CN111835800A - Internet of things service attribute identification method and device - Google Patents
Internet of things service attribute identification method and device Download PDFInfo
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Abstract
The embodiment of the invention provides a method and a device for identifying service attributes of an Internet of things, which are used for acquiring signaling data of a whole network user transmitted through a target network interface; classifying the signaling data according to the Internet of things identifiers contained in the signaling data to obtain a signaling data set corresponding to each Internet of things identifier; determining a characteristic value of a designated service performance index of the target Internet of things corresponding to the Internet of things identification based on the signaling data set; and determining the service attribute information of the target Internet of things according to the characteristic value of the designated service performance index. The method comprises the steps of carrying out big data analysis on fields related to service performance indexes in a plurality of signaling data under the same Internet of things, determining characteristic values of all specified service performance indexes influencing the service quality of the Internet of things from multiple dimensions, accurately identifying service attribute information of a target Internet of things based on index types corresponding to the characteristic values, and improving the evaluation efficiency and the evaluation accuracy of the service quality of the Internet of things.
Description
Technical Field
The invention relates to the technical field of mobile communication, in particular to a method and a device for identifying service attributes of an Internet of things.
Background
The internet of things extends and expands the user side of the internet of things to any object to object, and performs a network behavior of information exchange and communication. Under the scene of the internet of things, business objects are often 'objects' rather than 'people', and subjective perception evaluation and feedback are lacked.
The existing technology for evaluating the business behavior of the internet of things mainly adopts means such as OMC data, manual testing or terminal operation testing and the like, and generally analyzes and evaluates the business behavior of the internet of things in a one-user-one-case mode, however, the business of the internet of things is various in types, and the evaluation is carried out in a one-user-one-case mode, so that a great deal of time and energy are needed to be invested in each business of the internet of things for research and testing, the result is often unsatisfactory, sometimes, the performance of the business of the internet of things of the same type is shown to have great difference under different time and different environments, particularly, the types of the business of the internet of things with less quantity are kept, index fluctuation is obvious, the efficiency is low, the limitation is.
Therefore, the accuracy and the effectiveness of the service quality evaluation result of the existing Internet of things are not high.
Disclosure of Invention
The embodiment of the invention aims to provide a method and a device for identifying service attributes of internet of things, and aims to solve the problems that in the prior art, the service behaviors of the internet of things are analyzed and evaluated in a one-user-one-case mode, each internet of things requires more time and labor investment, the efficiency is low, the limitation is large, and the accuracy is low.
In order to solve the above technical problem, the embodiment of the present invention is implemented as follows:
in a first aspect, an embodiment of the present invention provides an internet of things service attribute identification method, including:
acquiring signaling data of a whole network user transmitted through a target network interface;
classifying the signaling data according to the Internet of things identifiers contained in the signaling data to obtain a signaling data set corresponding to each Internet of things identifier;
determining a characteristic value of a designated service performance index of the target Internet of things corresponding to the Internet of things identifier based on the signaling data set;
and determining the service attribute information of the target Internet of things according to the characteristic value of the designated service performance index.
In a second aspect, an embodiment of the present invention provides an apparatus for identifying service attributes of an internet of things, including:
the signaling data acquisition module is used for acquiring the signaling data of the whole network user transmitted through the target network interface;
the signaling data classification module is used for classifying the signaling data according to the Internet of things identifiers contained in the signaling data to obtain a signaling data set corresponding to each Internet of things identifier;
the characteristic value determining module is used for determining the characteristic value of the designated service performance index of the target internet of things corresponding to the internet of things identifier based on the signaling data set;
and the service attribute information determining module is used for determining the service attribute information of the target Internet of things according to the characteristic value of the specified service performance index.
In a third aspect, an embodiment of the present invention provides a computer device, including a processor, a communication interface, a memory, and a communication bus; the processor, the communication interface and the memory complete mutual communication through a bus; the memory is used for storing a computer program; the processor is configured to execute the program stored in the memory, and implement the steps of the method for identifying the service attribute of the internet of things according to the first aspect.
In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the steps of the method for identifying service attributes of an internet of things according to the first aspect are implemented.
