CN112580354A - Intelligent registration method and system for Internet of things equipment based on semantic Internet of things middleware - Google Patents
Intelligent registration method and system for Internet of things equipment based on semantic Internet of things middleware Download PDFInfo
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Abstract
The invention discloses an Internet of things equipment intelligent registration method based on semantic Internet of things middleware, which is characterized in that equipment triple entities are identified by utilizing rich equipment semantics in equipment protocol banner information and adopting a method of combining a corpus and rules; collecting equipment information of online equipment evaluation and retail websites by using a crawler technology, extracting equipment information in a uniform form based on an equipment body, and storing the equipment information in an equipment body library; and searching the equipment body library by taking the triples as search conditions, matching complete equipment information and storing the complete equipment information in the equipment information database, and finishing automatic registration. If the entity can not be identified in the banner information, actively collecting the equipment information by using an equipment specification submitted by the equipment provider user, extracting the equipment information based on the equipment body structure by using a rule, and respectively storing the equipment information in an equipment body library and an equipment information database to finish intelligent registration.
Description
Technical Field
The invention belongs to the technical field of equipment application protocol banner information entity identification, semantic Internet of things middleware, Internet of things equipment intelligent registration and particularly relates to an Internet of things equipment intelligent registration method and system based on the semantic Internet of things middleware.
Background
As more and more domains access the internet of things, the number of devices of the internet of things is growing on a large scale, and the heterogeneity of the underlying devices is a significant challenge for interoperability in the internet of things today. In order to mask device heterogeneity, many standard device accesses, protocols, frameworks, abstractions, and specifications have been proposed. On this basis, the important premise that the devices enter the internet of things and perform normal communication and work is that the devices are registered in a unified manner, so that it is important to provide a consistent scheme for supporting undifferentiated registration of heterogeneous IOT devices. In addition, the physical information of the equipment provides reasonable arrangement environment suggestions for the upper-layer users, for example, the ambient temperature of the equipment can be adjusted according to the working temperature range of the equipment, and a threshold value related to the temperature is provided for the normal work of the equipment. Therefore, it is also necessary to provide the device itself information during the registration process. In the current physical information registration of related equipment, most of the information of interest is manually input by a developer, and in the case of a large number of equipment, the method has low efficiency and high information error rate.
Disclosure of Invention
The invention aims to solve the problems that the information of the equipment of the Internet of things is heterogeneous, the information of the equipment is registered in a coarse granularity mode, and a large amount of manual input is needed in the existing equipment registration technology, and provides an intelligent registration method of the equipment of the Internet of things based on semantic Internet of things middleware. And secondly, equipment information is actively collected by the aid of equipment manufacturer users who submit equipment specifications by themselves, and then the equipment information based on the body is formed to complete registration. The result of the invention shows that the intelligent registration of the complete equipment information can be effectively realized by using the two modes.
The specific technical scheme for realizing the purpose of the invention is as follows:
an Internet of things equipment intelligent registration method based on semantic Internet of things middleware comprises the following specific steps:
step 1: the method comprises the following steps of acquiring application protocol content of equipment by using a data acquisition module, analyzing the application protocol content into a uniform text form, and forming banner information of the equipment, wherein the method comprises the following specific steps:
(1) after the equipment is accessed to the network, the information is continuously sent to the server by using a protocol, a passive interception technology is adopted, binary data sent by the equipment is captured and analyzed indiscriminately on the premise of not influencing the normal operation of the current route or gateway, and a passive interception module f is adopted to be different from the traditional passive interception1There is no need to capture a complete TCP connection nor to perform a specific parsing of the protocol. Randomly acquiring a small amount of binary data, and acquiring the ip address of the equipment according to the length of the ip address and the fixed offset address number between the destination ip and the source ip address. Based on the relationship between the binary protocol data and the ip address, a mapping formula is defined as follows:
f1(bi)＝IPi
wherein IPiIP address indicating the ith device, biRepresenting the acquired binary data;
(2) according to passive interception module f1Active detection module f for protocol implementation of acquired ip address2To obtain the banner of the protocol. And detecting the alive port of the equipment by adopting the half-connection, acquiring the alive port of the equipment, returning ACK + SYN for half-connection if the port of the equipment is opened, and returning RST + SYN to finish TCP connection if the port of the equipment is not opened. The mapping formula is defined as follows:
f2(IPi)＝Porti
wherein PortiLive port list, IP, representing the ith deviceiAn IP address representing the ith device;
(3) active detection module f2After the survival port of the equipment is provided, the protocol analysis module f3Constructing payload to the surviving port, and acquiring response information of the surviving port, namely the banner information of the protocol. According to the obtained Banner informationAnd analyzing the protocol, and converting the protocol banner information into a character string in a uniform form by using a protocol analysis template for subsequent operation because the different expression forms of the protocol banner information are different. The mapping relationship between the protocol and the banner information is as follows:
f3(Portij)＝bij
wherein b isijBanner information of j protocol representing i device, BiBanner information, Port, representing the ith deviceiA list of surviving ports representing the ith device.
