CN115146712A - Internet of things asset identification method, device, equipment and storage medium - Google Patents
Internet of things asset identification method, device, equipment and storage medium Download PDFInfo
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Abstract
The disclosure relates to an asset identification method, device, equipment and storage medium of the Internet of things, wherein the method comprises the following steps: acquiring asset information of equipment of the Internet of things, extracting keywords of the asset information, and performing vectorization processing on the asset information to generate vectorized data corresponding to the equipment of the Internet of things; matching the keywords with an asset classification library, if the keywords do not exist in the asset classification library, performing dimensionality reduction on the vectorized data, performing multi-classification on the vectorized data after the dimensionality reduction, and determining a classification result of the Internet of things equipment. According to the technical scheme, labor cost and time cost generated by the asset identification and classification of the Internet of things can be reduced, and the accuracy and intelligence of the asset identification and classification of the Internet of things are improved.
Description
Technical Field
The disclosure relates to the technical field of internet of things, and in particular relates to an internet of things asset identification method, device, equipment and storage medium.
Background
With the popularization of the internet of everything and the development of artificial intelligence technology, more and more internet of things equipment and services are exposed in the internet, the safety problem of the internet of things is concerned, and the asset identification of the internet of things is a necessary and key part in the safety of the internet of things.
The traditional asset identification method is manually dominant, labor cost and time cost are high, and the clustering algorithm is applied to the internet of things asset identification method generally combined with artificial intelligence, so that the accuracy of the internet of things asset identification method is required to be further improved.
Disclosure of Invention
In order to solve the technical problems or at least partially solve the technical problems, the present disclosure provides an internet of things asset identification method, apparatus, device and storage medium.
In a first aspect, an embodiment of the present disclosure provides an asset identification method for an internet of things, including:
acquiring asset information of the equipment of the Internet of things;
extracting keywords of the asset information, and performing vectorization processing on the asset information to generate vectorized data corresponding to the Internet of things equipment;
matching the keywords with an asset classification library, and if the keywords do not exist in the asset classification library, performing dimension reduction processing on the vectorization data;
and performing multi-classification on the vectorized data subjected to the dimensionality reduction processing, and determining a classification result of the Internet of things equipment.
Optionally, the extracting the keywords of the asset information, and performing vectorization processing on the asset information to generate vectorized data corresponding to the internet of things device includes:
extracting effective information of the asset information to generate text data;
performing word segmentation on the text data, and determining keywords of the text data through a TF-IDF algorithm; and
and vectorizing the text data to generate the vectorized data.
Optionally, after matching the keyword with the asset classification library, the method further includes:
and if the keywords exist in the asset classification library, performing tagging and unification processing on the asset information, and storing the asset information into the asset classification library.
Optionally, the multi-classifying the vectorized data after the dimensionality reduction processing, and determining a classification result of the internet of things device, includes:
inputting the vectorized data subjected to the dimensionality reduction into a Boosting algorithm model for processing, and determining a category corresponding to the Internet of things equipment;
and if the category is the asset category contained in the asset classification library, performing tagging and unification processing on the asset information, and storing the asset information into the asset classification library.
Optionally, after determining the category corresponding to the internet of things device, the method further includes:
determining that the category is not target data of the asset category contained in the asset classification library in the vectorization data after the dimension reduction processing;
clustering the target data through a clustering algorithm to generate a clustering result;
and calibrating the clustering result to store the clustering result in the asset classification library.
Optionally, the method further comprises: and updating the asset classification library according to the classification result of the Internet of things equipment.
Optionally, the obtaining asset information of the internet of things device includes:
sending a network protocol communication request to detect the equipment of the Internet of things;
and responding to the returned response information, and determining the asset information of the equipment of the Internet of things.
In a second aspect, an embodiment of the present disclosure provides an internet of things asset identification device, including:
the acquisition module is used for acquiring asset information of the Internet of things equipment;
the processing module is used for extracting keywords of the asset information, vectorizing the asset information and generating vectorized data corresponding to the Internet of things equipment;
the matching module is used for matching the keywords with an asset classification library, and if the keywords do not exist in the asset classification library, performing dimension reduction processing on the vectorized data;
and the classification module is used for carrying out multi-classification on the vectorization data subjected to the dimensionality reduction processing and determining a classification result of the Internet of things equipment.