The method and the device for identifying the service attribute of the Internet of things in the embodiment of the invention acquire signaling data of a whole network user transmitted through a target network interface; classifying the signaling data according to the Internet of things identifiers contained in the signaling data to obtain a signaling data set corresponding to each Internet of things identifier; determining a characteristic value of a designated service performance index of the target Internet of things corresponding to the Internet of things identification based on the signaling data set; and determining the service attribute information of the target Internet of things according to the characteristic value of the designated service performance index. The method comprises the steps of carrying out big data analysis on fields related to service performance indexes in a plurality of signaling data under the same Internet of things, determining characteristic values of all specified service performance indexes influencing the service quality of the Internet of things from multiple dimensions, accurately identifying service attribute information of a target Internet of things based on index types corresponding to the characteristic values, and improving the evaluation efficiency and the evaluation accuracy of the service quality of the Internet of things.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below, it is obvious that the drawings in the following description are only some embodiments described in the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
Fig. 1 is a first flowchart of a service attribute identification method for the internet of things according to an embodiment of the present invention;
fig. 2 is a second flowchart of the method for identifying service attributes of the internet of things according to the embodiment of the present invention;
fig. 3 is a third flowchart illustrating a method for identifying service attributes of the internet of things according to an embodiment of the present invention;
fig. 4 is a fourth flowchart illustrating a service attribute identification method for the internet of things according to an embodiment of the present invention;
fig. 5 is a fifth flowchart of the method for identifying service attributes of the internet of things according to the embodiment of the present invention;
fig. 6 is a sixth flowchart of the method for identifying service attributes of the internet of things according to the embodiment of the present invention;
fig. 7 is a seventh flowchart illustrating a method for identifying service attributes of the internet of things according to an embodiment of the present invention;
fig. 8 is a schematic diagram illustrating a module composition of an internet of things service attribute identification apparatus according to an embodiment of the present invention;
fig. 9 is a schematic structural diagram of a computer device according to an embodiment of the present invention.
Detailed Description
In order to make those skilled in the art better understand the technical solution of the present invention, the technical solution in the embodiment of the present invention will be clearly and completely described below with reference to the drawings in the embodiment of the present invention, and it is obvious that the described embodiment is only a part of the embodiment of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The embodiment of the invention provides an Internet of things service attribute identification method and device, which are used for carrying out big data analysis on fields related to service performance indexes in a plurality of signaling data under the same Internet of things, determining characteristic values of all specified service performance indexes influencing the service quality of the Internet of things from multiple dimensions, accurately identifying the service attribute information of a target Internet of things based on the index types corresponding to the characteristic values, and improving the evaluation efficiency and the evaluation accuracy of the service quality of the Internet of things.
Fig. 1 is a first flowchart of a service attribute identification method for an internet of things according to an embodiment of the present invention, and as shown in fig. 1, the method at least includes the following steps:
step S101, acquiring signaling data of a whole network user transmitted through a target network interface, wherein the target network interface is an S1-U interface, and the signaling data of the whole network user transmitted through the S1-U interface is acquired through a core network interface through which service data of the Internet of things pass, so that the signaling data of multiple Internet of things can be acquired at the same time, it is to be noted that the acquired signaling data can be ten million-level data, one or more embodiments of the present specification mainly aim at the acquisition of a whole amount of client signaling data transmitted through an SI-U interface, specifically, the acquired SI-U signaling data is analyzed through the acquisition of signaling data of an important interface in the Internet of things, such as an SI-U interface, so as to acquire related field information capable of representing service performance of the Internet of things, such as identification APN, the method comprises the following steps of analyzing relevant fields of collected signaling data, such as uplink flow, downlink flow, a bearer protocol, a user identification code IMSI, a Cell identification Cell ID and the like, and extracting important indexes influencing the service quality of the Internet of things, wherein the important indexes comprise the following steps: the method comprises the following steps that indexes such as interactivity of the internet of things, service sending frequency, ratio condition of uplink sending flow and downlink sending flow, the quantity of each time of sending service flow, whether a service using area is changed frequently and the like are adopted, collected signaling data are analyzed through the service indexes, and certain service behaviors of the internet of things can be effectively identified;
step S102, classifying the signaling data according to the Internet of things identification contained in the signaling data to obtain a signaling data set corresponding to each Internet of things identification, wherein the Internet of things identification is an APN field in the signaling data sent by a user in each business development process; because the signaling data acquired through the core interface of the internet of things is the signaling data of all the internet of things, when service data of a certain internet of things is analyzed, all the signaling data in a certain period of time under the internet of things need to be analyzed; therefore, after the signaling data acquired through the core interface of the internet of things is acquired, the signaling data with the same internet of things identifier needs to be classified into the same signaling data set according to the internet of things identifier APN contained in the signaling data;
step S103, determining a characteristic value of the designated service performance index of the target Internet of things corresponding to the Internet of things identification based on the signaling data set, wherein the characteristic value of the designated service performance index of the target Internet of things is determined according to a type division rule corresponding to the designated service performance index in the target Internet of things;
step S104, determining service attribute information of the target Internet of things according to the characteristic value of the designated service performance index;
specifically, for example: determining a designated service performance index of the target internet of things corresponding to the internet of things identifier as a service distribution area index based on the signaling data set; the type division rule is as follows: all the user sources in the target internet of things are divided into three types (d)1、d2、d3) And analyzing, wherein all users in the Internet of things are in the same local city and are considered to be local city level business (d)1) (ii) a All users in the internet of things belong to the same province and different cities, and the users are considered as provincial services (d)2) (ii) a All users in the internet of things come from all over the country and are considered as national services (d)3) (ii) a And determining a user identification code IMSI contained in each signaling data in the signaling data set based on the signaling data set, determining attribution information of the target Internet of things user according to each user identification code, and determining that the service subsection area of the target Internet of things is national service if the target Internet of things user is determined to be from all over the country according to the user identification code.