Step 2: converting the banner information of the equipment protocol into a banner word list through a banner information preprocessing module, and specifically comprising the following steps of:
(1) converting the original banner information into a list consisting of single words, and processing by using the rules and the corpus files provided by the invention;
(2) newly adding information rules which are irrelevant to equipment information, such as a deactivation rule, a time regular expression and the like, removing protocol-irrelevant words by using the rules, and reducing the error recognition rate of the model, wherein the rules are shown in a table 1:
TABLE 1 Add DeAction rule and time regular expression
(3) For an industrial protocol represented by S7, Modbus and Ethernet/Ip protocol, establishing a not _ banner anti-fuzzy model file, and deleting fields related to the file when collecting original banner information; for a long protocol represented by HTTP and HTTPS, the length of banner information is too long, and information related to equipment is concentrated in a title field or a body field, so that keywords are used for information screening;
(4) and establishing a device _ tag corpus file, wherein the initial corpus file only contains words related to the equipment. Because the form of the words related to the equipment in the banner information is complex but the content is similar, the synonym of the words related to the equipment is generated by adopting a Wikipedia corpus and an NLTK tool to form a final device _ tag corpus file;
(5) taking words or phrases in the device _ tag file as keywords to extract key fields from protocol information, reserving the text and the next row of content of the keywords to form the key fields, and extracting if the content exists; if the key field is still long, deleting the key field by using a proposed key field reduction rule, wherein the key field reduction rule is specifically expressed as: there are at most 3 "< >" in the key field, i.e. at most the content of the previous line of the key, this line and the next line in the standard HTML is retained. The rules are shown in table 2:
TABLE 2 Key field reduction rules
(6) And performing word segmentation and part-of-speech tagging on the processed corpus by using the NLTK, finally removing stop words, and adding stop words which conform to the experiment on the basis of the existing English stop word list, wherein the stop words comprise punctuation marks, protocol mark fields and service information.
And step 3: utilizing an equipment entity recognition module to mark words of brand, type and model from a banner word list, and the method comprises the following specific steps:
(1) based on the search of the whole-network online equipment sale and evaluation website, the equipment brands and equipment types of the whole-network Internet of things are collected, wherein the equipment types are divided into three-layer structures of a type major class, a type middle class and a type minor class according to the equipment coverage range, wherein the type minor class
Class in type
The type is large. Storing the brand and the type of the equipment into a corpus, and if a new brand and a new type of the equipment are added, updating a corresponding corpus list, wherein the brand and type list in the corpus is easy to expand and maintain;
(2) firstly, segmenting the brand and type corpus list, and marking BIO of the banner word list about the brand and the type by utilizing the segmented list. The model is initially screened for words using the first level of rules, the model is processed for pure alphabetic words, pure numeric words, and words containing "/", "_" using the second level of rules, and finally the final < brand, type, model > triplet is determined using the third level of rules.
(3) The first layer rule consists of two parts: firstly, a first layer of initial rules is proposed for marking the model, wherein the equipment model is composed of the following three forms:
1) pure numbers;
2) a combination of numbers and letters;
3) a combination of numbers, letters, and "-", where numbers and letters are present simultaneously;
the first layer initial rules are shown in table 3:
TABLE 3 first layer initial rules
In the first layer of initial rules, in addition to the three forms described above, a character string containing "/"; secondly, on the first layer of initial rules, a not _ tag file is built, and a regular expression rule containing' number? + keyword + number? "the character string conforming to the model rule disambiguates the character string, wherein the keyword comprises a tag symbol in HTML, a coding type involved in a protocol, and an element attribute word in the protocol. Defining a first layer of initial model rules after the not _ tag corpus processing as first layer rules;
(4) on the first layer rule, establishing a second layer rule for processing the following three conditions:
1) handling pure numbers and compliance with first-tier rules, searching at most M ahead1Searching the word marked as brand or model, and defining the rule for processing the word as a model extraction rule 1;
2) processing the condition that the character does not conform to the first layer rule but consists of a plurality of pure English character strings, wherein the character marked as 'model' currently is searched forward by at most M2If a word marked as a brand is searched, the middle English character is marked as a model, and a rule for processing the situation is defined as a model extraction rule 2;
3) processing the form XX/YY or XX _ YY requires that XX conform to the first layer rule or the case where XX represents "Series", and a rule for processing this case is defined as the model extraction rule 3.
(5) After the second layer of rule processing, the complete model can be determined relatively accurately, but a large number of character strings which do not belong to the model are identified as the model by mistake. Then, words marked as the brand, the type and the model form an exponential triple, which is not beneficial to the next searching of the ontology base, so that a third-layer rule is established, and the third-layer rule reduces the error recognition rate of the model by increasing the relation among the brand, the type and the model. The third rule contains the following specific cases:
1) determination of model number: the nearest word marked as the model before and after the brand mark word is marked, and the distance is less than a fixed value M3；
2) Determination of type: the words marked as type are all used as alternatives of the device model, and the device type priority is set, and the network communication device priority is defined to be less than the common device. Because the equipment represented by the common temperature sensor does not have the networking function, the equipment needs to be networked by relying on intermediate equipment (such as an intelligent gateway), and thus equipment at a lower layer below the intermediate equipment can be excavated. Secondly, defining a priority type major class < a type middle class < a type minor class under different types of grades, and if 2 or more types are identified in the banner information and belong to different levels of equipment types, only keeping the equipment type with the highest priority as a final equipment type;
and 4, step 4: the method comprises the following specific steps of providing an equipment information body structure and an equipment attribute information structure through an equipment information body generating module, generating equipment information of fine granularity of equipment to be identified, and storing the equipment information in an equipment information database, wherein the method comprises the following steps:
(1) providing an equipment information body structure and an equipment attribute information structure which accord with the invention;
the ontology includes 8 entity classes, where the thining class is a generic parent class of all the entity classes defined by owl, and the following is a specific description of the other 7 entity classes:
1) device: representing the IOT equipment to be identified by the invention;
2) basic _ logo represents the triple identifier < brand, type, model > of the IOT device;
3) basic _ Parameter, which represents the Basic hardware configuration Parameter of the IOT device;
4) function _ Features, which represents the Function parameter of the IOT device in normal operation;
5) word _ environment, representing the environment parameter of the IOT equipment working normally;
6) exterior: representing an appearance parameter of the IOT device;
7) data: represents the working data produced by the IOT device, which can be constructed using the O & M and SensorML modeling languages.