In a third aspect, an embodiment of the present disclosure provides an electronic device, including: a processor; a memory for storing the processor-executable instructions; the processor is configured to read the executable instructions from the memory and execute the instructions to implement the method for identifying assets in the internet of things according to the first aspect.
In a fourth aspect, the disclosed embodiments provide a computer-readable storage medium, where the storage medium stores a computer program, and the computer program, when executed by a processor, implements the method for identifying an asset in the internet of things according to the first aspect.
Compared with the prior art, the technical scheme provided by the embodiment of the disclosure has the following advantages: the method comprises the steps of extracting keywords of asset information by obtaining the asset information of the equipment of the Internet of things, and carrying out vectorization processing on the asset information to generate vectorization data corresponding to the equipment of the Internet of things; matching the keywords with an asset classification library, if the keywords do not exist in the asset classification library, performing dimensionality reduction on the vectorized data, performing multi-classification on the vectorized data after the dimensionality reduction, and determining a classification result of the Internet of things equipment. Therefore, the Internet of things asset identification classification is realized based on a natural language processing technology, a dimensionality reduction algorithm and an integrated learning multi-classification algorithm, the labor cost and the time cost generated by the Internet of things asset identification classification are reduced, and the accuracy and the intelligence of the Internet of things asset identification classification are improved.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and together with the description, serve to explain the principles of the disclosure.
In order to more clearly illustrate the embodiments or technical solutions in the prior art of the present disclosure, the drawings used in the description of the embodiments or prior art will be briefly described below, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without inventive exercise.
Fig. 1 is a schematic flowchart of an asset identification method of the internet of things according to an embodiment of the present disclosure;
fig. 2 is a schematic diagram of another method for identifying assets of the internet of things according to the embodiment of the disclosure;
fig. 3 is a schematic structural diagram of an asset identification device of the internet of things according to an embodiment of the present disclosure;
fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present disclosure.
Detailed Description
In order that the above objects, features and advantages of the present disclosure may be more clearly understood, aspects of the present disclosure will be further described below. It should be noted that the embodiments and features of the embodiments of the present disclosure may be combined with each other without conflict.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present disclosure, but the present disclosure may be practiced otherwise than as described herein; it is to be understood that the embodiments disclosed in the specification are only a few embodiments of the present disclosure, and not all embodiments.
Fig. 1 is a schematic flow diagram of an internet of things asset identification method according to an embodiment of the present disclosure, where the method according to the embodiment of the present disclosure may be executed by an internet of things asset identification device, and the device may be implemented by software and/or hardware and may be integrated on any electronic device with computing capability, such as a user terminal, e.g., a smart phone, a tablet computer, and the like.
As shown in fig. 1, an asset identification method of an internet of things provided by an embodiment of the present disclosure may include:
The method can be applied to asset identification and classification scenes of the Internet of things equipment. The internet of things equipment comprises but is not limited to smart home equipment, smart cars, sensor equipment, medical equipment and the like.
In this embodiment, a network protocol communication request may be sent to detect the internet of things device, and then, in response to the returned response information, asset information of the internet of things device is determined. Specifically, an active identification module is arranged, the active identification module detects assets on the internet in a mode of actively sending a network protocol communication request, and collects asset information of the internet of things equipment through response information returned by the request, the technology applied by the active identification module includes but is not limited to Nmap, and the acquired asset information includes multi-dimensional data.
And 102, extracting keywords of asset information, performing vectorization processing on the asset information, and generating vectorized data corresponding to the Internet of things equipment.
In this embodiment, after the asset information of the internet of things device is acquired, vectorization processing is performed on the collected asset information, including extracting effective information such as attributes and tag contents in a message, and then performing word segmentation, keyword extraction and text data vectorization processing on the effective information.
As an example, the extracting keywords of the asset information, and performing vectorization processing on the asset information to generate vectorized data corresponding to the internet of things device includes: extracting effective information from the asset information to generate text data; performing word segmentation on the text data, and determining keywords of the text data through a TF-IDF (term frequency-inverse text frequency index) algorithm; and vectorizing the text data to generate the vectorized data.
In this example, effective information related to asset identification is extracted by a natural language processing technique to generate corresponding text data, and then keywords of the text data are extracted to perform matching according to the keywords. For example, the keyword may be a business name, a device model number, an asset type, and the like.
