CN115102852B - Internet of things service opening method and device, electronic equipment and computer medium - Google Patents
Internet of things service opening method and device, electronic equipment and computer medium Download PDFInfo
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
- H04L41/08—Configuration management of networks or network elements
- H04L41/0803—Configuration setting
- H04L41/0806—Configuration setting for initial configuration or provisioning, e.g. plug-and-play
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L43/00—Arrangements for monitoring or testing data switching networks
- H04L43/16—Threshold monitoring
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/01—Protocols
- H04L67/12—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management
Abstract
The application provides a method, a device, electronic equipment and a computer medium for opening an Internet of things service, wherein the method comprises the following steps: identifying an Internet of things service opening request based on a pre-built convolutional neural network model to obtain a service type identification result; generating an Internet of things service opening instruction based on the service type identification result; evaluating the correctness of the service opening instruction of the Internet of things to obtain a value of the correctness; and responding to the correctness value being higher than a preset threshold value, and opening the corresponding internet of things service based on the internet of things service opening instruction. According to the method and the device, the service opening efficiency and accuracy of the Internet of things can be effectively improved, the labor cost is reduced, an efficient, convenient and high-accuracy service opening scheme of the Internet of things is provided for users, and the user experience is improved.
Description
Technical Field
The present disclosure relates to the field of internet of things, and in particular, to a method and an apparatus for opening an internet of things service, an electronic device, and a computer medium.
Background
With the continuous development of the fifth generation communication technology (5 th Generation Mobile Communication Technology, abbreviated as 5G), the service and application of the internet of things are gradually increased, and the opening mode of the service of the internet of things becomes a concern of researchers.
At present, the service of the internet of things is mainly distributed through a system and is realized by means of human eye identification and manual production, the realization mode not only increases the cost of manpower and material resources, but also consumes longer time by adopting human eye identification and manual production, and errors are easy to generate, so that the opening and the use of the service of the internet of things are affected for users.
Therefore, it is needed to provide an internet of things service opening scheme which is efficient, convenient and accurate for users.
Disclosure of Invention
In view of the above problems, the present application provides a method, an apparatus, an electronic device, and a computer medium for opening an internet of things service, so as to solve the problems of low efficiency, high cost, and easy error generation of the current internet of things service opening method.
In order to achieve the above object, the present application provides the following technical solutions:
according to an aspect of the present application, there is provided a method for opening an internet of things service, including:
identifying an Internet of things service opening request based on a pre-built convolutional neural network model to obtain a service type identification result;
generating an Internet of things service opening instruction based on the service type identification result;
evaluating the correctness of the service opening instruction of the Internet of things to obtain a value of the correctness;
and responding to the correctness value being higher than a preset threshold value, and opening the corresponding internet of things service based on the internet of things service opening instruction.
In one embodiment, the identifying, based on the pre-built convolutional neural network model, the service opening request of the internet of things to obtain the service type identification result includes:
extracting keywords of the internet of things service opening request; and converting the keywords into real vectors;
and inputting the real number vector into a pre-built convolutional neural network model for deep learning to obtain a service type identification result.
In an embodiment, the plurality of service opening requests of the internet of things identify the service opening requests of the internet of things based on a pre-built convolutional neural network model, and obtain a service type identification result, which includes:
extracting keywords of each Internet of things service opening request respectively;
converting the keywords into real vectors, obtaining a real vector set, and preprocessing the real vector set;
and inputting the preprocessed real number vector set into a pre-built convolutional neural network model for deep learning to obtain a service type identification result.
In one embodiment, the pre-built convolutional neural network model is a convolutional neural network model fused with a plurality of residual modules, and comprises a pooling layer and an overfitting-preventing Dropout layer connected with the pooling layer, wherein the pooling layer comprises a first layer of maximum pooling and a second layer of global average pooling.
In one embodiment, the evaluating the correctness of the service opening instruction of the internet of things includes:
performing vocabulary decomposition on the Internet of things service opening instruction to obtain a decomposition result;
acquiring dangerous word exchange rate of words in the decomposition result; the method comprises the steps of,
and evaluating the correctness of the service opening instruction of the Internet of things based on the dangerous vocabulary rate.
In an embodiment, the evaluating the correctness of the service opening instruction of the internet of things further includes:
acquiring correct vocabulary rate and parameter format correct rate of the vocabulary in the decomposition result;
the evaluating the correctness of the internet of things service opening instruction based on the dangerous vocabulary rate comprises the following steps: and evaluating the correctness of the service opening instruction of the Internet of things based on the dangerous word exchange rate, the correct word exchange rate and the parameter format correct rate.