The relationship between the entity classes is illustrated in table 4:
table 4 description of relationships between entity classes
| |
|
Relationships between | Description of the invention |
| Basic_logo | Device | is_logo | Triple unique identification IOT device |
| Device | Basic_logo | has_logo | IOT devices are uniquely identified by triplets |
| Device | Basic_Parameter | hasBasic_parameters | IOT device having hardware configuration parameters |
| Device | Function_Features | hasFunction_features | IOT device owning functional parameters |
| Device | Work_Envionment | hasWork_envionment | IOT device owning environmental parameters |
| Device | Exterior | hasExterior | Appearance of IOT device |
| Device | Data | hasData | IOT device generating data |
| Data | Device | isData | The data being IOT devices |
(2) Crawling equipment information of various online sales platforms and equipment evaluation platforms by using a crawler and filtering out non-Internet-of-things equipment;
(3) capturing relevant attribute information of the equipment based on the equipment information attribute rule, storing the relevant attribute information in a database, converting database data into an ontology file according to an equipment information ontology, and storing the ontology file in an ontology repository;
and 5: if the equipment entity triple can uniquely determine the Internet of things equipment, calling an automatic registration module, taking the equipment entity triple < brand, type and model > as a search condition, searching the information in the ontology repository by using the proposed registration rule, completing the equipment information by matching the information in the ontology repository, and further generating an ontology file of the equipment information to be identified;
the registration rules are shown in table 5:
table 5 registration rule description
Step 6: if the equipment entity triple cannot uniquely determine the internet of things equipment, calling an equipment provider registration module to actively collect equipment information, wherein the method comprises the following specific steps:
(1) the middleware actively sends a request for registering equipment information to an equipment business user, and the equipment business provides an equipment information file by itself, wherein the format of the information file is not limited;
(2) and extracting file information based on the equipment information attribute, forming an equipment body file according to an equipment information body structure, respectively adding the equipment body file into the body storage library and the equipment information database to complete equipment information registration when the banner information is missing or a complete triple cannot be identified, ensuring the integrity of the equipment information and completing the active collection of the equipment information.
And 7: and the upper user interface layer provides a python script to open a unified API for the upper user, and the spark ql is utilized to transparently access the equipment information database. The upper user interface layer opens the inquiry authority to the common user and opens the addition, deletion and check authority to the developer so as to realize the maintenance and expansion of the equipment information database.
Based on the method, the invention also provides an Internet of things equipment intelligent registration system based on the semantic Internet of things middleware, which comprises three parts, namely data acquisition, intelligent registration and an upper interface layer, wherein the data acquisition comprises an active detection module, a passive interception module and a protocol analysis module; the intelligent registration utilizes equipment protocol banner information analyzed by a protocol, converts the banner information into an equipment body file through a banner information preprocessing module, an equipment entity identification module and an equipment information body generating module, and stores the equipment body file in an equipment information database to realize automatic registration of the equipment information; if the entity information in the banner information cannot uniquely determine one device, the device information is interactively and actively collected with a device provider, and the device information in the device specification is extracted to realize a device provider registration module; the upper interface layer is a transparent equipment access interface opened to an upper layer common user.
The data acquisition, intelligent registration and upper interface layer are vertical structures from bottom to top.
The invention has the beneficial effects that:
the intelligent registering method of the Internet of things equipment based on the semantic Internet of things middleware is used for shielding heterogeneous equipment information, registering information describing fine granularity of the equipment, reducing the cost of manually registering the equipment information and realizing plug and play of the equipment.
The invention is used as a new idea for intelligent registration of the equipment of the Internet of things, pays attention to the equipment description information with fine granularity, and reduces the cost and error rate of manually inputting the equipment information. Meanwhile, the consistency of the semantic ontology is utilized, the heterogeneity of the Internet of things equipment is shielded, different types of equipment of different manufacturers are compatible, a unified interface is opened for upper-layer users, transparent access of the upper-layer users is achieved, privacy is guaranteed, and therefore intelligent registration of the Internet of things equipment is achieved, and the Internet of things equipment is easy to access by the upper-layer users.
According to the invention, from the heterogeneity of the equipment information, by using the semantic Internet of things middleware, how to acquire the equipment information and convert the equipment information into a unified form for registration is analyzed downwards, and how to shield the detailed structure of the underlying equipment by a user is analyzed upwards so as to realize transparent access. Meanwhile, the invention provides an intelligent registration method, which adopts a mode of combining automatic registration and an equipment information active collection technology based on equipment provider user registration, ensures the availability and fine granularity of equipment information and improves the current situation that fine granularity equipment information can only be manually input. In addition, the invention provides a method for identifying the triple entity of the equipment < brand, type, model > from the equipment banner information, which is expandable and has higher accuracy. Therefore, the method can be regarded as a universal intelligent registration method for the Internet of things equipment, and has strong practicability.
Drawings
FIG. 1 is a schematic structural diagram of a semantic IOT middleware;
FIG. 2 is a schematic flow diagram of a data acquisition module;
FIG. 3 is a schematic diagram of the framework of the banner information preprocessing;
FIG. 4 is a schematic diagram of a device entity identification framework based on the banner information;
FIG. 5 is a schematic diagram of a module framework for generating device information ontology;
FIG. 6 is a schematic diagram of an apparatus information body structure;
FIG. 7 is a diagram illustrating device information attributes;
FIG. 8 is a schematic diagram of an intelligent registration framework for a device, including an auto registration module and a merchant registration module;
fig. 9 is a schematic diagram of the value of the long and short protocol limit N;
FIG. 10 shows a distance parameter M1、M2And M3A value schematic diagram;
FIG. 11 is a diagram of a partial device information ontology file;
fig. 12 is a diagram illustrating a matching result of a device based on a registration rule.
Detailed Description
The invention is further described in detail with reference to the following specific examples and the accompanying drawings. The procedures, conditions, experimental methods and the like for carrying out the present invention are general knowledge and common general knowledge in the art except for the contents specifically mentioned below, and the present invention is not particularly limited.