In the field of natural language processing, text is represented as a vector capable of expressing text semantics through vectorization processing of text data, the text data vectorization processing mode includes but is not limited to a statistical-based method, a neural network-based method, and the like, and for example, vectorization data of text data may be generated by using a vectorization algorithm word2 vec.
And 103, matching the keywords with the asset classification library, and if the keywords do not exist in the asset classification library, performing dimension reduction on the quantitative data.
In this embodiment, the asset classification library includes a plurality of keywords, and the keywords of the text data are matched with the keywords in the asset classification library, and in an embodiment of the present disclosure, after the keywords are matched with the asset classification library, the method further includes: and if the keywords exist in the asset classification library, performing tagging and unification processing on the asset information, and storing the asset information into the asset classification library. The method comprises the steps of performing tagging and unification processing on text data to achieve inventory processing of Internet of things equipment, as an example, determining keywords of the text data to comprise an XX enterprise for the first Internet of things equipment, matching the XX enterprise with an asset classification library, performing inventory processing on the first Internet of things equipment if the XX enterprise also exists in the asset classification library, performing tagging and unification processing on the text data, and updating the asset classification library according to the processed text data.
In this embodiment, in the case that the keyword does not exist in the asset classification library, dimension reduction processing is performed on the vector quantization data. In an internet of things equipment asset identification scene, as the internet of things asset information dimension increases, a dimension disaster is caused, and the dimension disaster refers to a phenomenon that, in a problem related to vector calculation, the calculation amount is exponentially multiplied with the increase of the dimension, and in the case of the dimension disaster, a clustering algorithm increases a large amount of calculation and brings a problem of data accuracy.
And 104, performing multi-classification on the vectorization data subjected to the dimensionality reduction processing, and determining a classification result of the Internet of things equipment.
In this embodiment, the vectorized data after the dimensionality reduction is used as an input of the classification model, and the classification output by the classification model includes the asset type in the current asset classification library, so as to determine the classification result of the internet of things device, optionally, the internet of things device is subjected to warehousing processing according to the classification result of the internet of things device, and the asset classification library is updated.
As an example, the multi-classifying the vectorized data after the dimension reduction processing, and determining the classification result of the internet of things device includes: inputting the vectorized data subjected to the dimensionality reduction processing into a Boosting algorithm model for processing, and determining the category corresponding to the Internet of things equipment; and if the category is the asset category contained in the asset classification library, performing tagging and unification treatment on the asset information, and storing the asset information into the asset classification library.
In this example, the classification model is trained according to sample data, the output of the classification model is the asset type, the input is vectorization data, and optionally, the sample data is constructed according to the vectorization data of the internet of things devices stored in the asset classification library and the asset type to train the classification model.
In this example, the categories output by the model include a first asset category included in the asset classification library and a second asset category not included in the asset classification library, and optionally, after determining the category corresponding to the internet of things device, if the category corresponding to the internet of things device is the first asset category, storing the Internet of things equipment, and if the category corresponding to the Internet of things equipment is the second asset category, further calibrating the Internet of things equipment, namely determining target data of which the category is not the asset category contained in the asset classification library in vectorized data after dimension reduction processing; clustering the target data through a clustering algorithm to generate a clustering result; and calibrating the clustering result to store the clustering result in the asset classification library. The clustering algorithm includes, but is not limited to, a KMeans algorithm, and clusters the partial data to obtain asset type information of different types, and then, based on the clustered asset type information, the new asset type or the existing asset type is determined by calibration, and based on the calibration result, the asset classification library is updated.
For example, referring to fig. 2, an active recognition module, a vectorization module, a matching module, a dimension reduction module, an integrated classification module, a clustering module, and a manual intervention module are provided. The method comprises the steps of actively discovering a model through an Internet of things gateway, actively detecting Internet of things asset equipment in interconnection through Nmap, and carrying out preliminary data cleaning on detected response data. Vectorizing any Internet of things equipment, extracting keywords from the data by using a TF-IDF algorithm, matching the extracted keywords in a current asset classification library, and storing the current Internet of things equipment and updating the asset classification library if the extracted keywords are matched; if the data is not matched, performing data dimension reduction processing on the vectorized data, processing the vectorized data after dimension reduction by using an integrated learning Boosting algorithm, comparing the obtained classification result with the categories in the asset classification library, further clustering the data which are not successfully compared, and displaying the processed result on a gateway interface including the classified and unclassified data. Further, on the gateway interface, classified assets are verified based on further information provided by the customer, unclassified assets are recalibrated, and a library process and asset class library updates, including but not limited to, updates of keywords and asset types in the library, are performed. The above steps are repeatedly executed, and the accuracy and the automation of the asset classification of the Internet of things are improved.
