CN115146712B - 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 internet of things asset identification method, device, equipment and storage medium, wherein the method comprises the following steps: acquiring asset information of Internet of things equipment, extracting keywords of the asset information, and carrying out vectorization processing on the asset information to generate vectorization data corresponding to the Internet of things equipment; and matching the keywords with an asset classification library, if the keywords do not exist in the asset classification library, performing dimension reduction on the vectorized data, performing multi-classification on the vectorized data subjected to dimension reduction, and determining classification results of the Internet of things equipment. According to the technical scheme, labor cost and time cost generated by the identification and classification of the assets of the Internet of things can be reduced, and accuracy and intelligence of the identification and classification of the assets of the Internet of things are improved.
Description
Technical Field
The disclosure relates to the technical field of internet of things, in particular to an internet of things asset identification method, device, equipment and storage medium.
Background
With the popularization of internet of things and the development of artificial intelligence technology, more and more internet of things equipment and services are exposed in the internet, and the internet of things safety problem is also of great concern, so that internet of things asset identification is a necessary and key ring in internet of things safety.
The traditional asset identification method is manually dominant, the labor cost and the time cost are high, and the clustering algorithm is applied to the asset identification method of the internet of things by combining artificial intelligence, so that the accuracy of the asset identification method is required to be further improved.
Disclosure of Invention
In order to solve the technical problems described above or at least partially solve the technical problems described above, the present disclosure provides an asset identification method, device, equipment and storage medium for the internet of things.
In a first aspect, an embodiment of the present disclosure provides an asset identification method for the internet of things, including:
acquiring asset information of the Internet of things equipment;
extracting keywords of the asset information, and carrying out vectorization processing on the asset information to generate vectorization data corresponding to the Internet of things equipment;
matching the keywords with an asset classification library, and performing dimension reduction processing on the vectorized data if the keywords do not exist in the asset classification library;
and carrying out multi-classification on the vectorized data after the dimension reduction processing, and determining a classification result of the Internet of things equipment.
Optionally, the extracting the keyword 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;
word segmentation is carried out on the text data, and keywords of the text data are determined through a TF-IDF algorithm; and
and carrying out vectorization processing on the text data to generate vectorized data.
Optionally, after matching the keyword with the asset classification library, further comprising:
and if the keyword exists in the asset classification library, labeling and unifying the asset information, and storing the asset information into the asset classification library.
Optionally, the multi-classifying the vectorized data after the dimension reduction processing to determine a classification result of the internet of things device includes:
inputting the vectorized data subjected to the dimension 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, labeling and unifying 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 the target data of the asset category contained in the asset classification library in the vectorized data after the dimension reduction processing;
clustering the target data by a clustering algorithm to generate a clustering result;
and calibrating the clustering result to store the clustering result into 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 acquiring asset information of the internet of things device includes:
sending a network protocol communication request to detect the Internet of things equipment;
and responding to the returned response information, and determining the asset information of the Internet of things equipment.
In a second aspect, an embodiment of the present disclosure provides an asset identification device for internet of things, 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, carrying out vectorization processing on the asset information and generating vectorization data corresponding to the Internet of things equipment;
the matching module is used for matching the keywords with an asset classification library, and performing dimension reduction processing on the vectorized data if the keywords do not exist in the asset classification library;
and the classification module is used for carrying out multi-classification on the vectorized data after the dimension 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 instruction from the memory, and execute the instruction to implement the method for identifying an asset of the internet of things according to the first aspect.
In a fourth aspect, an embodiment of the present disclosure provides a computer readable storage medium, where a computer program is stored, where the computer program is executed by a processor to implement the method for identifying assets 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: extracting keywords of the asset information by acquiring the asset information of the Internet of things equipment, and carrying out vectorization processing on the asset information to generate vectorization data corresponding to the Internet of things equipment; and matching the keywords with an asset classification library, if the keywords do not exist in the asset classification library, performing dimension reduction on the vectorized data, performing multi-classification on the vectorized data subjected to dimension reduction, and determining classification results of the Internet of things equipment. Therefore, the method realizes the identification and classification of the assets of the Internet of things based on the natural language processing technology, the dimension reduction algorithm and the integrated learning multi-classification algorithm, reduces the labor cost and the time cost generated by the identification and classification of the assets of the Internet of things, and improves the accuracy and the intelligence of the identification and classification of the assets of the Internet of things.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the disclosure and together with the description, serve to explain the principles of the disclosure.
In order to more clearly illustrate the embodiments of the present disclosure or the solutions in the prior art, the drawings that are required for the description of the embodiments or the prior art will be briefly described below, and it will be obvious to those skilled in the art that other drawings can be obtained from these drawings without inventive effort.