The invention discloses an Internet of things equipment intelligent registration method based on semantic Internet of things middleware, which is characterized in that equipment triple entities are identified by utilizing equipment semantics rich in banner information of an equipment application protocol and adopting a method of combining a semantic library and rules; collecting equipment information of online equipment evaluation and retail websites by using a crawler technology, extracting equipment information in a uniform form based on a proposed equipment body, and storing the equipment information in an equipment body library; and searching the equipment body library by taking the triples as search conditions, matching complete equipment information and storing the complete equipment information in the equipment information database, and finishing automatic registration. If the entity can not be identified in the banner information, the equipment information based on the equipment body structure is extracted by using the equipment specification submitted by the equipment provider user and the rules, and is respectively stored in the equipment body library and the equipment information database so as to complete intelligent registration. By using the intelligent registration method depending on the semantic middleware, a uniform access interface is provided for a user, the heterogeneity of the bottom layer equipment is shielded, and the plug and play of the equipment is realized.
The present invention will be described in detail below with reference to the accompanying drawings.
The invention relates to an intelligent registration method of Internet of things equipment based on semantic middleware, which relies on the semantic Internet of things middleware and has a structure shown in figure 1. The invention comprises the following specific steps:
step 1: by using the data acquisition module shown in fig. 2, the application protocol content of the device is detected and analyzed into a uniform text form to form the banner information of the device, and the method comprises the following specific steps:
(1) after the equipment is accessed to the network, the information is continuously sent to the server by using a protocol, a passive interception technology is adopted, binary data sent by the equipment is captured and analyzed indiscriminately on the premise of not influencing the normal operation of the current route or gateway, and a passive interception module f is adopted to be different from the traditional passive interception1There is no need to capture a complete TCP connection nor to perform a specific parsing of the protocol. Randomly acquiring a small amount of binary data, and acquiring the ip address of the equipment according to the length of the ip address and the fixed offset address number between the destination ip and the source ip address. Based on the relationship between the binary protocol data and the ip address, a mapping formula is defined as follows:
f1(bi)＝IPi
wherein IPiIP address indicating the ith device, biRepresenting the acquired binary data;
(2) according to passive interception module f1Active detection module f for protocol implementation of acquired ip address2To obtain the banner of the protocol. And detecting the alive port of the equipment by adopting the half-connection, acquiring the alive port of the equipment, returning ACK + SYN for half-connection if the port of the equipment is opened, and returning RST + SYN to finish TCP connection if the port of the equipment is not opened. The mapping formula is defined as follows:
f2(IPi)＝Porti
wherein PortiLive port list, IP, representing the ith deviceiAn IP address representing the ith device;
(3) active detection module f2After the survival port of the equipment is provided, the protocol analysis module f3Constructing payload to the surviving port, and acquiring the surviving portThe response information of (2) is the banner information of the protocol. And analyzing the protocol according to the acquired Banner information, and converting the protocol Banner information into a character string in a unified form by using a protocol analysis template for subsequent operation due to different expression forms of the different protocol Banner information. The mapping relationship between the protocol and the banner information is as follows:
f3(Portij)＝bij
wherein b isijBanner information of j protocol representing i device, BiBanner information Port representing the ith deviceiA list of surviving ports representing the ith device.
Step 2: converting the banner information of the device into a banner word list by a banner information preprocessing module shown in fig. 3, which specifically comprises the following steps:
(1) converting the original banner information into a list consisting of single words, and processing by using the rules and the corpus files provided by the invention;
(2) newly adding information rules which are irrelevant to equipment information, such as a deactivation rule, a time regular expression and the like, removing protocol-irrelevant words by using the rules, and reducing the error recognition rate of the model, wherein the rules are shown in a table 1:
TABLE 1 Add DeAction rule and time regular expression
(3) For an industrial protocol represented by S7, Modbus and Ethernet/Ip protocol, establishing a not _ banner anti-fuzzy model file, and deleting fields related to the file when collecting original banner information; for a long protocol represented by HTTP and HTTPS, the length of banner information is too long, and information related to equipment is concentrated in a title field or a body field, so that keywords are used for information screening;
(4) and establishing a device _ tag corpus file, wherein the initial corpus file only contains words related to the equipment. Because the form of the words related to the equipment in the banner information is complex but the content is similar, the synonym of the words related to the equipment is generated by adopting a Wikipedia corpus and an NLTK tool to form a final device _ tag corpus file;
(5) taking words or phrases in the device _ tag file as keywords to extract key fields from protocol information, reserving the text and the next row of content of the keywords to form the key fields, and extracting if the content exists; if the key field is still long, deleting the key field by using a proposed key field reduction rule, wherein the key field reduction rule is specifically expressed as: there are at most 3 "< >" in the key field, i.e. at most the content of the previous line of the key, this line and the next line in the standard HTML is retained. The rules are shown in table 2:
TABLE 2 Key field reduction rules
(6) Performing word segmentation and part-of-speech tagging on the processed corpus by using NLTK; and finally, adding stop words conforming to the experiment on the basis of the existing English stop word list, wherein the stop words comprise punctuation marks, protocol mark fields (protocol names, protocol operators, protocol service response numbers such as 2XX, 4XX and the like), and service information (login, register, welome, server, name, type and id).
And step 3: the method for identifying the words of the brand, the type and the model from the banner word list by using the equipment entity identification module shown in the figure 4 comprises the following specific methods:
(1) on the basis of the search of the whole-network online equipment sale and evaluation website, 1885 equipment brands and 2040 equipment types are collected together, wherein the equipment types are divided into three-layer structures of a type major class, a type middle class and a type minor class according to the equipment coverage range, wherein the type minor class
Type (B)Class III
The large type category, the medium type category, and the small type category total 29, 139, and 1872. Storing the brand and the type of the equipment into a corpus, and if a new brand and a new type of the equipment are added, updating a corresponding corpus list, wherein the brand and type list in the corpus is easy to expand and maintain;
(2) and segmenting the brand and type corpus list, and labeling BIO of the banner word list about the brand and the type by utilizing the segmented list. The model is initially screened for words using the first level of rules, the model is initially screened for pure alphabetic words, pure numeric words, and words containing "/", "_" are processed using the second level of rules, and finally the final < brand, type, model > triplet is determined using the third level of rules.