In one embodiment, the obtaining the dangerous word exchange rate of the vocabulary in the decomposition result includes:
comparing the vocabulary in the decomposition result with the vocabulary types defined in the preset dangerous vocabulary library to obtain a comparison result; and calculating dangerous word exchange rate of the words in the decomposition result based on the comparison result.
In one embodiment, the preset dangerous vocabulary library comprises a low-risk vocabulary sub-library, a medium-risk vocabulary sub-library and a high-risk vocabulary word library, wherein the low-risk vocabulary sub-library defines a query command type; the medium-risk vocabulary sub-library defines parameter range types of different vocabularies; the high-risk vocabulary sub-library defines different parameters to correspond to different business types of the Internet of things;
the dangerous word exchange rate of the words in the decomposition result is calculated based on the comparison result, and the dangerous word exchange rate is obtained according to the following formula:
Y＝a*V1+b*V2+c*V3/(a+b+c)
wherein Y represents the dangerous word exchange rate, a represents the middle and low dangerous word quantity of the comparison result, b represents the middle and high dangerous word quantity of the comparison result, c represents the middle and high dangerous word quantity of the comparison result, V1, V2 and V3 respectively represent preset percentages, and V1 is less than V2 and less than V3.
In one embodiment, the correctness of the internet of things service opening instruction is evaluated based on the dangerous word exchange rate, the correct word exchange rate and the parameter format correct rate, and the method is obtained according to the following formula:
accuracy＝(A1+A2+A3)/(A1+A2+A3+B1+B2+B3)
in the formula, accuracy represents a value of correctness of the service opening instruction of the Internet of things, A1 represents a vocabulary correct rate, B1 represents a vocabulary error rate, A2 represents a parameter format correct rate, B2 represents a parameter format error rate, A3 represents a non-dangerous word exchange rate, and B3 represents a dangerous word exchange rate.
According to another aspect of the present application, there is provided an internet of things service provisioning apparatus, including:
the identification module is used for identifying the service opening request of the Internet of things based on the pre-built convolutional neural network model to obtain a service type identification result;
the instruction generation module is used for generating an Internet of things service opening instruction based on the service type identification result;
the evaluation module is used for evaluating the correctness of the service opening instruction of the Internet of things to obtain a value of the correctness;
and the opening module is set to respond to the correctness value being higher than a preset threshold value and open the corresponding internet of things service based on the internet of things service opening instruction.
According to still another aspect of the present application, there is provided an electronic apparatus including: a memory and a processor;
the memory stores computer-executable instructions;
and the processor executes the computer execution instructions stored in the memory, so that the electronic equipment executes the service opening method of the Internet of things.
According to still another aspect of the present application, there is provided a computer readable storage medium, where computer executable instructions are stored, where the computer executable instructions are used to implement the service provisioning method of the internet of things when executed by a processor.
According to the method, the device, the electronic equipment and the computer medium for opening the Internet of things service, the convolutional neural network is utilized to automatically identify various Internet of things services, the service type identification result is obtained, then the Internet of things service opening instruction is generated according to the service type identification result, the instruction correctness is evaluated, when the evaluation threshold is reached, the opening of the Internet of things service is realized based on the instruction, the Internet of things service opening efficiency and accuracy can be effectively improved, the labor cost is reduced, an efficient, convenient and high-accuracy Internet of things service opening scheme is provided for a user, and the user experience is improved.
Drawings
Fig. 1 is a schematic diagram of a possible application scenario provided in an embodiment of the present application;
fig. 2 is a flow chart of a method for opening an internet of things service according to an embodiment of the present application;
FIG. 3 is a diagram showing one possible example of step S201 in FIG. 2;
FIG. 4 is a diagram showing one possible example of step S204 in FIG. 2;
fig. 5 is one of flow diagrams of another method for opening an internet of things service according to an embodiment of the present application;
fig. 6 is a second flow chart of another method for opening services of the internet of things according to the embodiment of the present application;
FIG. 7 is an exemplary diagram of adjusting vocabulary and parameters in an instruction in an embodiment of the present application;
fig. 8 is a third flow chart of another method for opening services of the internet of things according to the embodiment of the present application;
fig. 9 is a schematic structural diagram of an internet of things service opening device provided in an embodiment of the present application;
fig. 10 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
At present, the service of the internet of things has different services for different types of users, the service types are various, the service opening of the users of the internet of things needs to manually make scripts, and then the scripts are operated on corresponding network elements. The error rate of manual script is high because of the great influence caused by the misoperation of the core network, and the supporting experience of the access province is influenced by the high error rate, so that the service use of users is seriously influenced.
