CN113573325B - Wireless network analysis method, system and computer readable storage medium - Google Patents
Wireless network analysis method, system and computer readable storage medium Download PDFInfo
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Abstract
Embodiments of the present invention provide a wireless network analysis method, system, and computer-readable storage medium, the method comprising: the data acquisition management module performs acquisition task configuration and issues the acquisition task configuration to the OneNET platform; the acquisition task configuration comprises an acquisition instruction and an acquisition frequency; the OneNET platform issues the acquisition task to a device cache command list; acquiring an acquisition task when registering or updating the equipment terminal; the equipment terminal reads network parameters according to the acquisition task and reports the network parameters to the data acquisition management module through the OneNET platform; the data acquisition management module processes and stores the data; the data acquisition management module transmits the data to the NB network analysis platform or other application platforms in an API interface mode. The system scene has stronger applicability, the test result is the true evaluation of the existing network equipment, the accuracy is higher, and the actual service perception of the existing network can be reflected better.
Description
Technical Field
The invention relates to the technical field of the internet of things, in particular to a wireless network analysis method, a wireless network analysis system and a computer readable storage medium.
Background
When mobile network operators develop NB-IoT network construction, there are multiple difficulties in wanting to optimize the analysis of NB-IoT networks: because NB-IoT network technology specifications are pushed out for a short time, operators have not yet fully established an evaluation system for the technology specifications; because the NB-IoT application scene is a low-rate and wide-coverage internet of things scene, the traditional 2/3/4G network analysis method is poor in applicability.
Currently, the main methods of NB-IoT network analysis include the following:
(1) Road test: and (3) carrying out network test by using a special test tool, carrying out network test on a main traffic trunk line of a network and a key place by using the special test tool and a vehicle, and analyzing a test result. The technical scheme is the most common network test analysis method in the wireless communication industry, special test tools, test vehicles and test staff are needed, the resource consumption is huge, and the test results can only feed back sampling results of key traffic trunks and key places.
(2) NB-IoT coverage prediction approach, this approach has the following drawbacks: 1. in the field of wireless communication, the indexes for evaluating network coverage include parameters such as level strength (RSRP), RSRQ, SINR, RSSI, and the like, and only the level strength can be evaluated, so that the comprehensive evaluation of the network coverage indexes has limitations. 2. The scene of the service generated by the terminal covered by the GSM900 has larger difference from the scene of the service generated by the NB-IoT, the coverage of the NB-IoT is estimated by using the coverage of the GSM900, the accuracy is poor, for example, the service of the GSM900 mainly occurs in a hot spot area in a building at the same longitude and latitude, but the NB-IoT service occurs in a first layer of a basement and a second layer of the basement, and the obtained evaluation results obviously have huge differences.
(3) The method for evaluating the coverage and the structure of the NB-IoT network by using big data, 1, the scene of massive MR data of LTE is greatly different from the working scene of NB-IoT, the MR of NB-IoT is estimated by using LTE MR, the accuracy is obviously insufficient, and the hot spot areas of the two are different, so that an LTE hot spot area does not have NB-IoT equipment, the evaluation value is reduced, and the NB-IoT hot spot area does not have service of LTE, so that the network cannot be evaluated; 2. with the NB-IoT cell and FDD LTE cell common mode conditions, this is clearly not applicable in the current network, since the number of LTE sites is far greater than the number of NB-IoT sites, and in most MR sampling points the home LTE base station, which is not a co-sited NB-IoT base station, cannot evaluate this part of the sampling points.
Disclosure of Invention
The technical problem to be solved by the invention is to provide a wireless network analysis method, a wireless network analysis system and a computer readable storage medium, so that the scene applicability is stronger, the test result is the actual evaluation of the existing network equipment, the accuracy is higher, and the actual service perception of the existing network can be reflected more.
To solve the above technical problem, in a first aspect, an embodiment of the present invention provides a wireless network analysis method applied to a narrowband internet of things NB-IoT wireless network system, the method including:
the data acquisition management module performs acquisition task configuration and issues the acquisition task configuration to the OneNET platform; the acquisition task configuration comprises an acquisition instruction and an acquisition frequency;
the OneNET platform issues the acquisition task to a device cache command list;
acquiring an acquisition task when registering or updating the equipment terminal;
the equipment terminal reads relevant network parameters according to the acquisition task and reports the parameters to the data acquisition management module through the OneNET platform;
the data acquisition management module processes and stores the data;
the data acquisition management module communicates the data to the NB-IoT network analysis platform or other application platform in an application program interface API manner.