(3) The first layer rule consists of two parts: firstly, a first layer of initial rules is proposed for marking the model, wherein the equipment model is composed of the following three forms:
1) pure numbers;
2) a combination of numbers and letters;
3) a combination of numbers, letters, and "-", where numbers and letters are present simultaneously;
the first layer initial rules are shown in table 3:
TABLE 3 first layer initial rules
In the first layer of initial rules, in addition to the three forms described above, a character string containing "/"; secondly, on the first layer of initial rules, a not _ tag file is built, and a regular expression rule containing' number? + keyword + number? "the character string conforming to the model rule disambiguates the character string, wherein the keyword comprises a tag symbol in HTML, a coding type involved in a protocol, and an element attribute word in the protocol. Defining a first layer of initial model rules after the not _ tag corpus processing as first layer rules;
(4) on the first layer rule, establishing a second layer rule for processing the following three conditions:
1) type extraction rule 1: handling pure numbers and compliance with first-tier rules, searching at most M ahead1The word is searched to a word labeled as brand or model, where M1＝5；
2) Type extraction rule 2: handling the case where the first-level rule is not met, but consists of several pure english strings, such as HP (brand) color Laser (english) mfp (english) 178nw (model), the character currently labeled "model", searches forward by at most M2If a word marked as a brand is searched, the middle English character is marked as a model, wherein M2＝5；
3) Type extraction rule 3: processing the form XX/YY or XX _ YY requires that XX comply with the first layer rule or that XX represents "Series".
(5) After the second layer of rule processing, the complete model can be determined relatively accurately, but a large number of character strings which do not belong to the model are identified as the model by mistake. Then the words labeled as brand, type and model will form an exponential triple that is not conducive to the next search of the ontology base, so a third level of rules is established. The third layer of rules reduces the error recognition rate of the model by increasing the relation among the brand, the type and the model. The third rule contains the following specific cases:
1) determination of model number: the nearest word marked as the model before and after the brand mark word is marked, and the distance is less than a fixed value M3Wherein M is3＝20；
2) Determination of type: the words marked as type are all used as alternatives of the device model, and the device type priority is set, and the network communication device priority is defined to be less than the common device. Because the equipment represented by the common temperature sensor does not have the networking function, the equipment needs to be networked by relying on intermediate equipment (such as an intelligent gateway), and thus equipment at a lower layer below the intermediate equipment can be excavated. Secondly, defining a priority type major class < a type middle class < a type minor class under different types of grades, and if 2 or more types are identified in the banner information and belong to different levels of equipment types, only keeping the equipment type with the highest priority as a final equipment type;
and 4, step 4: through the device information generating body module shown in fig. 5, a device information body structure and a device information attribute structure are proposed, and device information of fine granularity of a device to be identified is generated and stored in a device information database, which includes the following specific methods:
(1) the device information ontology structure shown in fig. 6 and the device information attribute shown in fig. 7 are provided, where the ontology includes 8 entity classes, where the thining class is a generic parent class of all the entity classes defined by owl, and the following is a specific description of other 7 entity classes:
1) device: representing the IOT equipment to be identified by the invention;
2) basic _ logo represents the triple identifier < brand, type, model > of the IOT device;
3) basic _ Parameter, which represents the Basic hardware configuration Parameter of the IOT device;
4) function _ Features, which represents the Function parameter of the IOT device in normal operation;
5) word _ environment, representing the environment parameter of the IOT equipment working normally;
6) exterior: representing an appearance parameter of the IOT device;
7) data: represents the working data produced by the IOT device, which can be constructed using the O & M and SensorML modeling languages.
The relationship between the entity classes is illustrated in table 4:
table 4 description of relationships between entity classes
(2) Crawling equipment information of various online sales platforms and equipment evaluation platforms by using a crawler and filtering out non-Internet-of-things equipment;
(3) capturing relevant attribute information of the equipment based on the equipment information attribute rule, storing the relevant attribute information in a database, converting database data into an ontology file according to an equipment information ontology, and storing the ontology file in an ontology repository;
and 5: if the equipment entity triple can uniquely determine an internet of things equipment, calling an automatic registration module shown in the attached figure 8, taking the equipment entity triple < brand, type, model > as a search condition, searching information in an ontology repository by using a registration rule provided by the table 5, matching the information in the ontology repository to complete equipment information, and further generating an ontology file of the equipment information to be identified;
table 5 registration rule description
Step 6: if the equipment entity triplet cannot uniquely determine an internet of things equipment, the equipment provider registration module shown in fig. 8 is called to actively collect equipment information, and the method includes the following specific steps:
(1) the middleware actively sends a request for registering equipment information to an equipment business user, and the equipment business provides an equipment information file by itself, wherein the format of the information file is not limited;
(2) and extracting file information based on the equipment information attribute, forming an equipment body file according to an equipment information body structure, respectively adding the equipment body file into the body storage library and the equipment information database to complete equipment information registration when the banner information is missing or a complete triple cannot be identified, ensuring the integrity of the equipment information and completing the active collection of the equipment information.
And 7: and the upper user interface layer provides a python script to open a unified API for the upper user, and the spark ql is utilized to transparently access the equipment information database. The upper user interface layer opens the inquiry authority to the common user and opens the addition, deletion and check authority to the developer so as to realize the maintenance and expansion of the equipment information database.
In the collection of the banner information, the banner information of all protocols of 10000 different internet of things devices collected in Censys and Shodan and comments of the devices about brands, types and models are used as data sets of the invention, 27 brands and 35 types of devices are collected, wherein the types of the devices comprise sensor devices, smart home devices, industrial devices, security devices, network devices and communication devices, the types of the devices comprise 5 types of industrial devices, proprietary device communication protocols are used, and other types of device protocols comprise standard application layer protocols such as FTP, HTTP, HTTPS, TELNET, SSH, SNMP and the like.