Aiming at the technical problems, the embodiment of the application provides an automatic identification and opening scheme for the Internet of things service, which utilizes a convolutional neural network to realize the automatic identification of various Internet of things services, and then realizes the automatic manufacture of scripts for the identified services so as to generate an opening instruction for the Internet of things service, evaluate the correctness of the instruction and further realize the opening of the Internet of things service. According to the embodiment of the application, the service type is automatically identified through the convolutional neural network fusion residual error module, so that the accuracy of service classification is improved; and the colleagues synthesize various indexes to evaluate the instruction correctness, so that the instruction correctness is improved, the instruction issuing accuracy is improved, the error rate of manual production is reduced, the supporting experience of an access province is improved, and the service use experience of the Internet of things of the user is effectively improved.
For the purpose of making the objects, technical solutions and advantages of the present application more apparent, the technical solutions in the embodiments of the present application will be described in more detail below with reference to the accompanying drawings in the embodiments of the present application. In the drawings, the same or similar reference numerals refer to the same or similar components or components having the same or similar functions throughout. The described embodiments are some, but not all, of the embodiments of the present application. The embodiments described below by referring to the drawings are exemplary and intended for the purpose of explaining the present application and are not to be construed as limiting the present application. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are within the scope of the present disclosure.
Fig. 1 is a schematic diagram of one possible scenario provided in the embodiment of the present application, as shown in fig. 1, including a terminal device 110 and a server 120, where the terminal device 110 and the server 120 are connected to each other through a wired or wireless network. In some embodiments, the terminal 110 is configured to build a convolutional neural network model for identifying an internet of things service opening request, and provide a pre-built convolutional neural network model to the server 120, and the server 120 is configured to identify the internet of things service opening request based on the convolutional neural network model provided by the terminal 110, and generate an internet of things service opening instruction and open a corresponding internet of things service. Optionally, in building the convolutional neural network model, the server 120 takes on secondary computing work and the terminal 110 takes on primary computing work; alternatively, the server 120 takes over primary computing work and the terminal 110 takes over secondary computing work; alternatively, either the server 120 or the terminal 110, respectively, can solely undertake computing work.
The terminal device 110 may include, but is not limited to, a computer, a smart phone, a tablet computer, an e-book reader, a dynamic image expert compression standard audio layer 3 (Moving Picture experts group audio layer III, MP3 for short) player, a dynamic image expert compression standard audio layer 4 (Moving Picture experts group audio layer IV, MP4 for short) player, a portable computer, a car-mounted computer, a wearable device, a desktop computer, a set-top box, a smart television, and the like.
The server 120 may be an independent physical server, a server cluster or a distributed system formed by a plurality of physical servers, or may be a cloud server providing cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, a content delivery network (Content Delivery Network, CDN), basic cloud computing services such as big data and an artificial intelligence platform.
Alternatively, the number of the terminals 110 or the servers 120 may be more or less, which is not limited in the embodiment of the present application. In some embodiments, the terminal 110 and server 120 may also act as nodes in a blockchain system,
the scenario schematic diagram of the present application is briefly described above, and the method for opening the service of the internet of things provided in the embodiment of the present application is described in detail below by taking the server 120 applied in fig. 1 as an example.
Referring to fig. 2, fig. 2 is a flowchart of an internet of things service opening method according to an embodiment of the present application, where the method includes steps S201 to S204.
Step S201, identifying an Internet of things service opening request based on a pre-built convolutional neural network model, and obtaining a service type identification result.
In this embodiment, the request for opening the internet of things service, that is, the application for opening the internet of things service initiated by the user, may be one or more, may be a real-time request, or may be a history application work order, where the internet of things service, for example, black and white list making, MEC (Multi-access Edge Computing, edge cloud) interfaces with SMF (Service Management Function ), or UPF (User Port Function, user port function) opening, etc. In practical application, aiming at the application of opening the service of the Internet of things initiated by the user, the service type of the Internet of things is mainly identified manually, and the manual script is manufactured to realize opening of the corresponding service of the Internet of things for the user, so that the service type of the service of the Internet of things is identified by using the convolutional neural network model, the service type of the service of the Internet of things is identified, the opening efficiency of the service of the Internet of things is improved, the labor cost is saved, and the accuracy is higher.
The pre-built convolutional neural network model may be built and trained by the terminal 110 and transmitted to the server 120, and in some embodiments, the server 120 may also build and train the convolutional neural network model in advance and put into use.
In one embodiment, the pre-built convolutional neural network model is a convolutional neural network model fused with a plurality of residual modules, and comprises a pooling layer and an overfitting-preventing Dropout layer connected with the pooling layer, wherein the pooling layer comprises a first layer of maximum pooling and a second layer of global average pooling.
In this embodiment, the convolutional neural network model may include two or more layers, where the pooling operation of the final first layer adopts maximum pooling, the pooling operation of the second layer adopts global average pooling, and the maximum pooling and average pooling are used to achieve a better learning effect, and meanwhile, in order to prevent overfitting, a Dropout layer is added after the pooling operation, and one or more residual modules are fused in the neural network model, so as to improve the accuracy of classification.