In the embodiment of the invention, the characteristic values of all specified service performance indexes influencing the service quality of the Internet of things are determined from multiple dimensions by performing big data analysis on fields related to the service performance indexes in a plurality of signaling data of the same Internet of things, so that the service attribute information of the target Internet of things is accurately identified based on the index types corresponding to the characteristic values, and the evaluation efficiency and the evaluation accuracy of the service quality of the Internet of things are improved.
The method comprises the following steps of analyzing download flow in a signaling data field in a target Internet of things collected from an SI-U interface, and evaluating whether the flow service of the target Internet of things in a preset time period is a small flow service, a medium flow service or a large flow service, wherein the specified service performance indexes comprise: a service downloading flow index; as shown in fig. 2, in step S103, determining, based on the signaling data set, a feature value of a specified service performance index of the target internet of things corresponding to the internet of things identifier includes:
step S1031, determining service download flow contained in each signaling data in the signaling data set, wherein the signaling data is generated when each networking user under the target Internet of things is in service, and the signaling data contains field information such as user identification IMSI and service download flow; specifically, the method comprises the steps of determining service download flow generated by downloading services of all networking users under the target internet of things through a signaling data set of the target internet of things acquired within preset time, and counting the service download flow generated by downloading services of all the networking users under the target internet of things;
step S1032, determining the number of the networking users of the target Internet of things corresponding to the Internet of things identification according to the user identification contained in the signaling data set, specifically, determining the number of all the networking users of the target Internet of things in a preset time period according to the IMSI field information of different user identifications contained in the signaling data when all the networking users contained in the signaling data set carry out downloading service;
step S1033, based on the current-carrying capacity of each service and the number of networked users, determining the average service download flow of the target Internet of things, specifically, determining the average service download flow of the target Internet of things according to the sum of the service download flows generated by all users for downloading services in the target Internet of things and the number of the users for generating the current-carrying capacity service in the identified target Internet of things in the obtained preset time period, and then determining the attribute information of the service download flow of the target Internet of things according to the determined average service download flow and the type division rule corresponding to the obtained service download flow performance index;
for example: the target internet of things is Zhangzhou city public Security office internet of things, the total downloading flow of services generated under the internet of things in a preset time period is calculated to be 183MB, the number of users is 1269, and the calculated average downloading flow is 147 KB/person; the type division rule corresponding to the service download flow performance index is as follows: dividing the service download flow into 3 grades (n1, n2 and n3), and regarding the service with the average flow less than 1Kb per household as the low flow service (n 1); for traffic with an average traffic flow per household between 1Kb and 5Kb, consider medium flow traffic (n 2); traffic with an average traffic per user greater than 5Kb is considered high traffic (n 3); therefore, in a preset time period, the attribute information of the flow downloaded by the internet of things service of the Zhangzhou city public bureau is a high-flow service.
The method comprises the following steps of analyzing the ratio of uplink flow to downlink flow in a preset time period by analyzing the uplink flow and the downlink flow in a signaling data field in a target internet of things collected in an SI-U interface, so as to reflect the service type of the target internet of things in the preset time period, wherein the specified service performance indexes comprise: service uplink and downlink flow rate ratio index; as shown in fig. 3, in step S103, determining, based on the signaling data set, a feature value of a specified service performance index of the target internet of things corresponding to the internet of things identifier includes:
step S1034, determining the uplink flow and the downlink flow contained in each signaling data in the signaling data set;
step 1035, determining an uplink flow total value and a downlink flow total value of the signaling data set according to each uplink flow and each downlink flow;
step S1036, determining a service uplink and downlink flow ratio of a target Internet of things corresponding to the Internet of things identifier according to the uplink flow total value and the downlink flow total value;
the signaling data is generated when each networking user under the target internet of things generates services, and the signaling data comprises field information such as service uplink flow, service downlink flow and the like; specifically, through a signaling data set of a target internet of things acquired within a preset time, determining uplink traffic and downlink traffic of a service generated when each networked user under the target internet of things performs a service action, further respectively determining a total uplink traffic value and a total downlink traffic value generated when all networked users under the target internet of things perform services, and calculating to obtain a ratio of the total uplink traffic value to the downlink traffic value; then, determining business uplink and downlink flow ratio attribute information of the target Internet of things according to the determined ratio information of the total uplink flow value and the downlink flow and the type division rule corresponding to the obtained business uplink and downlink flow ratio index;