Aiming at the data set of the invention, the invention carries out the data set of the invention about the parameters of a long and short protocol boundary N and a distance parameter M1、M2And M3The value test of (2) verifies the necessity of three layers of rules proposed in the equipment information entity identification module and the experimental result of the entity identification method, and finally counts the matching result based on the registration rule.
The accuracy rate (accuracycacy) and the false identification rate (FAR) are calculated by using the following two formulas respectively, so that the application effect of the entity identification method in the invention is evaluated.
According to the invention, when the value of N is 500-3000, the accuracy, the error recognition rate, the time and the number of the triples corresponding to different values of N are counted, wherein the effect of not adding the parameter N is shown when the value of N is not N, the statistical result is shown in figure 9, when N is equal to 700, the accuracy is highest, and the error recognition rate is lowest. Second, for the time stamp, when N equals 700, the time is lower and the number of triples generated is lower. So N takes the value of 700. When N is 3000, the accuracy, misrecognition and generated triplet are greatly changed, and the effect is not good, so the range is up to 3000. If N is not used, the accuracy rate, the misrecognition rate, the marking time and the generated triad number are the highest, so that the scheme of adding N is necessary.
The invention counts the distance parameter M in the data set1、M2And M3The statistics of the value of (A) and (B) are shown in FIG. 10, M1Most of the components are distributed in [1,3 ]]In, [5,10 ]]The frequency of occurrence is 0, then the value range of M _1 is M1≤5。M2Most of the components are distributed in [2,4 ]]Middle, [6,10]The frequency of occurrence is 0, then M2Has a value range of M2≤5。M3Most of the components are distributed in [1,5 ]]The frequency of occurrence of the distance is extremely small and close to 0 in other ranges less than 20, and the frequency of occurrence of the distance is 0 in the range more than 20, then M3Has a value range of M3≤20。
The invention respectively counts the recognition effects under the three-layer rules in the device information entity recognition method as shown in table 6:
TABLE 6 representation of entity identification method under one, two, and three layer rules
| Hierarchy | Rate of accuracy | False recognition rate | Number of triplets |
| A layer of | 90％ | 52％ | 341955 |
| Two layers | 99.38％ | 8.9％ | 68106 |
| Three layers | 99.48％ | 4.66％ | 28639 |
It can be seen that after the second layer rule is added, the accuracy is obviously improved, and the misrecognition rate and the number of triples are obviously reduced. After the third-layer rule is added, the misrecognition rate and the number of the triples are obviously reduced again, because the third-layer rule removes a plurality of character strings which are misrecognized, and therefore, the three-layer rule provided by the invention is necessary and feasible.
The invention makes statistics of the performances of the equipment information entity identification method in industrial equipment, common networking equipment and overall data set respectively, and the comparison results are shown in table 7:
TABLE 7 identification result of device information entity identification method
| Rate of accuracy | False recognition rate | |
| Industrial equipment (makeUsing proprietary protocols) | 99.1％ | 0.14％ |
| Generic networking device | 99.6％ | 5.1％ |
| Integral body | 99.48％ | 4.66％ |
The present invention can generate the ontology file of the device, and fig. 11 shows part of the ontology file information of the Brother ads-2100 scanner, in which the relationship between entities and detailed attribute data in the Basic _ parameter of the device are defined.
The invention carries out classified statistics on the devices in the data set according to the registration rule, the statistical result is shown in figure 12, the devices which accord with the rules 1,3 and 8 are more, in the collected devices, 56.2 percent of the devices can be automatically registered without the device business users submitting files, and 43.8 percent of the devices require the device business users to submit device description files as required for registration, so as to complete the active collection of device information, thereby realizing the intelligent registration of the devices.
The invention also provides an Internet of things equipment intelligent registration system based on the semantic Internet of things middleware, which comprises three parts, namely data acquisition, intelligent registration and an upper interface layer, wherein the data acquisition comprises an active detection module, a passive interception module and a protocol analysis module; the intelligent registration utilizes equipment protocol banner information analyzed by a protocol, converts the banner information into an equipment body file through a banner information preprocessing module, an equipment entity identification module and an equipment information body generating module, and stores the equipment body file in an equipment information database to realize automatic registration of the equipment information; if the entity information in the banner information cannot uniquely determine one device, the device information is interactively and actively collected with a device provider, and the device information in the device specification is extracted to realize a device provider registration module; the upper interface layer is a transparent equipment access interface opened to an upper layer common user.
The data acquisition, intelligent registration and upper interface layer are vertical structures from bottom to top.
The protection of the present invention is not limited to the above embodiments. Variations and advantages that may occur to those skilled in the art may be incorporated into the invention without departing from the spirit and scope of the inventive concept, and the scope of the appended claims is intended to be protected.
Claims (9)
1. The intelligent registering method of the Internet of things equipment based on the semantic Internet of things middleware is characterized by comprising the following steps:
step 1: acquiring the application protocol content of the equipment by using a data acquisition module, and analyzing the application protocol content into a uniform text form to form banner information of the equipment protocol;
step 2: converting the banner information of the equipment protocol into a banner word list through a banner information preprocessing module;
and step 3: marking words of brand, type and model from a banner word list by using an equipment entity identification module to form an equipment entity triple;
and 4, step 4: the device information body generating module is used for providing a device information body structure and a device attribute information structure, and generating device information of fine granularity of the device to be identified and storing the device information in a device information database;
and 5: if the equipment entity triple can uniquely determine the Internet of things equipment, calling an automatic registration module, using the equipment entity triple as a search condition, searching the information in the ontology repository by using the proposed registration rule, and completing the equipment information by matching the information in the ontology repository, thereby generating an ontology file of the equipment information to be identified;
step 6: if the equipment entity triple cannot uniquely determine an internet of things equipment, calling an equipment provider registration module to actively collect equipment information in an equipment file to form a body file, and finishing registration;
and 7: the upper user interface layer provides a python script to open a unified API for the upper user, and the spark ql is used for transparently accessing the equipment information database; the upper user interface layer opens the inquiry authority to the common user and opens the addition, deletion and check authority to the developer so as to realize the maintenance and expansion of the equipment information database.