In a more specific embodiment, in order to improve learning efficiency, the method for identifying an internet of things service opening request based on a pre-built convolutional neural network model, to obtain a service type identification result (step S201), includes the following steps:
extracting keywords of the internet of things service opening request; and converting the keywords into real vectors;
and inputting the real number vector into a pre-built convolutional neural network model for deep learning to obtain a service type identification result.
The key words in the service opening request of the Internet of things can be extracted by adopting a thermal coding technology, and it can be understood that thermal coding is One-Hot coding, also called One-bit effective coding, is that N states are coded by using an N-bit state register, each state has independent register bits, and only One bit is effective at any time.
In another embodiment, when the number of service opening requests of the internet of things is multiple, the real number vector set is preprocessed to further improve learning efficiency, and the service opening request of the internet of things is identified based on the pre-built convolutional neural network model, so as to obtain a service type identification result (step S201), which includes the following steps:
extracting keywords of each Internet of things service opening request respectively;
converting the keywords into real vectors, obtaining a real vector set, and preprocessing the real vector set;
and inputting the preprocessed real number vector set into a pre-built convolutional neural network model for deep learning to obtain a service type identification result.
In the above embodiment, by collecting all the application worksheets of the internet of things user, integrating the names of the application worksheets of the internet of things user service into one data set, then sending the data set into the convolutional neural network model for classification, extracting the characteristics of each worksheet type, and finally realizing automatic identification and classification of the internet of things service worksheets by the model according to the unique keywords and characteristics of each service worksheet. And the data set of the work order of the user service application of the Internet of things is converted into a real number vector by a thermal coding mode, and the convolutional neural network only can identify binary data, so that the collected data set can be input into a convolutional neural network model after data preprocessing. That is, data preprocessing, i.e., converting a real vector into a binary vector.
In some embodiments, to further improve accuracy of model classification, a service type feature library may be established, and in conjunction with fig. 3, a service type recognition model of the internet of things, that is, a convolutional neural network model, is shown in fig. 3, and a recognition result module is used to store a service type recognition result, and a service opening system corresponds to an opening module 94 in an embodiment of the device described later, and interacts with the service type feature library based on the convolutional neural network model, and the convolutional neural network model implements recognition of the service type of the internet of things based on features extracted from the network and features in the service type feature library, and obtains the service type recognition result, and completes opening of the service of the internet of things in a subsequent step.
And step S202, generating an Internet of things service opening instruction based on the service type identification result.
In this embodiment, the identified service type may be automatically manufactured by python to generate an internet of things service opening instruction, and in a specific implementation manner, a foreground and background interoperation platform based on Django may be built according to a 5G internet of things security system, file data derived from a network management may be extracted based on the corresponding internet of things service in the service type identification result, and the automatic manufacturing of the instruction may be implemented by combining a front end page and inputting necessary parameters according to a customer requirement. That is, the internet of things service opening instruction includes the internet of things service type and parameters of the internet of things service.
It can be understood that the request for opening the service of the internet of things carries a parameter corresponding to the requirement of the client, and the parameter can be an operation parameter corresponding to the service of opening the internet of things.
And step 203, evaluating the correctness of the service opening instruction of the Internet of things to obtain a value of the correctness.
In some embodiments, the correctness of the instruction can be evaluated by analyzing and evaluating the vocabulary in the service opening instruction of the internet of things, and a corresponding evaluation value is obtained, and the specific process is described in detail in the embodiments described later.
And step S204, responding to the correctness value being higher than a preset threshold value, opening the corresponding Internet of things service based on the Internet of things service opening instruction, and ending the flow and opening the Internet of things service according to the prior art if not.
In this embodiment, the opening of the internet of things service is achieved by using the internet of things service opening instruction automatically generated by the system, in one example, in order to further ensure the opening of the internet of things service, the internet of things service opening instruction is sent to the network manager to perform operation execution, as shown in fig. 4, the internet of things opening instruction is to ADD a physical network element slice ADD PNFNS, taking the internet of things service as an example, and in fig. 4, the internet of things opening instruction carries various parameters and index information, so as to facilitate the rapid opening of the internet of things service.
It should be noted that, a person skilled in the art may adaptively set the preset value in connection with the actual application.
In the related art, when the service of the internet of things is opened, accuracy evaluation is not generally performed on an opening instruction or a script of the service of the internet of things, so that the opening error rate of the service of the internet of things is high.
Referring to fig. 5, fig. 5 is a flow chart of another method for opening an internet of things service provided in the embodiment of the present application, and based on the above embodiment, the present embodiment further illustrates a process for evaluating the correctness of an internet of things service opening instruction, where the evaluating the correctness of the internet of things service opening instruction (step S203) includes steps S501-S503.