Optionally, the wireless network analysis method further includes the following steps:
the OneNET data acquisition module also sets the following reporting parameters of the SDK at the equipment side:
a. when the platform side does not issue parameter reporting settings, the SDK of the equipment side defaults to read all data of relevant network parameters of the equipment side and report the data in the process of reporting the data;
b. if the platform side issues a write command, the device side SDK correspondingly sets network data for subsequent re-reporting according to the bit corresponding to the command field.
Optionally, the wireless network analysis method further includes the following steps:
the OnneT platform also extracts basic service information reported by the equipment terminal according to the service configuration of the equipment terminal;
the OnneT platform creates a ticket according to the extracted network parameter information and the basic service information and sends the ticket to the data processing module, wherein the ticket comprises a service package type, package service condition and other card opening information which are opened by the Internet of things card;
the data processing module integrates business behavior data and call ticket data of the terminal ， Classifying the services according to the traffic and reporting frequency;
the AI modeling module performs AI modeling on the terminal of the Internet of things according to the reporting behavior of the terminal and the information of the card of the Internet of things, performs verification by combining one or more of product information, card opening information and user investigation information of the platform of the Internet of things, outputs an industry model, and performs industry division on the terminal through the model;
the industry comprehensive analysis module performs statistical analysis according to different industries, and the statistical analysis content comprises industry network quality and business requirements of the industries.
Optionally, performing AI modeling on the terminal of the internet of things includes the following steps:
according to a product function list in the object model, initially determining the industry of the terminal;
AI analysis is performed according to the flow model, identifying the closest industry.
Optionally, the AI modeling for the terminal of the internet of things further includes the following steps:
identifying industries by analyzing network coverage quality; or/and inquiring the card opening information of the Internet of things card corresponding to the terminal, and grabbing the industry characteristic information.
Optionally, the service is classified according to the flow and the reporting frequency, specifically: the service is divided into four types, namely a small flow long period, a large flow long period, a small flow short period and a large flow short period, and the classification algorithm is as follows:
small flow long period: the single month flow is less than or equal to 1Mb, and the single month reporting times are more than 31 times;
large flow rate long period: the flow rate of a month is more than 1Mb, and the reporting times of the month are more than 31 times;
small flow short period: the single month flow is less than or equal to 1Mb, and the single month reporting frequency is less than or equal to 31 times;
high flow short period: the single month flow is larger than 1Mb, and the single month reporting frequency is smaller than or equal to 31 times.
Optionally, the OneNET platform issues the acquisition task to the device cache command list as follows:
if the device has a cache command which is not executed, updating the latest cache command; if the device does not have an unexecuted cache command, the cache command is added.
In a second aspect, embodiments of the present invention also provide a wireless network analysis system including an OneNET data acquisition module and an NB-IoT network analysis platform;
the OneNET data acquisition module comprises a main platform, a target platform and an application platform;
the main platform package:
the main database module is in charge of storing and managing equipment group management information, task management information, statistical information and network parameter information;
the acquisition group management/task management module is responsible for carrying out batch management on equipment and configuring tasks, wherein the configuration comprises one or more of acquisition network parameter items, acquisition frequency, trigger time, task equipment control, parameter acquisition instructions and acquisition frequency;
the command distribution service module is responsible for distributing the task to the target platform according to the task configuration;
the data receiving service module is responsible for synchronizing the data of the target platform and storing the data of the target platform into the main database module;
the API interface module is responsible for transmitting the network parameter data stored in the main database module to the upper application platform in an interface mode;
the statistical service module is responsible for carrying out statistical analysis on the task collecting process;
the target platform comprises:
the API cache command interface is in charge of storing offline commands and replacing or deleting the offline commands;
the target database module is responsible for the storage and distribution of the acquisition task cache command, the management of equipment access information and the management of equipment report data;
the acquisition service module is responsible for issuing an offline command after the terminal equipment is online; when the terminal equipment is registered or updated, the acquisition instruction is issued concomitantly, and the terminal equipment does not need to be independently awakened;
the NB-IoT access machine module is responsible for the access management of the terminal equipment and issues an offline command to the terminal equipment;
the terminal equipment is used for collecting data according to the instruction of the command after receiving the offline command and storing the collected data to the target platform;
the NB-IoT network analysis platform comprises an NB-IoT network analysis module and a data acquisition management module;
the application platform is responsible for receiving network parameter data stored in the main database in a manner of API interface.