2. The intelligent registering method for the internet of things equipment based on the semantic internet of things middleware as claimed in claim 1, wherein the step 1 comprises the following substeps:
step 1.1: after the equipment accesses the network, a protocol is used for continuously sending information to a server, a passive interception technology is adopted, and binary data sent by the equipment are captured and analyzed indiscriminately on the premise of not influencing the normal operation of the current route or gateway; the data acquisition module adopts a passive interception module f1Randomly acquiring a small amount of binary data, acquiring the ip address of the equipment according to the length of the ip address and the fixed offset address number between the destination ip and the source ip address, and defining a mapping formula as follows based on the relationship between the binary protocol data and the ip address:
f1(bi)＝IPi
wherein IPiIP address indicating the ith device, biRepresenting the acquired binary data;
step 1.2: according to passive interception module f1Active detection module f for protocol implementation of acquired ip address2And detecting the alive port of the equipment by adopting the half-connection with the banner of the acquisition protocol, acquiring the alive port of the equipment, returning ACK + SYN for half-connection if and only if the port of the equipment is opened, and returning RST + SYN to finish TCP connection if not, wherein a mapping formula is defined as follows:
f2(IPi)＝Porti
wherein PortiLive port list, IP, representing the ith deviceiIP expressing ith deviceAn address;
step 1.3: active detection module f2After the survival port of the equipment is provided, the protocol analysis module f3Constructing payload to the surviving port, and acquiring response information of the surviving port, namely banner information of a protocol; the protocol analysis module analyzes the protocol according to the acquired banner information, and converts the protocol banner information into a character string in a unified form by using a protocol analysis template for subsequent operation, wherein the mapping relation between the protocol and the banner information is as follows:
f3(Portij)＝bij
wherein b isijBanner information of j protocol representing i device, BiBanner information, Port, representing the ith deviceiA list of surviving ports representing the ith device.
3. The intelligent registration method for Internet of things equipment based on semantic Internet of things middleware, according to claim 1, characterized in that the step 2 comprises the following substeps:
step 2.1: converting the original banner information into a list of single word components, the banner (B) obtained in step 1.3 of claim 2i) As the linguistic data to be processed, the Banner information preprocessing module processes the linguistic data by using rules and linguistic data files;
step 2.2: newly adding a deactivation rule and a time regular expression, defining the deactivation rule and the time regular expression as rules irrelevant to equipment information, and removing protocol irrelevant words by using the rules to reduce the error recognition rate of the model;
step 2.3: for an industrial protocol represented by S7, Modbus and Ethernet/Ip protocol, establishing a not _ banner anti-fuzzy model file, and deleting fields related to the file when collecting original banner information; for a long protocol represented by HTTP and HTTPS, the length of banner information is too long, and information related to equipment is concentrated in a title field or a body field, so that keywords are used for information screening;
step 2.4: establishing a device _ tag corpus file, wherein the initial corpus file only contains words related to equipment; because the form of the words related to the equipment in the banner information is complex but the content is similar, the synonym of the words related to the equipment is generated by adopting a Wikipedia corpus and an NLTK tool to form a final device _ tag corpus file;
step 2.5: taking words or phrases in the device _ tag file as keywords to extract key fields from protocol information, reserving the text and the next row of content of the keywords to form the key fields, and extracting if the content exists; if the key field is still long, deleting the key field by using the proposed key field reduction rule, wherein the key field reduction rule is specifically expressed as follows: at most 3 '< >'s exist in the key field, namely, the content of the previous line and the next line of the key word in the standard HTML is reserved at most;
step 2.6: and performing word segmentation and part-of-speech tagging on the processed corpus by using the NLTK, finally removing stop words, and adding stop words which conform to the experiment on the basis of the existing English stop word list, wherein the stop words comprise punctuation marks, protocol mark fields and service information.
4. The intelligent registering method for the internet of things equipment based on the semantic internet of things middleware as claimed in claim 1, wherein the step 3 comprises the following substeps:
step 3.1: collecting the brand and the type of the equipment of the internet of things of the whole network based on the equipment information of the whole network online equipment selling and evaluating website, wherein the equipment types are divided into three-layer structures of a type major class, a type middle class and a type minor class according to the coverage range of the equipment, wherein the type minor class
Class in type
A type major class; storing the device brand and type in the corpus, and if new device brand and type are added, updating the pairsThe method comprises the following steps of applying a corpus list, wherein the brand and type list in the corpus is easy to expand and maintain;
step 3.2: firstly, segmenting a brand and type corpus list, and marking BIO of a Banner word list about the brand and the type by utilizing the segmented list; next, the model's words are initially filtered using the first level rules, then the model's pure alphabetic words, pure numeric words, and words containing "/", "_" are processed using the second level rules, and finally the final < brand, type, model > triplets are determined using the third level rules.