And step S501, performing vocabulary decomposition on the Internet of things service opening instruction to obtain a decomposition result.
In this embodiment, the internet of things service type and the corresponding parameters thereof to be opened are carried in the internet of things service opening instruction, and all the vocabularies carried in the instruction are analyzed by performing vocabulary decomposition on the instruction, so that the correctness of the instruction is obtained, and the accuracy evaluation result of the instruction can be effectively improved.
Step S502, acquiring dangerous word exchange rate of words in the decomposition result.
In this embodiment, the accuracy of the instruction is evaluated by obtaining the dangerous word exchange rate of the decomposed word, where the dangerous word, that is, the word that may affect the network, may be classified into a low-risk word, a medium-risk word, and a high-risk word according to the degree of influence on the network, where the higher the dangerous word exchange rate, the lower the accuracy.
In one embodiment, the dangerous word exchange rate is obtained by constructing a preset dangerous word library and comparing the words in the decomposition result with the word types defined in the preset dangerous word library, so as to improve the recognition effect of the dangerous word exchange rate, specifically, the dangerous word exchange rate of the words in the decomposition result is obtained (step S502), and the method comprises the following steps:
comparing the vocabulary in the decomposition result with the vocabulary types defined in the preset dangerous vocabulary library to obtain a comparison result; and calculating dangerous word exchange rate of the words in the decomposition result based on the comparison result.
Further, the preset dangerous vocabulary library comprises a low-risk vocabulary sub-library, a medium-risk vocabulary sub-library and a high-risk vocabulary sub-library, wherein the low-risk vocabulary sub-library defines a query command type; the medium-risk vocabulary sub-library defines parameter range types of different vocabularies; and the high-risk vocabulary sub-library defines that different parameters correspond to different business types of the Internet of things.
Illustratively, the present embodiment constructs the set of important parameters by: constructing a low-risk vocabulary library: classifying query commands such as LST, DSP and the like into low-risk words; building a medium-risk vocabulary library: setting a certain range for the corresponding parameters, wherein the out-of-range instruction is a medium-risk instruction; constructing a high-risk vocabulary library: defining different parameters as different types, for example, APN (Access Point Name ) type as xxxxx.jnm2mapn, xxxxx.jnlot; in addition, in order to facilitate the opening of the service of the internet of things, parameters uploaded by the user (parameters carried by the instruction) and parameters in an operation log on the device (for example, the maximum value of the index value) can be collected, and a json set of related parameters, such as (index maximum value: 15), is constructed for opening the service.
The dangerous word exchange rate of the words in the decomposition result is calculated based on the comparison result, and the dangerous word exchange rate is obtained according to the following formula:
Y＝a*V1+b*V2+c*V3/(a+b+c)
wherein Y represents the dangerous word exchange rate, a represents the middle and low dangerous word quantity of the comparison result, b represents the middle and high dangerous word quantity of the comparison result, c represents the middle and high dangerous word quantity of the comparison result, V1, V2 and V3 respectively represent preset percentages, and V1 is less than V2 and less than V3.
Exemplary, dangerous word exchange rate=20% + medium risk 30% + high risk 50%/(low risk+medium risk+high risk), and corresponding non-dangerous word exchange rate is 1-20% + medium risk 30% + high risk 50%/(low risk+medium risk+high risk).
And step S503, evaluating the correctness of the service opening instruction of the Internet of things based on the dangerous vocabulary rate.
In one implementation, a dangerous threshold may be set, and by comparing the dangerous word exchange rate with the dangerous threshold, when the dangerous word exchange rate is less than the threshold, a preset threshold for correctness assessment may be considered to be reached.
In one embodiment, when evaluating the service type of the internet of things, besides considering the vocabulary risk, problems such as vocabulary errors and parameter format errors may occur, which may result in the service opening efficiency of the internet of things, as shown in fig. 6, the evaluating the correctness of the service opening instruction of the internet of things (step S203) further includes step S504.
Step S504, obtaining the correct vocabulary rate and the parameter format correct rate of the vocabulary in the decomposition result.
Wherein the correct vocabulary exchange rate of the vocabulary is obtained, whether the vocabulary is correct, such as spelling is correct; the parameter format accuracy is obtained, and it can be understood that the parameter format is general for opening the service of the internet of things, and if the parameter format carried in the instruction is different from the general parameter format, the parameter format is considered to be incorrect.
The evaluating the correctness of the internet of things service opening instruction based on the dangerous vocabulary rate (step S503) specifically includes: and step S503a, evaluating the correctness of the service opening instruction of the Internet of things based on the dangerous word exchange rate, the correct word exchange rate and the parameter format correct rate.