According to the technical scheme of the embodiment of the disclosure, asset information of the equipment of the Internet of things is obtained, keywords of the asset information are extracted, vectorization processing is carried out on the asset information, and vectorization data corresponding to the equipment of the Internet of things are generated; matching the keywords with an asset classification library, if the keywords do not exist in the asset classification library, performing dimensionality reduction on the vectorized data, performing multi-classification on the vectorized data after the dimensionality reduction, and determining a classification result of the Internet of things equipment. Therefore, asset identification and classification are realized based on a natural language processing technology, a dimensionality reduction algorithm and an integrated learning multi-classification algorithm, labor cost and time cost generated by asset identification and classification of the Internet of things are reduced, and accuracy and intelligence of asset identification and classification of the Internet of things are improved.
Fig. 3 is a schematic structural diagram of an internet of things asset identification device provided in an embodiment of the present disclosure, and as shown in fig. 3, the internet of things asset identification device includes: an acquisition module 31, a processing module 32, a matching module 33, and a classification module 34.
The acquiring module 31 is configured to acquire asset information of the internet of things device;
the processing module 32 is configured to extract keywords of the asset information, perform vectorization processing on the asset information, and generate vectorized data corresponding to the internet of things device;
a matching module 33, configured to match the keyword with an asset classification library, and if the keyword does not exist in the asset classification library, perform dimension reduction processing on the vectorized data;
the classification module 34 is configured to perform multi-classification on the vectorized data after the dimension reduction processing, and determine a classification result of the internet of things device.
In an embodiment of the present disclosure, the processing module 32 is specifically configured to: extracting effective information of the asset information to generate text data; performing word segmentation on the text data, and determining keywords of the text data through a TF-IDF algorithm; and vectorizing the text data to generate the vectorized data.
In one embodiment of the present disclosure, the apparatus further comprises: and the first library storage module is used for performing labeling and unification processing on the asset information and storing the asset information into the asset classification library if the keywords exist in the asset classification library.
In one embodiment of the present disclosure, classification module 34 is specifically configured to: inputting the vectorized data subjected to the dimensionality reduction processing into a Boosting algorithm model for processing, and determining the category corresponding to the Internet of things equipment; and if the category is the asset category contained in the asset classification library, performing tagging and unification treatment on the asset information, and storing the asset information into the asset classification library.
In an embodiment of the present disclosure, the classification module 34 is specifically configured to: determining that the category is not target data of the asset category contained in the asset classification library in the vectorization data after the dimension reduction processing; clustering the target data through a clustering algorithm to generate a clustering result; and calibrating the clustering result to store the clustering result in the asset classification library.
In one embodiment of the present disclosure, the apparatus further comprises: and the updating module is used for updating the asset classification library according to the classification result of the Internet of things equipment.
In an embodiment of the present disclosure, the obtaining module 31 is specifically configured to: sending a network protocol communication request to detect the equipment of the Internet of things; and responding to the returned response information, and determining the asset information of the equipment of the Internet of things.
The asset identification device of the internet of things provided by the embodiment of the disclosure can execute any asset identification method of the internet of things provided by the embodiment of the disclosure, and has corresponding functional modules and beneficial effects of the execution method. Reference may be made to the description of any method embodiment of the disclosure that may not be described in detail in the embodiments of the apparatus of the disclosure.
Fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present disclosure. As shown in fig. 4, the electronic device 600 includes one or more processors 601 and memory 602.
The processor 601 may be a Central Processing Unit (CPU) or other form of processing unit having data processing capabilities and/or instruction execution capabilities, and may control other components in the electronic device 600 to perform desired functions.
In one example, the electronic device 600 may further include: an input device 603 and an output device 604, which are interconnected by a bus system and/or other form of connection mechanism (not shown). The input device 603 may also include, for example, a keyboard, a mouse, and the like. The output device 604 may output various information including the determined distance information, direction information, and the like to the outside. The output devices 604 may include, for example, a display, speakers, a printer, and a communication network and its connected remote output devices, among others.