Fig. 1 is a schematic flow chart of an asset identification method of the internet of things according to an embodiment of the disclosure;
fig. 2 is a schematic diagram of another method for identifying assets of the internet of things according to an 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 disclosure.
Detailed Description
In order that the above objects, features and advantages of the present disclosure may be more clearly understood, a further description of aspects of the present disclosure will be provided below. It should be noted that, without conflict, the embodiments of the present disclosure and features in the embodiments may be combined with each other.
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 will be apparent that the embodiments in the specification are only some, but not all, embodiments of the disclosure.
Fig. 1 is a flow chart of an asset identification method of the internet of things, which is provided by an embodiment of the present disclosure, where the method provided by the embodiment of the present disclosure may be performed by an asset identification device of the internet of things, and the device may be implemented by using software and/or hardware and may be integrated on any electronic device having computing capability, for example, a user terminal such as a smart phone, a tablet computer, and the like.
As shown in fig. 1, the method for identifying the assets of the internet of things provided by the embodiment of the disclosure may include:
and step 101, acquiring asset information of the Internet of things equipment.
The method of the embodiment of the disclosure can be applied to asset identification classification scenes of the Internet of things equipment. The internet of things equipment comprises, but is not limited to, intelligent home equipment, intelligent automobiles, 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 further, in response to the returned response information, determine asset information of the internet of things device. Specifically, an active identification module is provided, the active identification module detects the assets on the internet by actively sending a network protocol communication request, and the asset information of the equipment of the internet of things is collected by requesting returned response information, and the active identification module uses technologies including, but not limited to, nmap and the like, and the acquired asset information comprises multidimensional data.
And 102, extracting keywords of the asset information, and carrying out vectorization processing on the asset information to generate vectorization data corresponding to the Internet of things equipment.
In this embodiment, after the asset information of the internet of things device is obtained, vectorization processing is performed on the collected asset information, including extracting effective information such as attributes and tag content in a message, and then, vectorization processing of word segmentation, keyword extraction and text data is performed on the effective information.
As an example, the extracting the keyword 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; word segmentation is carried out on the text data, and keywords of the text data are determined through a TF-IDF (term frequency-inverse text frequency index) algorithm; and carrying out vectorization processing on the text data to generate vectorization data.
In this example, effective information related to asset recognition is extracted by natural language processing technology to generate corresponding text data, and further, keywords of the text data are extracted to perform matching according to the keywords. For example, the keywords may be business name, device model number, asset type, etc.
In the field of natural language processing, text is represented as a vector capable of expressing text semantics through vectorization processing of text data, and a text data vectorization processing mode includes, but is not limited to, a statistical-based method, a neural network-based method, and the like, for example, vectorization data of text data may be generated by using a vectorization algorithm word2 vec.
And step 103, matching the keywords with the asset classification library, and performing dimension reduction processing on the vector data if the keywords do not exist in the asset classification library.
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 one embodiment of the present disclosure, after the keywords are matched with the asset classification library, the method further includes: and if the keyword exists in the asset classification library, labeling and unifying the asset information, and storing the asset information into the asset classification library. The method comprises the steps of performing labeling and unification processing on text data to realize repository processing of the Internet of things equipment, determining that keywords of the text data comprise XX enterprises for the first Internet of things equipment, matching the XX enterprises of the keywords with an asset classification library, performing the repository processing on the first Internet of things equipment if the XX enterprises of the keywords exist in the asset classification library, and updating the asset classification library according to the processed text data by performing labeling and unification processing on the text data.
In this embodiment, in the case where the keyword does not exist in the asset classification library, the vector data is subjected to the dimension reduction processing. In the case of the internet of things equipment asset identification scene, as the dimension of the internet of things asset information increases, a dimension disaster is caused, the dimension disaster refers to a phenomenon that the calculated amount increases exponentially with the increase of the dimension in the problem of vector calculation, and under the condition of the dimension disaster, a clustering algorithm increases a large amount of calculation and brings about the problem of data accuracy, so in the embodiment, firstly, dimension reduction processing is performed on the vectorized data, specifically, after keywords corresponding to a plurality of internet of things equipment are matched with an asset classification library, at least one internet of things equipment with the keywords are determined to be absent in the asset classification library, and the dimension reduction processing is performed on the vectorized data corresponding to the at least one internet of things equipment by adopting data dimension reduction technology such as PCA (Principal Component Analysis).
And 104, performing multi-classification on the vectorized data after the dimension reduction processing, and determining a classification result of the Internet of things equipment.
In this embodiment, the vectorized data after dimension reduction is used as input of a classification model, and the category 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, performing repository processing on the internet of things device according to the classification result of the internet of things device, and updating the asset classification library.
As an example, performing multi-classification on the vectorized data after the dimension reduction processing to determine a classification result of the internet of things device, including: inputting the vectorized data subjected to the dimension 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, labeling and unifying the asset information, and storing the asset information into the asset classification library.