Compared with the method for evaluating the correctness of the vocabulary by only dangerous word exchange rate, the method for evaluating the correctness of the vocabulary by the aid of the dangerous word exchange rate increases the correct word exchange rate and parameter format correct rate, and can effectively improve the accuracy of an evaluation result.
In one embodiment, the correctness of the internet of things service opening instruction is evaluated based on the dangerous word exchange rate, the correct word exchange rate and the parameter format correct rate, and the method is obtained according to the following formula:
accuracy＝(A1+A2+A3)/(A1+A2+A3+B1+B2+B3)
in the formula, accuracy represents a value of correctness of the service opening instruction of the Internet of things, A1 represents a vocabulary correct rate, B1 represents a vocabulary error rate, A2 represents a parameter format correct rate, B2 represents a parameter format error rate, A3 represents a non-dangerous word exchange rate, and B3 represents a dangerous word exchange rate. For ease of understanding, a1+b1=1, a2+b2=1, a3+b3=1, refer to table 1 below.
Table 1:
in an example, if the accuracy is greater than or equal to 99% (the preset threshold value), opening the internet of things service based on the internet of things service, ending the flow when the accuracy is less than 99%, and adjusting error vocabulary, parameters which do not conform to the format and the like in the internet of things service opening instruction, wherein the process of adjusting the vocabulary and the parameters in the instruction can be shown in fig. 7, and further regenerating the internet of things service opening instruction; in another example, when the accuracy is less than 99%, the accuracy is more than or equal to 95% and the accuracy is less than or equal to 98% of the instructions can be automatically searched and checked, the instructions are updated, and if the accuracy is less than or equal to 94%, the vocabulary, parameters and the like in the service opening instruction of the internet of things are adjusted, and the service opening instruction of the internet of things is regenerated.
For the understanding of the embodiments of the present application, with reference to fig. 8, fig. 8 provides a second flowchart of the embodiments of the present application, and it should be noted that, in the drawing, black and white list creation, MEC and SMF docking, and UPF opening between the neural network model and instruction creation are three different service types of the internet of things.
Based on the same technical concept, the embodiment of the present application correspondingly further provides an internet of things service opening device, as shown in fig. 9, where the device includes:
the identification module 91 is configured to identify an internet of things service opening request based on a pre-built convolutional neural network model, and obtain a service type identification result;
an instruction generation module 92 configured to generate an internet of things service activation instruction based on the service type identification result;
the evaluation module 93 is configured to evaluate the correctness of the service opening instruction of the internet of things, and obtain a value of the correctness;
and an opening module 94, configured to open the corresponding internet of things service based on the internet of things service opening instruction in response to the correctness value being higher than a preset threshold.
In one embodiment, the identification module 91 includes:
the first extraction unit is used for extracting keywords of the internet of things service opening request; and a first conversion unit configured to convert the keyword into a real number vector;
the first learning unit is used for inputting the real number vector into a pre-built convolutional neural network model to perform deep learning, and a service type identification result is obtained.
In one embodiment, the plurality of internet of things service opening requests, the identifying module 91 includes:
the second extraction unit is used for respectively extracting keywords of each Internet of things service opening request;
the second conversion unit is used for converting the keywords into real vectors, obtaining a real vector set and preprocessing the real vector set;
the second learning unit is used for inputting the preprocessed real number vector set into a pre-built convolutional neural network model for deep learning, and a service type recognition result is obtained.
In one embodiment, the pre-built convolutional neural network model is a convolutional neural network model fused with a plurality of residual modules, and comprises a pooling layer and an overfitting-preventing Dropout layer connected with the pooling layer, wherein the pooling layer comprises a first layer of maximum pooling and a second layer of global average pooling.
In one embodiment, the evaluation module 93 includes:
the decomposition unit is used for carrying out vocabulary decomposition on the business opening instruction of the Internet of things to obtain a decomposition result;
the first acquisition unit is used for acquiring dangerous word exchange rate of words in the decomposition result; the method comprises the steps of,
and the evaluation unit is used for evaluating the correctness of the internet of things service opening instruction based on the dangerous vocabulary rate.
In one embodiment, the evaluation module 93 further includes:
the second acquisition unit is used for acquiring the correct word exchange rate and the parameter format correct rate of the words in the decomposition result;
the evaluation unit is specifically configured to evaluate the correctness of the internet of things service opening instruction based on the dangerous word exchange rate, the correct word exchange rate and the parameter format correct rate.
In one embodiment, the first obtaining unit is specifically configured to compare the vocabulary in the decomposition result with the vocabulary types defined in the preset dangerous vocabulary library to obtain a comparison result; and calculating dangerous word exchange rate of the words in the decomposition result based on the comparison result.
