities in the prior art. As shown in fig. 1, the application flow of the method proposed in this embodiment 2 is as follows: the edge server first generates feature extractor E using a feature extractor generation algorithm, such as Principal Component Analysis (PCA) and data set information, and then runs a single-volume non-redundant multi-function feature extractor generation algorithm to generate E multi . E that the edge server will generate later multi And sending the data to the Internet of things equipment. After the data are collected by the Internet of things equipment, the data are preprocessed firstly, then a feature extractor is selected according to the current available resources of the data to extract the principal component features of the data, and the extracted principal component feature data are uploaded to an edge server to be further processed. The method described in this embodiment can be divided into an offline stage and an online stage.
An off-line stage:
feature extractor E is first generated using the data set information stored by the edge server and a feature extractor generation algorithm (PCA algorithm). Then, in order to adapt to the characteristic of dynamic change of available resources of the Internet of things equipment, the method and the system utilize the available resources of the Internet of things equipmentThe single-capacity non-redundant multifunctional feature extractor divides the feature extractor E into a plurality of non-redundant sub-feature extractors, and generates the single-capacity non-redundant multifunctional feature extractor E by sharing parameters of the non-redundant sub-feature extractors multi . Wherein, the definition of the redundant sub-feature extractor is shown in definition 1.
Definition 1 (redundant sub-feature extractor): for two sub-feature extractors S Ei And S Ej In which S is Ei ＝{E 1 ,E 2 ,...,E i },S Ej ＝{E 1 ,E 2 ,...,E j H, i ≠ j, if the sub-feature extractor S Ei And S Ej Corresponding extraction accuracy is the same, then S Ei Or S Ej Is redundant.
From definition 1, it can be determined which sub-feature extractors are redundant in the present embodiment.
In addition, a method for removing redundant sub-feature extractors is also defined in this embodiment, see definition 2.
Definition 2 (remove feature extractor): for two sub-feature extractors S Ei And S Ej Wherein S is Ei ＝{E 1 ,E 2 ,...,E i },S Ej ＝{E 1 ,E 2 ,...,E j H, if i < j, removing the sub-feature extractor S Ej 。
To this end, redundant sub-feature extractors are removed according to definition 2, and a single-content multi-functional feature extractor E is then generated in a parameter-sharing manner multi . Finally, the edge server will E multi And sending the data to the Internet of things equipment and deploying the data.
An online stage:
after the data are collected by the internet of things device, the data are preprocessed, such as graying and dimension alignment. Then, the internet of things equipment selects a proper sub-feature extractor to extract the main component features of the preprocessed data according to the current available resources (CPU resources, storage resources or network bandwidth resources). And finally, the Internet of things equipment sends the feature data of the extractor to an edge server.
In summary, the method described in this embodiment 2 removes the redundant sub-feature extractor, and deploys a plurality of non-redundant feature extractors on the terminal device in a parameter sharing manner, thereby avoiding the waste of storage resources of the internet of things device. By analyzing the composition of the feature extractors and defining and removing the redundant feature extractors, the storage resources occupied by a plurality of feature extractors are reduced and the switching efficiency among the feature extractors is improved. A plurality of non-redundant feature extractors are nested in a parameter sharing mode, and the consumption of storage resources of terminal equipment is effectively reduced.
Example 3
An embodiment 3 of the present invention provides a non-transitory computer-readable storage medium, where the non-transitory computer-readable storage medium is configured to store computer instructions, and when the computer instructions are executed by a processor, the non-transitory computer-readable storage medium implements a feature extractor deployment method that is resource-friendly for devices in the internet of things, where the method includes:
calling stored data set information and a feature extractor generation algorithm to generate a feature extractor;
the method comprises the steps that a single-capacity non-redundant multifunctional feature extractor is utilized on the Internet of things equipment to split the feature extractor into a plurality of non-redundant sub-feature extractors, and the non-redundant sub-feature extractors are used for generating the single-capacity non-redundant multifunctional feature extractor in a parameter sharing mode;
sending the generated single-capacity multifunctional feature extractor to the Internet of things equipment; when the data collected by the Internet of things equipment is preprocessed, a feature extractor is selected according to the current available resources to extract the principal component features of the data, and then the extracted principal component feature data is uploaded to an edge server.