In this example, the classification model is trained from sample data, the output of the classification model is asset type, the input is vectorized data, and optionally, the sample data is constructed from vectorized data and asset types of the internet of things devices that have been stored in the asset classification library 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, 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, performing repository processing on the internet of things device, and if the category corresponding to the internet of things device is the second asset category, further calibrating the internet of things device, that is, determining that the category is not the target data of the asset category included in the asset classification library in the vectorized data after the dimension reduction processing; clustering the target data by a clustering algorithm to generate a clustering result; and calibrating the clustering result to store the clustering result into the asset classification library. The clustering algorithm includes but is not limited to KMeans algorithm, different kinds of asset type information is obtained by clustering the part of data, and then calibration is performed based on the clustered asset type information to determine a new asset type or an existing asset type, and an asset classification library is updated based on a calibration result.
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 detecting internet of things asset equipment in interconnection through an internet of things gateway active discovery model through an Nmap, and performing primary data cleaning on detected response data. For any Internet of things equipment, carrying out vectorization processing on data, extracting keywords from the data by utilizing a TF-IDF algorithm, then utilizing the extracted keywords to match the data in a current asset classification library, and if the extracted keywords are matched, carrying out repository processing on the current Internet of things equipment and updating the asset classification library; if the data is not matched, carrying out data dimension reduction on the vectorized data, processing the dimension reduced vectorized data by using an ensemble learning Boosting algorithm, comparing the obtained classification result with the classes in the asset classification library, further clustering the data which are not successfully compared, and displaying the processed result on a gateway interface, wherein the processed result comprises classified and unclassified data. Further, on the gateway interface, the classified assets are verified based on further information provided by the customer, unclassified assets are recalibrated, and repository processing and asset classification library updates are performed, including but not limited to in-library keyword and asset type updates. The above steps are repeatedly executed, so that the accuracy and the automation of the classification of the assets of the Internet of things are improved.
According to the technical scheme of the embodiment of the disclosure, the key words of the asset information are extracted by acquiring the asset information of the Internet of things equipment, and the asset information is subjected to vectorization processing to generate vectorization data corresponding to the Internet of things equipment; and matching the keywords with an asset classification library, if the keywords do not exist in the asset classification library, performing dimension reduction on the vectorized data, performing multi-classification on the vectorized data subjected to dimension reduction, and determining classification results of the Internet of things equipment. Therefore, the asset identification classification is realized based on the natural language processing technology, the dimension reduction algorithm and the integrated learning multi-classification algorithm, the labor cost and the time cost generated by the asset identification classification of the Internet of things are reduced, and the accuracy and the intelligence of the asset identification classification of the Internet of things are improved.
Fig. 3 is a schematic structural diagram of an asset identification device for internet of things, provided in an embodiment of the present disclosure, as shown in fig. 3, the asset identification device for internet of things includes: the device comprises 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 a keyword of the asset information, and perform vectorization processing on the asset information to generate vectorized data corresponding to the internet of things device;
the matching module 33 is 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;
and the classification module 34 is used for carrying out multi-classification on the vectorized data after the dimension reduction processing to determine a classification result of the internet of things equipment.
In one embodiment of the present disclosure, the processing module 32 is specifically configured to: extracting effective information of the asset information to generate text data; word segmentation is carried out on the text data, and keywords of the text data are determined through a TF-IDF algorithm; and carrying out vectorization processing on the text data to generate vectorization data.
In one embodiment of the present disclosure, the apparatus further comprises: and the first repository module is used for labeling and unifying the asset information if the keywords exist in the asset classification library, and storing the asset information into 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 dimension 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, labeling and unifying the asset information, and storing the asset information into the asset classification library.
In one embodiment of the present disclosure, classification module 34 is specifically configured to: determining that the category is not the target data of the asset category contained in the asset classification library in the vectorized data after the dimension reduction processing; clustering the target data by a clustering algorithm to generate a clustering result; and calibrating the clustering result to store the clustering result into 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 one embodiment of the present disclosure, the obtaining module 31 is specifically configured to: sending a network protocol communication request to detect the Internet of things equipment; and responding to the returned response information, and determining the asset information of the Internet of things equipment.
The internet of things asset identification device provided by the embodiment of the disclosure can execute any internet of things asset identification method provided by the embodiment of the disclosure, and has the corresponding functional modules and beneficial effects of the execution method. Details of the embodiments of the apparatus of the present disclosure that are not described in detail may refer to descriptions of any of the embodiments of the method of the present disclosure.
Fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the 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 and/or instruction execution capabilities and may control other components in the electronic device 600 to perform desired functions.