for example: the target internet of things is Zhangzhou city public Security office internet of things, and the total value of uplink flow generated under the internet of things in a preset time period is calculated to be 150MB, the total value of downlink flow is 183MB, and the ratio of uplink flow to downlink flow of business is 0.82; the type division rule corresponding to the performance index of the uplink-downlink traffic ratio of the service is as follows: analyzing the ratio of the uplink traffic to the downlink traffic of the service, dividing the ratio into 3 grades (m1, m2 and m3), and if the ratio of the uplink traffic to the downlink traffic is less than 0.8, determining the service is a download-type service (m 1); if the ratio of the uplink and downlink flows is between 0.8 and 1.2, the service is considered to be interactive (m 2); if the ratio of the uplink and downlink flows is more than 1.2, the service is considered to be an uploading service (m 3); therefore, in a preset time period, the uplink-downlink flow ratio of the internet of things service of Zhangzhou city public Security office is 0.82, and is between 0.8 and 1.2, so that the service is an interactive service;
the method comprises the steps that the frequency of sending services of each networking user under the target Internet of things collected in an SI-U interface is analyzed, and the frequency of sending the services of each networking user in a preset time period is analyzed, so that the frequency of sending the services of the target Internet of things in a preset time can be reflected; the specified service performance indexes include: service sending frequency index; as shown in fig. 4, in step S103, determining, based on the signaling data set, a feature value of a specified service performance index of the target internet of things corresponding to the internet of things identifier includes:
step S1037, determining the number of signaling data contained in the signaling data set;
step S1038, according to the user identification contained in the signaling data set, determining the number of networking users of the target Internet of things corresponding to the Internet of things identification;
step S1039, determining a service sending frequency of the target internet of things according to the number of the signaling data and the number of the networking users.
Specifically, the signaling data is generated when each networking user in the target internet of things has a service, and the signaling data includes field information such as a user identifier IMSI and a service type; specifically, the number of networking users of the target Internet of things is determined according to a user identifier IMSI contained in a signaling data set through the signaling data set of the target Internet of things acquired within a preset time; determining the quantity of signaling data contained in the signaling data set, namely determining the total quantity of services generated by the networking users in a preset time period; determining the service sending frequency of the target Internet of things based on the determined total number of services of the networking users in a preset time period and the determined number of the networking users of the target Internet of things, and then determining attribute information of the service sending frequency of the target Internet of things according to the determined service sending frequency and a type division rule corresponding to the obtained performance index of the service sending frequency;
for example: the target internet of things is Zhangzhou city public Security office internet of things, the total number of services sent by networking users under the internet of things in a preset time period is calculated to be 288086 times, the number of the networking users of the target internet of things is 1269 people, and the sending frequency in the preset time period is calculated to be 227 times/person; the type division rule corresponding to the service sending frequency performance index is as follows: dividing the service sending frequency into 3 grades (a1, a2, a3) for analysis, sending the service for 1 time per user each day, and the sending time is fixed, which is considered to be regular but less service sending (a 1); transmitting the traffic more than 1 time but less than 5 times per day per user, and the transmission time is irregular, which is considered as irregular and infrequent traffic transmission (a 2); sending the service more than 5 times per user and irregularly, and considering that the service is sent frequently and irregularly (a 3); therefore, in a preset time period, attribute information of service sending frequency of the Internet of things of Zhangzhou city public Security office is that service sending is frequent;
the method comprises the following steps of analyzing the number of signaling data set cell identifications and the number of networking users in a preset time period by analyzing the service use areas of the networking users under the networking of a target Internet of things collected from an SI-U interface, so that the dispersion condition of the service use areas of the target Internet of things in the preset time period can be reflected, wherein the designated service performance indexes comprise: service use area index; as shown in fig. 5, in step S103, determining, based on the signaling data set, a feature value of a specified service performance index of the target internet of things corresponding to the internet of things identifier includes:
step S103a, determining the cell identification contained in each signaling data in the signaling data set;
step S103b, determining the number of networking users of the target Internet of things corresponding to the Internet of things identification according to the user identification contained in the signaling data set;
step S103c, determining the service use area dispersion of the target Internet of things according to the number of the cell identifications and the number of the networking users in the signaling data set.