Of course, for simplicity, only some of the components of the electronic device 600 relevant to the present disclosure are shown in fig. 4, and components such as buses, input/output interfaces, and the like are omitted. In addition, electronic device 600 may include any other suitable components depending on the particular application.
In addition to the methods and apparatus described above, embodiments of the present disclosure may also be a computer program product comprising computer program instructions that, when executed by a processor, cause the processor to perform any of the methods provided by embodiments of the present disclosure.
The computer program product may write program code for performing operations for embodiments of the present disclosure in any combination of one or more programming languages, including an object oriented programming language such as Java, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server.
Furthermore, embodiments of the present disclosure may also be a computer readable storage medium having stored thereon computer program instructions that, when executed by a processor, cause the processor to perform any of the methods provided by embodiments of the present disclosure.
A computer-readable storage medium may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may include, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
It is noted that, in this document, relational terms such as "first" and "second," and the like, are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrases "comprising a," "8230," "8230," or "comprising" does not exclude the presence of additional like elements in a process, method, article, or apparatus that comprises the element.
The foregoing are merely exemplary embodiments of the present disclosure, which enable those skilled in the art to understand or practice the present disclosure. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the disclosure. Thus, the present disclosure is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. An asset identification method of the Internet of things is characterized by comprising the following steps:
acquiring asset information of the equipment of the Internet of things;
extracting keywords of the asset information, and performing vectorization processing on the asset information to generate vectorized data corresponding to the Internet of things equipment;
matching the keywords with an asset classification library, and if the keywords do not exist in the asset classification library, performing dimension reduction processing on the vectorization data;
and performing multi-classification on the vectorized data subjected to the dimensionality reduction processing, and determining a classification result of the Internet of things equipment.
2. The method of claim 1, wherein the extracting keywords of the asset information and vectorizing the asset information to generate vectorized data corresponding to the internet of things device comprises:
extracting effective information from the asset information to generate text data;
performing word segmentation on the text data, and determining keywords of the text data through a TF-IDF algorithm; and
and vectorizing the text data to generate the vectorized data.
3. The method of claim 1, after matching the keyword to an asset classification library, further comprising:
and if the keywords exist in the asset classification library, performing tagging and unification processing on the asset information, and storing the asset information into the asset classification library.
4. The method of claim 1, wherein the multi-classifying the vectorized data after the dimension reduction processing to determine the classification result of the internet of things device comprises:
inputting the vectorized data subjected to the dimensionality reduction processing into a Boosting algorithm model for processing, and determining the category corresponding to the Internet of things equipment;
and if the category is the asset category contained in the asset classification library, performing tagging and unification treatment on the asset information, and storing the asset information into the asset classification library.
5. The method of claim 4, after determining the class to which the Internet of things device corresponds, further comprising:
determining that the category is not target data of an asset category contained in the asset classification library in the vectorization data after the dimension reduction processing;
clustering the target data through a clustering algorithm to generate a clustering result;
and calibrating the clustering result to store the clustering result in the asset classification library.
6. The method of any one of claims 1-5, further comprising:
and updating the asset classification library according to the classification result of the Internet of things equipment.
7. The method of claim 1, wherein the obtaining asset information of the internet of things device comprises:
sending a network protocol communication request to detect the equipment of the Internet of things;
and responding to the returned response information, and determining the asset information of the equipment of the Internet of things.
8. An internet of things asset identification device, comprising:
the acquisition module is used for acquiring asset information of the Internet of things equipment;
the processing module is used for extracting keywords of the asset information, vectorizing the asset information and generating vectorized data corresponding to the Internet of things equipment;
the matching module is used for matching the keywords with an asset classification library, and if the keywords do not exist in the asset classification library, performing dimension reduction processing on the vectorized data;
and the classification module is used for performing multi-classification on the vectorized data subjected to the dimensionality reduction processing and determining a classification result of the Internet of things equipment.
9. An electronic device, comprising:
a processor;
a memory for storing the processor-executable instructions;
the processor is configured to read the executable instructions from the memory and execute the instructions to implement the method for identifying assets in the internet of things as claimed in any one of the claims 1 to 7.
10. A computer-readable storage medium, wherein the storage medium stores a computer program, and the computer program when executed by a processor implements the method for identifying assets in the internet of things as claimed in any one of claims 1 to 7.
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| CN117194947A (en) * | 2023-08-16 | 2023-12-08 | 惠州市庆展科技有限公司 | Smart home equipment characteristic determining method and system |
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