In a third aspect, embodiments of the present invention provide a computer readable storage medium comprising instructions which, when executed on a computer, cause the computer to perform the steps of implementing the method according to the first aspect.
The scheme of the invention at least comprises the following beneficial effects:
according to the scheme, the Internet of things terminal based on the existing network is tested, the scene applicability is higher, the test result is the real evaluation of the existing network equipment, the accuracy is higher, the service experience of the terminal equipment can be fed back, and the actual service perception of the existing network can be reflected. The existing network equipment is used for testing, the coverage of the testing range is better, and as long as the area has service, the testing equipment is available, and network quality evaluation analysis can be carried out on the Internet of things equipment of the whole network; the method can also combine industry data to conduct industry hierarchical classification on NB-IoT network coverage, and can conduct industry coverage analysis aiming at different business scenes so as to achieve the purpose of balanced development of industry coverage.
Drawings
FIG. 1 is a flow chart of a wireless network analysis method of an embodiment of the present invention;
FIG. 2 is a flow chart of data acquisition analysis of a wireless network analysis method according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of an industry comprehensive analysis flow chart of a wireless network analysis method according to an embodiment of the invention;
fig. 4 is a schematic architecture diagram of a wireless network analysis system according to an embodiment of the present invention.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
As shown in fig. 1 to 3, an embodiment of the present invention proposes a wireless network analysis method, based on an NB-IoT wireless network in which network parameters are reported by an SDK of an internet of things device, the method comprising:
step 101, a data acquisition management module performs acquisition task configuration and issues the acquisition task configuration to an OneNET platform; the acquisition task configuration comprises an acquisition instruction and an acquisition frequency;
step 102, the OneNET platform issues an acquisition task to a device cache command list;
step 103, acquiring an acquisition task when the equipment terminal is registered or updated;
step 104, the equipment terminal reads relevant network parameters according to the acquisition task and reports the relevant network parameters to the data acquisition management module through the OneNET platform;
step 105, the data acquisition management module processes and stores the data;
step 106, the data acquisition management module transmits the data to the NB-IoT network analysis platform or other application platforms in an API interface manner.
In the embodiment of the invention, the existing network internet of things equipment is adopted for testing, and SDK (Software Development Kit ) is utilized for reporting network parameters, so that measurement and reporting of MR-like (measurement report) are realized. And an SDK integrated network parameter acquisition interface is adopted, a message interaction mode of the OneNET platform is adopted, a terminal does not need to add a new physical interface, additional development is not needed, and the network parameter reporting function can be supported only by accessing the OneNET platform. The parameter acquisition instructions and the acquisition frequency are flexibly configured, and can be set according to different Internet of things scenes as required, so that the flow consumption and the power consumption are saved. The method comprises the steps that when a device terminal registers or updates, a cache command is obtained, and the cache command comprises detailed configuration of the command; in this case, when the device terminal registers or updates, the acquisition instruction is issued concomitantly, the device does not need to wake up alone, and the extra power consumption is hardly perceived.
Optionally, the wireless network analysis method further comprises the following steps:
the OneNET data acquisition module also presets the following reporting parameters of the SDK at the equipment side:
a. when the platform side does not issue parameter reporting settings, the SDK of the equipment side defaults to read all data of relevant network parameters of the equipment side (module side) and report the data in the process of reporting the data;
b. if the platform side issues a write command, the device side SDK correspondingly sets network data for subsequent re-reporting according to the bit corresponding to the command field.