The signaling data is generated when each networking user under the target Internet of things has a service, and the signaling data comprises field information such as a user identifier IMSI, a Cell identifier Cell ID and the like; specifically, the number of networking users of the target Internet of things is determined according to a user identifier IMSI contained in a signaling data set through the signaling data set of the target Internet of things acquired within a preset time; determining the total number of Cell identifiers in the signaling data set according to the Cell identifier (Cell ID) contained in each signaling data in the determined signaling data set, namely determining the total number of different Cell identifiers (Cell IDs) of the networked users moving in a preset time period; determining the service use area dispersion of the target Internet of things based on the total number of the determined Cell identifications (Cell IDs) of the networking users moving in the preset time period and the determined number of the networking users of the target Internet of things, and then determining the attribute information of the service use area of the target Internet of things according to the determined service use area dispersion and a type division rule corresponding to the obtained service use area dispersion performance index;
for example: the target internet of things is Zhangzhou city public Security office internet of things, the total number of different Cell identifications (Cell IDs) of networking user activities under the internet of things in a preset time period is calculated to be 28808, the number of the networking users of the target internet of things is 1269, and the dispersion of service use areas in the preset time period is calculated to be 22.7; the type division rule corresponding to the service use region performance index is as follows: dividing the service activity area into 2 grades (b1, b2), wherein the cell where each user sends the service every day is fixed, and the service is considered to be of a fixed type (b 1); each user sends service in more than 2 cells per day, and the user is considered as an activity type (b 2); therefore, in a preset time period, users in the service use area of the Internet of things of the Zhangzhou city public Security office are in an active type.
The service transmission condition of the target Internet of things in a preset time period can be reflected by using a bearer protocol for each networking user service under the target Internet of things collected in an SI-U interface, and the specified service performance indexes comprise: a bearer protocol indicator; as shown in fig. 6, in step S103, determining, based on the signaling data set, a feature value of a specified service performance index of the target internet of things corresponding to the internet of things identifier includes:
step S103d, determining the bearing protocol information contained in each signaling data in the signaling data set;
step S103e, according to each piece of bearer protocol information, determining the type of the bearer protocol index related to the target Internet of things corresponding to the Internet of things identifier.
The signaling data is generated when each networking user under the target Internet of things has a service, and the signaling data comprises field information such as user identification IMSI, bearer protocol and the like; specifically, through a signaling data set of a target internet of things acquired within a preset time, determining bearer protocol information contained in each signaling data in the signaling data set, and according to the bearer protocol information contained in the determined signaling data set and a type division rule corresponding to an acquired service usage bearer protocol performance index, determining attribute information of a service bearer protocol of the target internet of things;
for example: the target Internet of things is a Zhangzhou city public bureau Internet of things, and in a signaling data set of the target Internet of things acquired within preset time, the contained signaling data bearer protocol comprises a UDP bearer protocol and a TCP bearer protocol; the type division rule corresponding to the service usage bearer protocol performance index is as follows: dividing the bearer protocols into 3 classes (c1, c2 and c3), wherein the user transmission service is only borne on UDP, and the service with high transmission efficiency is considered to be required (c 1); user transport traffic is carried only on TCP, and reliable traffic is considered to be required (c 2); the user transport traffic is carried over both UDP and TCP, considered as a hybrid transport (c 3); therefore, in a preset time period, the transmission mode of the internet of things in the Zhangzhou city public bureau is a hybrid transmission mode.
The service level condition of the target Internet of things in a preset time period can be reflected by analyzing the service distribution area of each networking user under the target Internet of things collected in the SI-U interface, and the specified service performance indexes comprise: a service distribution area index; the step S103 of determining, based on the signaling data set, a feature value of the designated service performance index of the target internet of things corresponding to the internet of things identifier includes:
step one, determining a user identification code contained in each signaling data in a signaling data set;
determining attribution information of each networking user in the target Internet of things corresponding to the Internet of things identification according to each user identification code;
and step three, determining the business distribution affiliated area of the networking users in the target Internet of things according to the attribution information of the networking users.
Specifically, for example: the target internet of things is a Zhangzhou city public bureau internet of things, and the attribution information of the user identification code in the signaling data set of the target internet of things acquired within preset time is Zhangzhou city; class corresponding to performance index of service distribution regionThe type division rule is as follows: all the user sources in the target internet of things are divided into three types (d)1、d2、d3) And analyzing, wherein all users in the Internet of things are in the same local city and are considered to be local city level business (d)1) (ii) a All users in the internet of things belong to the same province and different cities, and the users are considered as provincial services (d)2) (ii) a All users in the internet of things come from all over the country and are considered as national services (d)3) (ii) a Therefore, in a preset time period, the service distribution area of the internet of things of Zhangzhou city public security office is a local-level service (d)1)；
As shown in fig. 7, in the step S104, determining the service attribute information of the target internet of things according to the feature value of the designated service performance index includes:
step S1041, obtaining a type partition rule corresponding to the specified service performance index, where the type partition rule includes: the corresponding relation between the index characteristic information and the type of the index;
step S1042, according to the corresponding relation between the index characteristic information and the index belonged type, determining the index belonged type corresponding to the characteristic value of the specified service performance index;
and S1043, determining the type of the index as the service attribute information of the target Internet of things.
Specifically, in the index information divided in the type division rule, index information matched with the feature value of the specified service performance index is searched, for example: if the determined characteristic value of the download traffic index is 4Kb, index information of which 1Kb is less than X and less than 5Kb in the type division rule is index information matched with the characteristic value of the download traffic index; and determining the type of the index corresponding to the searched index information according to the corresponding relation.