In one embodiment, the preset dangerous vocabulary library comprises a low-risk vocabulary sub-library, a medium-risk vocabulary sub-library and a high-risk vocabulary word library, wherein the low-risk vocabulary sub-library defines a query command type; the medium-risk vocabulary sub-library defines parameter range types of different vocabularies; the high-risk vocabulary sub-library defines different parameters to correspond to different business types of the Internet of things;
the dangerous word exchange rate of the words in the decomposition result is calculated based on the comparison result, and the dangerous word exchange rate is obtained according to the following formula:
Y＝a*V1+b*V2+c*V3/(a+b+c)
wherein Y represents the dangerous word exchange rate, a represents the middle and low dangerous word quantity of the comparison result, b represents the middle and high dangerous word quantity of the comparison result, c represents the middle and high dangerous word quantity of the comparison result, V1, V2 and V3 respectively represent preset percentages, and V1 is less than V2 and less than V3.
In one embodiment, the correctness of the internet of things service opening instruction is evaluated based on the dangerous word exchange rate, the correct word exchange rate and the parameter format correct rate, and the method is obtained according to the following formula:
accuracy＝(A1+A2+A3)/(A1+A2+A3+B1+B2+B3)
in the formula, accuracy represents a value of correctness of the service opening instruction of the Internet of things, A1 represents a vocabulary correct rate, B1 represents a vocabulary error rate, A2 represents a parameter format correct rate, B2 represents a parameter format error rate, A3 represents a non-dangerous word exchange rate, and B3 represents a dangerous word exchange rate.
Based on the same technical concept, the embodiment of the present application correspondingly further provides an electronic device, as shown in fig. 10, where the electronic device includes: a memory 101 and a processor 102;
the memory 101 stores computer-executable instructions;
the processor 102 executes the computer-executed instructions stored in the memory 101, so that the electronic device executes the service opening method of the internet of things.
Based on the same technical concept, the embodiment of the application correspondingly provides a computer readable storage medium, wherein computer execution instructions are stored in the computer readable storage medium, and the computer execution instructions are used for realizing the service opening method of the internet of things when being executed by a processor.
Those of ordinary skill in the art will appreciate that all or some of the steps, systems, functional modules/units in the apparatus, and methods disclosed above may be implemented as software, firmware, hardware, and suitable combinations thereof. In a hardware implementation, the division between the functional modules/units mentioned in the above description does not necessarily correspond to the division of physical components; for example, one physical component may have multiple functions, or one function or step may be performed cooperatively by several physical components. Some or all of the physical components may be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application specific integrated circuit. Such software may be distributed on computer readable media, which may include computer storage media (or non-transitory media) and communication media (or transitory media).
The term computer storage media includes both volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data, as known to those skilled in the art. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital Versatile Disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by a computer.
Furthermore, as is well known to those of ordinary skill in the art, communication media typically embodies computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media.
In the description of the embodiments of the present application, the term "and/or" merely represents an association relationship describing an association object, which means that three relationships may exist, for example, a and/or B may represent: a exists alone, A and B exist together, and B exists alone. In addition, the term "at least one" means any combination of any one or at least two of the plurality, e.g., including at least one of A, B, may mean any one or more elements selected from the set consisting of A, B and C communication. Furthermore, the term "plurality" means two or more, unless specifically stated otherwise.
In the description of embodiments of the present application, the terms "first," "second," "third," "fourth," and the like (if any) are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that embodiments of the present application described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present application.
Claims (10)
1. The method for opening the service of the Internet of things is characterized by comprising the following steps:
identifying an Internet of things service opening request based on a pre-built convolutional neural network model to obtain a service type identification result;
generating an Internet of things service opening instruction based on the service type identification result;
evaluating the correctness of the service opening instruction of the Internet of things to obtain a value of the correctness;
responding to the correctness value being higher than a preset threshold value, and opening corresponding Internet of things service based on the Internet of things service opening instruction;
the evaluating the correctness of the internet of things service opening instruction comprises the following steps: performing vocabulary decomposition on the Internet of things service opening instruction to obtain a decomposition result; acquiring dangerous word exchange rate of words in the decomposition result; and evaluating the correctness of the internet of things service opening instruction based on the dangerous vocabulary rate;
the step of obtaining the dangerous word exchange rate of the words in the decomposition result comprises the following steps:
comparing the vocabulary in the decomposition result with the vocabulary types defined in the preset dangerous vocabulary library to obtain a comparison result; calculating dangerous word exchange rate of words in the decomposition result based on the comparison result; the preset dangerous vocabulary library comprises a low-risk vocabulary sub-library, a medium-risk vocabulary sub-library and a high-risk vocabulary sub-library, wherein the low-risk vocabulary sub-library defines a query command type; the medium-risk vocabulary sub-library defines parameter range types of different vocabularies; and the high-risk vocabulary sub-library defines that different parameters correspond to different business types of the Internet of things.