The memory 602 may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and/or non-volatile memory. Volatile memory can include, for example, random Access Memory (RAM) and/or cache memory (cache) and the like. The non-volatile memory may include, for example, read Only Memory (ROM), hard disk, flash memory, and the like. One or more computer program instructions may be stored on a computer readable storage medium and the processor 601 may execute the program instructions to implement the methods of the embodiments of the present disclosure above and/or other desired functions. Various contents such as an input signal, a signal component, a noise component, and the like may also be stored in the computer-readable storage medium.
In one example, the electronic device 600 may further include: input device 603 and output device 604, which are interconnected by a bus system and/or other form of connection mechanism (not shown). In addition, the input device 603 may also include, for example, a keyboard, a mouse, and the like. The output device 604 may output various information to the outside, including the determined distance information, direction information, and the like. The output means 604 may include, for example, a display, speakers, a printer, and a communication network and remote output devices connected thereto, etc.
Of course, only some of the components of the electronic device 600 that are relevant to the present disclosure are shown in fig. 4 for simplicity, components such as buses, input/output interfaces, etc. are omitted. In addition, the 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 which, when executed by a processor, cause the processor to perform any of the methods provided by the embodiments of the present disclosure.
The computer program product may be written 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, 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 which, when executed by a processor, cause the processor to perform any of the methods provided by the 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. The readable storage medium may include, for example, but is 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 would include the following: an electrical connection having one or more wires, a portable disk, a hard disk, random Access Memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
It should be 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. Moreover, 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 phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The foregoing is merely a specific embodiment of the disclosure to enable one skilled in the art to understand or practice the 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 and described herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (7)
1. The method for identifying the assets of the Internet of things is characterized by comprising the following steps of:
acquiring asset information of the Internet of things equipment;
extracting keywords of the asset information, and carrying out vectorization processing on the asset information to generate vectorization data corresponding to the Internet of things equipment;
matching the keywords with an asset classification library, and if the keywords exist in the asset classification library, labeling and unifying the asset information and storing the asset information into the asset classification library;
if the keyword does not exist in the asset classification library, performing dimension reduction on the vectorized data;
performing multi-classification on the vectorized data after the dimension reduction processing, and determining a classification result of the Internet of things equipment; the multi-classification of the vectorized data after the dimension reduction processing, and the determination of the classification result of the internet of things device comprise the following steps: inputting the vectorized data subjected to the dimension reduction processing into a Boosting algorithm model for processing, and determining the category corresponding to the Internet of things equipment; if the category is the asset category contained in the asset classification library, labeling and unifying the asset information, and storing the asset information into the asset classification library; determining that the category is not the target data of the asset category contained in the asset classification library in the vectorized data after the dimension reduction processing; clustering the target data by a clustering algorithm to generate a clustering result; and calibrating the clustering result to store the clustering result into the asset classification library.
2. The method of claim 1, wherein the extracting the key words of the asset information and vectorizing the asset information to generate vectorized data corresponding to the internet of things device comprises:
extracting effective information of the asset information to generate text data;
word segmentation is carried out on the text data, and keywords of the text data are determined through a TF-IDF algorithm; and
and carrying out vectorization processing on the text data to generate vectorized data.
3. The method of claim 1 or 2, further comprising:
and updating the asset classification library according to the classification result of the Internet of things equipment.
4. The method of claim 1, wherein the obtaining asset information for the internet of things device comprises:
sending a network protocol communication request to detect the Internet of things equipment;
and responding to the returned response information, and determining the asset information of the Internet of things equipment.
5. The utility model provides an thing networking asset identification device which characterized in that includes:
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, carrying out vectorization processing on the asset information and generating vectorization data corresponding to the Internet of things equipment;
the matching module is used for matching the keywords with an asset classification library, and performing dimension reduction processing on the vectorized data if the keywords do not exist in the asset classification library;
the first repository module is used for labeling and unifying the asset information if the keywords exist in the asset classification library, and storing the asset information into the asset classification library;
the classification module is used for inputting the vectorized data subjected to the dimension reduction processing into a Boosting algorithm model for processing, and determining the class corresponding to the Internet of things equipment; if the category is the asset category contained in the asset classification library, labeling and unifying the asset information, and storing the asset information into the asset classification library; determining that the category is not the target data of the asset category contained in the asset classification library in the vectorized data after the dimension reduction processing; clustering the target data by a clustering algorithm to generate a clustering result; and calibrating the clustering result to store the clustering result into the asset classification library.
6. 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 internet of things asset identification method according to any one of the preceding claims 1-4.
7. A computer readable storage medium, characterized in that the storage medium stores a computer program which, when executed by a processor, implements the internet of things asset identification method of any of the preceding claims 1-4.
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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 |
| CN117493777B (en) * | 2023-12-29 | 2024-03-15 | 成都秦川物联网科技股份有限公司 | Ultrasonic flowmeter data cleaning method, system and device based on Internet of things |
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