As a preferable embodiment, as shown in fig. 3, the wireless network analysis method further includes the steps of:
the OnneT platform also extracts basic service information reported by the equipment terminal according to the service configuration of the equipment terminal;
the OnneT platform creates a ticket according to the extracted network parameter information and the basic service information and sends the ticket to the data processing module, wherein the ticket comprises a service package type, package service condition and other card opening information which are opened by the Internet of things card;
the data processing module integrates business behavior data and call ticket data of the terminal ， Classifying the services according to the traffic and reporting frequency;
the AI modeling module performs AI modeling on the terminal of the Internet of things according to the reporting behavior of the terminal and the information of the card of the Internet of things, performs verification by combining one or more of product information, card opening information and user investigation information of the platform of the Internet of things, outputs an industry model, and performs industry division on the terminal through the model;
the industry comprehensive analysis module performs statistical analysis according to different industries, and the statistical analysis content comprises industry network quality and business requirements of the industries.
Further, the AI modeling for the terminal of the Internet of things comprises the following steps:
according to a product function list in the object model, initially determining the industry of the terminal;
AI analysis is performed according to the flow model, identifying the closest industry.
The terminal equipment can report basic service information (product functions: such as temperature, position and the like) according to service configuration; because each Internet of things industry has own characteristic data, for example, a sharing bicycle can comprise a position function, and an air monitoring terminal can comprise product functions such as PM2.5, temperature, humidity and the like; the functions among industries have overlapping parts, and only the range is needed to be identified; for example, intelligent street lamps can be turned on in the dark and turned off after being lighted every day, and the industry personality data can provide an important supporting means for industry identification.
Further, the AI modeling for the terminal of the internet of things further includes the following steps:
identifying industries by analyzing network coverage quality; or/and inquiring the card opening information of the Internet of things card corresponding to the terminal, and grabbing the industry characteristic information.
The industries are identified by analyzing the network coverage quality, and each industry has individual characteristics due to different environments. The scene where the terminal is located can be analyzed by various network index features such as weak coverage, strong coverage, single cell, multi-cell, low interference, high interference and the like. For example, the scene where the intelligent street lamp is located is a road, the coverage intensity and the quality are good, the intelligent street lamp can accept multi-cell signals, and the interference is strong. The water meter is in a deep coverage area, the signal strength is weak, only a weak signal of one cell can be received, and no other signal interference exists.
In conclusion, the terminal can be accurately distinguished in industry. It should be noted that, AI modeling recognition also performs industry distinction according to various feature rules of industry, so that data verification needs to be performed on an industry model.
There are several ways of data verification: the method comprises the steps that a large amount of verification data, user feedback information of the OneNET platform and manual simulation data reporting can be provided by the known Internet of things project;
the industry comprehensive analysis module can carry out statistical analysis according to different industries and output multidimensional analysis of market development conditions, industry network quality, industry value and the like of the industries.
The OnneT platform creates a ticket according to the extracted network parameter information and the basic service information, wherein the ticket comprises the telephone traffic details of the Internet of things card, including the service package type opened by the card, package service condition and other card opening information; part of the package name already contains industry uses such as package name: the 10-element package of the Internet of vehicles shares the 15-element package of the bicycle.
Through accumulated industry information data, the business requirements of the industry can be statistically analyzed, and a guiding effect is provided for package customization; meanwhile, according to the network index analysis of the industry, the provincial company is helped to find out the network short board, so that an impetus can be found for the development of the key industry, and a guiding direction is provided for the popularization of the industry; the business and network index analysis is carried out according to the industry, which is superior to the traditional analysis method, is more suitable for the multi-scene characteristics of the business of the Internet of things, and is convenient for users to better know and optimize the network.
The embodiment combines various data such as product characteristics, business analysis, flow models, internet of things card information, network parameter information and the like to model, and extracts a business model and a business model. The coverage scenes of different industries are different, the coverage requirements on the network are also different, in the embodiment, the network coverage of the NB-IoT can be classified in an industry layering manner, and the industry coverage analysis can be performed aiming at different service scenes, so that the aim of balanced development of the industry coverage is fulfilled.