Further, the specified service performance indicators include, but are not limited to: the method comprises the following steps that a service downloading flow index, a service uplink-downlink flow ratio index, a service sending frequency index, a service use area index, a bearer protocol index and a service distribution area index are included, wherein each index is a dimension for measuring the service quality of the Internet of things, so that a three-dimensional Internet of things quality evaluation system is formed, and each Internet of things can be matched with the most appropriate characteristic value in the system;
TABLE 1
Service features of different internet of things are gathered according to characteristic conditions that indexes of services of different internet of things fall into different ranges of contents, 6 dimensions are shown in table 1, each dimension can be divided into 2-3 types of characteristics according to the indexes, and therefore the characteristics of the internet of things at present can be C3 1C3 1C3 1C2 1C3 1C3 1And (4) combination.
For example, the internet of things of the Fujian power is a service with a download flow of 637Kb (large flow), an uplink flow is 2.8 (upload type) compared with a download flow, only one service sending behavior (regular and less service sending) is performed every day, all the services are sent in the same cell (fixed type of service user), all the services are borne on TCP (reliable transmission), all the users are cities and places of the whole province (general service of the whole province), so the characteristics of the service of the internet of things are clearly embodied, and the Internet of things of the Fujian power is a service with a large flow, an upload type, a regular and less service sending rule, a fixed property, reliable transmission and general use of the whole province.
The method for identifying the service attribute of the internet of things, provided by the embodiment of the invention, comprises the steps of acquiring signaling data of a whole network user transmitted through a target network interface; classifying the signaling data according to the Internet of things identifiers contained in the signaling data to obtain a signaling data set corresponding to each Internet of things identifier; determining a characteristic value of a designated service performance index of the target Internet of things corresponding to the Internet of things identification based on the signaling data set; and determining the service attribute information of the target Internet of things according to the characteristic value of the designated service performance index. The method comprises the steps of carrying out big data analysis on fields related to service performance indexes in a plurality of signaling data under the same Internet of things, determining characteristic values of all specified service performance indexes influencing the service quality of the Internet of things from multiple dimensions, accurately identifying service attribute information of a target Internet of things based on index types corresponding to the characteristic values, and improving the evaluation efficiency and the evaluation accuracy of the service quality of the Internet of things.
On the basis of the same technical concept, the service attribute identification method of the internet of things provided by the embodiment of the present invention further provides a service attribute identification device of the internet of things, fig. 8 is a schematic view of a module composition of the service attribute identification device of the internet of things provided by the embodiment of the present invention, the service attribute identification device of the internet of things is used for executing the service attribute identification method of the internet of things described in fig. 1 to fig. 7, and as shown in fig. 8, the service attribute identification device of the internet of things includes:
a signaling data obtaining module 801, configured to obtain signaling data of a full network user transmitted through a target network interface;
a signaling data classification module 802, configured to classify the signaling data according to the internet of things identifiers included in the signaling data, so as to obtain a signaling data set corresponding to each internet of things identifier;
a feature value determining module 803, configured to determine, based on the signaling data set, a feature value of a specified service performance index of the target internet of things corresponding to the internet of things identifier;
a service attribute information determining module 804, configured to determine the service attribute information of the target internet of things according to the feature value of the specified service performance index.
In the embodiment of the invention, the characteristic values of all specified service performance indexes influencing the service quality of the Internet of things are determined from multiple dimensions by performing big data analysis on fields related to the service performance indexes in a plurality of signaling data of the same Internet of things, so that the service attribute information of the target Internet of things is accurately identified based on the index types corresponding to the characteristic values, and the evaluation efficiency and the evaluation accuracy of the service quality of the Internet of things are improved.
Optionally, the specifying the service performance indicator includes: a service downloading flow index;
the characteristic value determining module 803 is specifically configured to:
determining service downloading flow contained in each signaling data in the signaling data set;
determining the number of networking users of the target Internet of things corresponding to the Internet of things identification according to the user identification contained in the signaling data set;
and determining the average service downloading flow of the target Internet of things based on the service downloading flows and the number of the networking users.
Optionally, the specifying the service performance indicator includes: service uplink and downlink flow rate ratio index;
the characteristic value determining module 803 is further specifically configured to:
determining uplink flow and downlink flow contained in each signaling data in the signaling data set;
respectively determining an uplink flow total value and a downlink flow total value of the signaling data set according to each uplink flow and each downlink flow;
and determining the service uplink and downlink flow ratio of the target Internet of things corresponding to the Internet of things identification according to the uplink flow total value and the downlink flow total value.
Optionally, the specifying the service performance indicator includes: service sending frequency index;
the characteristic value determining module 803 is further specifically configured to:
determining the amount of signaling data contained in the set of signaling data;
determining the number of networking users of the target Internet of things corresponding to the Internet of things identification according to the user identification contained in the signaling data set;
and determining the service sending frequency of the target Internet of things according to the quantity of the signaling data and the quantity of the networking users.