5. The intelligent registration method for Internet of things equipment based on semantic Internet of things middleware, according to claim 4, characterized in that the first-layer rule consists of two parts: firstly, a first layer of initial rules is proposed for marking the model, wherein the equipment model is composed of the following three forms:
1) pure numbers;
2) a combination of numbers and letters;
3) a combination of numbers, letters, and "-", where numbers and letters are present simultaneously;
in the first layer of initial rules, in addition to the three forms described above, a character string containing "/";
secondly, on the above-mentioned first-layer initial rule, a not _ tag file is created by deleting a rule "number? + keyword + number? "the character string conforming to the model rule eliminates the character string ambiguity, wherein the keyword comprises a tag symbol in HTML, a coding type related to a protocol and an element attribute word in the protocol, and a first layer of initial model rule after the not _ tag corpus processing is defined as a first layer rule;
on the first layer rule, establishing a second layer rule for processing the following three conditions:
1) handling pure numbers and compliance with first-tier rules, searching at most M ahead1Searching the word marked as brand or model, and defining the rule for processing the word as a model extraction rule 1;
2) the processing does not comply with the first level rules,but in the case of a plurality of pure English character strings, the characters marked as 'model' are searched forward by at most M2If a word marked as a brand is searched, the middle English character is marked as a model, and a rule for processing the situation is defined as a model extraction rule 2;
3) processing the form of XX/YY or XX _ YY, requiring XX to conform to the first layer rule or the case that XX represents "Series", defining the rule for processing this case as a model extraction rule 3;
establishing a third-layer rule, wherein the third-layer rule reduces the error recognition rate of the model by increasing the relation among the brand, the type and the model, and the third-layer rule comprises the following specific conditions:
1) determination of model number: the nearest word marked as the model before and after the brand mark word is marked, and the distance is less than a fixed value M3；
2) Determination of type: the words marked as types are all used as the alternatives of the equipment models, the priority of the equipment types is set, and the priority of the network communication equipment is defined to be less than that of the common equipment; because the equipment represented by the common temperature sensor does not have the networking function, the equipment needs to be networked by relying on intermediate equipment (such as an intelligent gateway), and thus equipment at a lower layer below the intermediate equipment can be excavated. Secondly, defining a priority type major class < a class in the class < a class minor class under different types of grades, and if 2 or more types are identified in the banner information and belong to different levels of equipment types, only keeping the equipment type with the highest priority as the final equipment type.
6. The intelligent registering method for the internet of things equipment based on the semantic internet of things middleware as claimed in claim 1, wherein the step 4 comprises the following substeps:
step 4.1: providing an equipment information body structure and an equipment attribute information structure which accord with the invention;
step 4.2: crawling equipment information of various online sales platforms and equipment evaluation platforms by using a crawler and filtering out non-Internet-of-things equipment;
step 4.3: and capturing relevant attribute information of the equipment based on the equipment information attribute, storing the relevant attribute information in a database, converting the database data into an ontology file according to the equipment information ontology, and storing the ontology file in an ontology repository.
7. The intelligent registering method for the internet of things equipment based on the semantic internet of things middleware as claimed in claim 1, wherein the step 5 comprises the following substeps: and if the equipment entity triple can uniquely determine the Internet of things equipment, calling an automatic registration module, using the equipment entity triple as a search condition, searching the information in the ontology repository by using the proposed registration rule, and completing the equipment information by matching the information in the ontology repository, thereby generating an ontology file of the equipment information to be identified.
8. The intelligent registering method for the internet of things equipment based on the semantic internet of things middleware as claimed in claim 1, wherein the step 6 comprises the following substeps: if the equipment entity triple can not uniquely determine an internet of things equipment, calling an equipment provider registration module to actively collect equipment information,
step 6.1: the middleware actively sends a request for registering equipment information to an equipment business user, and the equipment business provides an equipment information file by itself, wherein the format of the information file is not limited;
step 6.2: extracting file information based on the provided equipment information attribute, forming an equipment body file according to an equipment information body structure, respectively adding the equipment body file into a body storage library and an equipment information database to complete equipment information registration when the banner information is missing or a complete triple cannot be identified, ensuring the integrity of the equipment information and completing the active collection of the equipment information.
9. A registration system for the intelligent registration method of the Internet of things equipment based on the semantic Internet of things middleware, which is characterized by comprising three parts, namely data acquisition, intelligent registration and an upper interface layer, according to any one of claims 1 to 8;
the data acquisition comprises an active detection module, a passive interception module and a protocol analysis module; the intelligent registration utilizes equipment protocol banner information analyzed by a protocol, converts the banner information into an equipment body file through a banner information preprocessing module, an equipment entity identification module and an equipment information body generating module, and stores the equipment body file in an equipment information database to realize automatic registration of the equipment information; if the entity information in the banner information cannot uniquely determine one device, the device information is interactively and actively collected with a device provider, and the device information in the device specification is extracted to realize a device provider registration module; the upper interface layer is a transparent equipment access interface opened to an upper layer common user; the data acquisition, intelligent registration and upper interface layer are vertical structures from bottom to top.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN111614507A (en) * | 2020-04-01 | 2020-09-01 | 西安电子科技大学 | Network protocol feature identification method |
| CN114070824A (en) * | 2021-11-17 | 2022-02-18 | 远景智能国际私人投资有限公司 | Registration method, registration cloud service, medium, and program product for internet of things device |
| CN115208923A (en) * | 2022-07-18 | 2022-10-18 | 阿里云计算有限公司 | Equipment information determination method, device and equipment |
| CN117422002A (en) * | 2023-12-19 | 2024-01-19 | 利尔达科技集团股份有限公司 | AIGC-based embedded product generation method, system and storage medium |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN111614507A (en) * | 2020-04-01 | 2020-09-01 | 西安电子科技大学 | Network protocol feature identification method |
| CN111614507B (en) * | 2020-04-01 | 2021-11-05 | 西安电子科技大学 | Network protocol feature identification method |
| CN114070824A (en) * | 2021-11-17 | 2022-02-18 | 远景智能国际私人投资有限公司 | Registration method, registration cloud service, medium, and program product for internet of things device |
| CN114070824B (en) * | 2021-11-17 | 2023-12-05 | 远景智能国际私人投资有限公司 | Registration method of Internet of things equipment, registration cloud server and medium |
| CN115208923A (en) * | 2022-07-18 | 2022-10-18 | 阿里云计算有限公司 | Equipment information determination method, device and equipment |
| CN117422002A (en) * | 2023-12-19 | 2024-01-19 | 利尔达科技集团股份有限公司 | AIGC-based embedded product generation method, system and storage medium |
| CN117422002B (en) * | 2023-12-19 | 2024-04-19 | 利尔达科技集团股份有限公司 | AIGC-based embedded product generation method, AIGC-based embedded product generation system and storage medium |
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