2. The method of claim 1, wherein the identifying the service opening request of the internet of things based on the pre-built convolutional neural network model to obtain the service type identification result comprises:
extracting keywords of the internet of things service opening request; and converting the keywords into real vectors;
and inputting the real number vector into a pre-built convolutional neural network model for deep learning to obtain a service type identification result.
3. The method of claim 1, wherein the plurality of internet of things service opening requests identify the internet of things service opening requests based on a pre-built convolutional neural network model, and obtain a service type identification result, comprising:
extracting keywords of each Internet of things service opening request respectively;
converting the keywords into real vectors, obtaining a real vector set, and preprocessing the real vector set;
and inputting the preprocessed real number vector set into a pre-built convolutional neural network model for deep learning to obtain a service type identification result.
4. A method according to any of claims 1-3, wherein the pre-built convolutional neural network model is a convolutional neural network model with several residual modules integrated, comprising a pooling layer and an overfitting-preventing Dropout layer connected to the pooling layer, the pooling layer comprising a first layer maximum pooling and a second layer global average pooling.
5. The method of claim 1, wherein the evaluating the correctness of the internet of things service provisioning instruction further comprises:
acquiring correct vocabulary rate and parameter format correct rate of the vocabulary in the decomposition result;
the evaluating the correctness of the internet of things service opening instruction based on the dangerous vocabulary rate comprises the following steps: and evaluating the correctness of the service opening instruction of the Internet of things based on the dangerous word exchange rate, the correct word exchange rate and the parameter format correct rate.
6. The method of claim 1, wherein the calculating the dangerous word exchange rate of the vocabulary in the decomposition result based on the comparison result is obtained according to the following formula:
Y＝a*V1+b*V2+c*V3/(a+b+c)
wherein Y represents the dangerous word exchange rate, a represents the middle and low dangerous word quantity of the comparison result, b represents the middle and high dangerous word quantity of the comparison result, c represents the middle and high dangerous word quantity of the comparison result, V1, V2 and V3 respectively represent preset percentages, and V1 is less than V2 and less than V3.
7. The method of claim 5, wherein the correctness of the internet of things service provisioning instruction is evaluated based on the dangerous word exchange rate, the correct word exchange rate and the parameter format correct rate, and is obtained according to the following formula:
accuracy＝(A1+A2+A3)/(A1+A2+A3+B1+B2+B3)
in the formula, accuracy represents a value of correctness of the service opening instruction of the Internet of things, A1 represents a vocabulary correct rate, B1 represents a vocabulary error rate, A2 represents a parameter format correct rate, B2 represents a parameter format error rate, A3 represents a non-dangerous word exchange rate, and B3 represents a dangerous word exchange rate.
8. The utility model provides a thing networking service opening device which characterized in that includes:
the identification module is used for identifying the service opening request of the Internet of things based on the pre-built convolutional neural network model to obtain a service type identification result;
the instruction generation module is used for generating an Internet of things service opening instruction based on the service type identification result;
the evaluation module is used for evaluating the correctness of the service opening instruction of the Internet of things to obtain a value of the correctness;
the opening module is set to respond to the fact that the correctness value is higher than a preset threshold value, and corresponding Internet of things service is opened based on the Internet of things service opening instruction;
wherein the evaluation module comprises: the decomposition unit is used for carrying out vocabulary decomposition on the business opening instruction of the Internet of things to obtain a decomposition result; the first acquisition unit is used for acquiring dangerous word exchange rate of words in the decomposition result; the evaluation unit is used for evaluating the correctness of the internet of things service opening instruction based on the dangerous vocabulary rate;
the first acquisition unit is specifically configured to compare the vocabulary in the decomposition result with the vocabulary types defined in the preset dangerous vocabulary library to obtain a comparison result; calculating dangerous word exchange rate of words in the decomposition result based on the comparison result; the preset dangerous vocabulary library comprises a low-risk vocabulary sub-library, a medium-risk vocabulary sub-library and a high-risk vocabulary sub-library, wherein the low-risk vocabulary sub-library defines a query command type; the medium-risk vocabulary sub-library defines parameter range types of different vocabularies; and the high-risk vocabulary sub-library defines that different parameters correspond to different business types of the Internet of things.
9. An electronic device, comprising: a memory and a processor;
the memory stores computer-executable instructions;
the processor executes the computer-executable instructions stored in the memory, so that the electronic device executes the internet of things service opening method according to any one of claims 1 to 7.
10. A computer readable storage medium, wherein computer executable instructions are stored in the computer readable storage medium, and when executed by a processor, the computer executable instructions are configured to implement the method for opening services of the internet of things according to any one of claims 1-7.
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