Optionally, the specifying the service performance indicator includes: service use area index;
the characteristic value determining module 803 is further specifically configured to:
determining a cell identifier contained in each signaling data in the signaling data set;
determining the number of networking users of the target Internet of things corresponding to the Internet of things identification according to the user identification contained in the signaling data set;
and determining the service use area dispersion of the target Internet of things according to the number of the cell identifications in the signaling data set and the number of the networking users.
Optionally, the specifying the service performance indicator includes: a bearer protocol indicator;
the characteristic value determining module 803 is further specifically configured to:
determining the bearing protocol information contained in each signaling data in the signaling data set;
and determining the type of the bearer protocol index related to the target Internet of things corresponding to the Internet of things identification according to the bearer protocol information.
Optionally, the specifying the service performance indicator includes: a service distribution area index;
the characteristic value determining module 803 is further specifically configured to:
determining a user identification code contained in each signaling data in the signaling data set;
according to each user identification code, determining attribution information of each networking user in the target Internet of things corresponding to the Internet of things identification;
and determining the region to which the service distribution of the networking users in the target Internet of things belongs according to the attribution information of each networking user.
Optionally, the service attribute information determining module 804 is specifically configured to:
obtaining a type division rule corresponding to the specified service performance index, wherein the type division rule comprises: the corresponding relation between the index characteristic information and the type of the index;
determining the type of the index corresponding to the characteristic value of the specified service performance index according to the corresponding relation between the index characteristic information and the type of the index;
and determining the type of the index as the service attribute information of the target Internet of things.
The service attribute identification device of the Internet of things in the embodiment of the invention acquires signaling data of a whole network user transmitted through a target network interface; classifying the signaling data according to the Internet of things identifiers contained in the signaling data to obtain a signaling data set corresponding to each Internet of things identifier; determining a characteristic value of a designated service performance index of the target Internet of things corresponding to the Internet of things identification based on the signaling data set; and determining the service attribute information of the target Internet of things according to the characteristic value of the designated service performance index. The method comprises the steps of carrying out big data analysis on fields related to service performance indexes in a plurality of signaling data under the same Internet of things, determining characteristic values of all specified service performance indexes influencing the service quality of the Internet of things from multiple dimensions, accurately identifying service attribute information of a target Internet of things based on index types corresponding to the characteristic values, and improving the evaluation efficiency and the evaluation accuracy of the service quality of the Internet of things.
The service attribute identification device of the internet of things provided by the embodiment of the invention can realize each process in the embodiment corresponding to the service attribute identification method of the internet of things, and is not repeated here to avoid repetition.
It should be noted that the device for identifying service attributes of the internet of things provided by the embodiment of the present invention and the method for identifying service attributes of the internet of things provided by the embodiment of the present invention are based on the same inventive concept, so specific implementation of the embodiment may refer to implementation of the method for identifying service attributes of the internet of things, and repeated details are not repeated.
Based on the same technical concept, the embodiment of the present invention further provides a computer device for performing the method for locating network quality abnormality, where fig. 9 is a schematic structural diagram of a computer device for implementing the embodiments of the present invention, as shown in fig. 9. Computer devices may vary widely in configuration or performance and may include one or more processors 901 and memory 902, where the memory 902 may have one or more stored applications or data stored therein. Memory 902 may be, among other things, transient storage or persistent storage. The application program stored in memory 902 may include one or more modules (not shown), each of which may include a series of computer-executable instructions for a computing device. Still further, the processor 901 may be configured to communicate with the memory 902 to execute a series of computer-executable instructions in the memory 902 on a computer device. The computer apparatus may also include one or more power supplies 903, one or more wired or wireless network interfaces 904, one or more input-output interfaces 905, one or more keyboards 906.
In this embodiment, the computer device includes a processor, a communication interface, a memory, and a communication bus; the processor, the communication interface and the memory complete mutual communication through a bus; a memory for storing a computer program; a processor for executing the program stored in the memory, implementing the following method steps:
acquiring signaling data of a whole network user transmitted through a target network interface;
classifying the signaling data according to the Internet of things identifiers contained in the signaling data to obtain a signaling data set corresponding to each Internet of things identifier;
determining a characteristic value of a designated service performance index of the target Internet of things corresponding to the Internet of things identifier based on the signaling data set;
and determining the service attribute information of the target Internet of things according to the characteristic value of the designated service performance index.
An embodiment of the present application further provides a computer-readable storage medium, in which a computer program is stored, and when the computer program is executed by a processor, the following method steps are implemented:
acquiring signaling data of a whole network user transmitted through a target network interface;
classifying the signaling data according to the Internet of things identifiers contained in the signaling data to obtain a signaling data set corresponding to each Internet of things identifier;
determining a characteristic value of a designated service performance index of the target Internet of things corresponding to the Internet of things identifier based on the signaling data set;
and determining the service attribute information of the target Internet of things according to the characteristic value of the designated service performance index.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.
Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in the process, method, article, or apparatus that comprises the element.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The above are merely examples of the present application and are not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.
Claims (10)
1. An Internet of things service attribute identification method is characterized by comprising the following steps:
acquiring signaling data of a whole network user transmitted through a target network interface;
classifying the signaling data according to the Internet of things identifiers contained in the signaling data to obtain a signaling data set corresponding to each Internet of things identifier;
determining a characteristic value of a designated service performance index of the target Internet of things corresponding to the Internet of things identifier based on the signaling data set;
and determining the service attribute information of the target Internet of things according to the characteristic value of the designated service performance index.
2. The method of claim 1, wherein the specifying the service performance indicator comprises: a service downloading flow index;
the determining, based on the signaling data set, a feature value of a specified service performance index of the target internet of things corresponding to the internet of things identifier includes:
determining service downloading flow contained in each signaling data in the signaling data set;
determining the number of networking users of the target Internet of things corresponding to the Internet of things identification according to the user identification contained in the signaling data set;
and determining the average service downloading flow of the target Internet of things based on the service downloading flows and the number of the networking users.
3. The method of claim 1, wherein the specifying the service performance indicator comprises: service uplink and downlink flow rate ratio index;
the determining, based on the signaling data set, a feature value of a specified service performance index of the target internet of things corresponding to the internet of things identifier includes:
determining uplink flow and downlink flow contained in each signaling data in the signaling data set;
respectively determining an uplink flow total value and a downlink flow total value of the signaling data set according to each uplink flow and each downlink flow;
and determining the service uplink and downlink flow ratio of the target Internet of things corresponding to the Internet of things identification according to the uplink flow total value and the downlink flow total value.
4. The method of claim 1, wherein the specifying the service performance indicator comprises: service sending frequency index;
the determining, based on the signaling data set, a feature value of a specified service performance index of the target internet of things corresponding to the internet of things identifier includes:
determining the amount of signaling data contained in the set of signaling data;
determining the number of networking users of the target Internet of things corresponding to the Internet of things identification according to the user identification contained in the signaling data set;
and determining the service sending frequency of the target Internet of things according to the quantity of the signaling data and the quantity of the networking users.
5. The method of claim 1, wherein the specifying the service performance indicator comprises: service use area index;
the determining, based on the signaling data set, a feature value of a specified service performance index of the target internet of things corresponding to the internet of things identifier includes:
determining a cell identifier contained in each signaling data in the signaling data set;
determining the number of networking users of the target Internet of things corresponding to the Internet of things identification according to the user identification contained in the signaling data set;
and determining the service use area dispersion of the target Internet of things according to the number of the cell identifications in the signaling data set and the number of the networking users.
6. The method of claim 1, wherein the specifying the service performance indicator comprises: a bearer protocol indicator;
the determining, based on the signaling data set, a feature value of a specified service performance index of the target internet of things corresponding to the internet of things identifier includes:
determining the bearing protocol information contained in each signaling data in the signaling data set;
and determining the type of the bearer protocol index related to the target Internet of things corresponding to the Internet of things identification according to the bearer protocol information.
7. The method according to any one of claims 1 to 6, wherein the determining the service attribute information of the target internet of things according to the eigenvalue of the specified service performance index includes:
obtaining a type division rule corresponding to the specified service performance index, wherein the type division rule comprises: the corresponding relation between the index characteristic information and the type of the index;
determining the type of the index corresponding to the characteristic value of the specified service performance index according to the corresponding relation between the index characteristic information and the type of the index;
and determining the type of the index as the service attribute information of the target Internet of things.
8. An internet of things service attribute recognition device, comprising:
the signaling data acquisition module is used for acquiring the signaling data of the whole network user transmitted through the target network interface;
the signaling data classification module is used for classifying the signaling data according to the Internet of things identifiers contained in the signaling data to obtain a signaling data set corresponding to each Internet of things identifier;
the characteristic value determining module is used for determining the characteristic value of the designated service performance index of the target internet of things corresponding to the internet of things identifier based on the signaling data set;
and the service attribute information determining module is used for determining the service attribute information of the target Internet of things according to the characteristic value of the specified service performance index.
9. A computer device comprising a processor, a communication interface, a memory, and a communication bus; the processor, the communication interface and the memory complete mutual communication through a bus; the memory is used for storing a computer program; the processor is configured to execute the program stored in the memory, and implement the steps of the internet-of-things service attribute identification method according to any one of claims 1 to 7.
10. A computer-readable storage medium, characterized in that the computer-readable storage medium has stored thereon a computer program which, when being executed by a processor, implements the internet of things service property identification method steps of any one of claims 1 to 7.
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