Example 4
An embodiment 4 of the present invention provides a computer program (product), including a computer program, where the computer program is configured to implement a feature extractor deployment method that is resource-friendly for devices in the internet of things, when the computer program runs on one or more processors, and the method includes:
calling stored data set information and a feature extractor generation algorithm to generate a feature extractor;
the method comprises the steps that a single-capacity non-redundant multifunctional feature extractor is utilized on the Internet of things equipment to split the feature extractor into a plurality of non-redundant sub-feature extractors, and the non-redundant sub-feature extractors are used for generating the single-capacity non-redundant multifunctional feature extractor in a parameter sharing mode;
sending the generated single-capacity multifunctional feature extractor to the Internet of things equipment; when the internet of things equipment collects data, preprocessing the data, selecting a feature extractor to extract principal component features of the data according to current available resources of the internet of things equipment, and uploading the extracted principal component feature data to an edge server.
Example 5
An embodiment 5 of the present invention provides an electronic device, including: a processor, a memory, and a computer program; wherein the processor is connected with the memory, the computer program is stored in the memory, and when the electronic device runs, the processor executes the computer program stored in the memory, so that the electronic device executes instructions for implementing the feature extractor deployment method friendly to the resources of the device of the internet of things, the method includes:
calling stored data set information and a feature extractor generation algorithm to generate a feature extractor;
the method comprises the steps that a single-capacity non-redundant multifunctional feature extractor is utilized on the Internet of things equipment to split the feature extractor into a plurality of non-redundant sub-feature extractors, and the non-redundant sub-feature extractors are used for generating the single-capacity non-redundant multifunctional feature extractor in a parameter sharing mode;
sending the generated single-capacity multifunctional feature extractor to the Internet of things equipment; when the data collected by the Internet of things equipment is preprocessed, a feature extractor is selected according to the current available resources to extract the principal component features of the data, and then the extracted principal component feature data is uploaded to an edge server.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention 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 invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations 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.
Although the embodiments of the present invention have been described with reference to the accompanying drawings, it is not intended to limit the scope of the invention, and it should be understood by those skilled in the art that various modifications and variations can be made without inventive changes in the embodiments of the present invention.
Claims (5)
1. A feature extractor deployment method for resource-friendly equipment of the Internet of things is characterized by comprising the following steps:
calling stored data set information and a feature extractor generation algorithm to generate a feature extractor;
the method comprises the steps that a single-capacity non-redundant multifunctional feature extractor is utilized on the Internet of things equipment to split the feature extractor into a plurality of non-redundant sub-feature extractors, and the non-redundant sub-feature extractors are used for generating the single-capacity non-redundant multifunctional feature extractor in a parameter sharing mode;
sending the generated single-capacity multifunctional feature extractor to the Internet of things equipment; when the data collected by the Internet of things equipment is preprocessed, a feature extractor is selected according to the current available resources to extract the principal component features of the data, and then the extracted principal component feature data is uploaded to an edge server.
2. A resource-friendly feature extractor deployment system for equipment of the Internet of things is characterized by comprising:
the calling module is used for calling the stored data set information and generating a feature extractor by a feature extractor generating algorithm;
the generating module is used for splitting the feature extractor into a plurality of non-redundant sub-feature extractors by using the single-capacity non-redundant multifunctional feature extractor on the Internet of things equipment, and generating the single-capacity non-redundant multifunctional feature extractor by using the non-redundant sub-feature extractors in a parameter sharing mode;
the extraction module is used for sending the generated single-capacity multifunctional feature extractor to the Internet of things equipment; when the internet of things equipment collects data, preprocessing the data, selecting a feature extractor to extract principal component features of the data according to current available resources of the internet of things equipment, and uploading the extracted principal component feature data to an edge server.
3. A non-transitory computer-readable storage medium storing computer instructions which, when executed by a processor, implement the internet of things device resource-friendly feature extractor deployment method as recited in claim 1.
4. A computer program product, comprising a computer program which, when run on one or more processors, is configured to implement the internet of things device resource-friendly feature extractor deployment method of claim 1.
5. An electronic device, comprising: a processor, a memory, and a computer program; wherein a processor is connected to the memory, and a computer program is stored in the memory, and when the electronic device runs, the processor executes the computer program stored in the memory to make the electronic device execute the instructions to implement the internet-of-things device resource-friendly feature extractor deployment method as claimed in claim 1.
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