As an preferable implementation manner, the data processing module classifies the service according to the traffic and the reporting frequency, specifically: the service is divided into four types, namely a small flow long period, a large flow long period, a small flow short period and a large flow short period, and the classification algorithm is as follows:
small flow long period: the single month flow is less than or equal to 1Mb, and the single month reporting times are more than 31 times;
large flow rate long period: the flow rate of a month is more than 1Mb, and the reporting times of the month are more than 31 times;
small flow short period: the single month flow is less than or equal to 1Mb, and the single month reporting frequency is less than or equal to 31 times;
high flow short period: the single month flow is larger than 1Mb, and the single month reporting frequency is smaller than or equal to 31 times.
In this embodiment, the data processing module integrates the service behavior data and the call ticket data of the terminal, and performs association and aggregation processing to classify the service according to the traffic and the reporting frequency, and specifically includes: a small flow long period, a large flow long period, a small flow short period, and a large flow long period.
Optionally, in step 102, the OneNET platform issues the acquisition task to the device cache command list as follows:
if the device has a cache command which is not executed, updating the latest cache command; if the device does not have an unexecuted cache command, the cache command is added.
According to the NB-IoT wireless network analysis method based on the network parameters reported by the SDK of the internet of things equipment, the SDK accessed by the NB equipment of the OnneT platform can report network parameter data of the equipment side when the platform is connected, such as RSRP, SINR, RSRQ, RSSI, CELLID, and related operations and displays such as network quality analysis are carried out by transmitting the parameters to an upper application through the platform.
The whole scheme adopts an SDK integrated network parameter acquisition interface, the message interaction adopts an interaction mode of an OneNET platform, a terminal does not need to add a physical interface, additional development is not needed, and the network parameter reporting function can be supported only by accessing the OneNET platform; after the platform receives the network parameters, a network analysis method for establishing an industry model and carrying out layering optimization according to the industry is established, the coverage scene is consistent with the NB-IoT coverage scene, the test result is the real evaluation of the existing network equipment, the accuracy is higher, and the service experience of the terminal equipment can be fed back; the existing network equipment is used for testing, the coverage of the testing range is better, and the testing equipment is available as long as the area has service.
The specific implementation of the above method is described with reference to fig. 2 and 3 above: an NB-IoT wireless network analysis method based on network parameters reported by an SDK of an Internet of things device comprises the following specific steps of;
s1, a data acquisition management program performs acquisition task configuration, including acquisition instructions and acquisition frequencies, and issues the acquisition instructions and the acquisition frequencies to an OneNET platform;
s2, the OneNET platform issues a cache command to a device cache command list;
if the device has a cache command which is not executed, updating the latest cache command;
if the device does not have the unexecuted cache command, adding the cache command;
s3, when the equipment terminal registers or updates, a cache command is acquired, wherein the cache command comprises detailed configuration of the command;
s4, the terminal reads related network parameters and reports the parameters to a data acquisition management program;
s5, the data acquisition management program processes and stores the data;
and S6, the data acquisition management program transmits the data to the NB network analysis platform or other application platforms in an API interface mode.
Further, the OneNET data acquisition module may set reporting parameters of the SDK on the device side according to actual situations:
a. when the platform side does not issue parameter reporting setting, the SDK defaults to read all the data of the module side in the service flow and reports the data;
b. if the platform side issues a write command, the SDK can set the network data for subsequent re-reporting according to the bit corresponding to the command field
S7, the terminal equipment reports basic service information (product functions, such as temperature, position and the like) according to service configuration;
s8, the OneNET platform creates a ticket according to the extracted network parameter information and the basic service information, wherein the ticket comprises the telephone traffic details of the Internet of things card, including the service package type opened by the card, package use condition and other card opening information; part of the package name already contains industry uses such as package name: 10-element package of the Internet of vehicles and 15-element package of the shared bicycle;
s9, the data processing module integrates the business behavior data and the call ticket data of the terminal, and carries out association aggregation processing to classify the business according to the flow and the reporting frequency, and the business is specifically divided into: a small flow long period, a large flow long period, a small flow short period, and a large flow short period;
the classification algorithm is as follows:
small flow long period: the single month flow is less than or equal to 1Mb, and the single month reporting times are more than 31 times;
large flow rate long period: the flow rate of a month is more than 1Mb, and the reporting times of the month are more than 31 times;
small flow short period: the single month flow is less than or equal to 1Mb, and the single month reporting frequency is less than or equal to 31 times;
high flow short period: the flow rate of a month is more than 1Mb, and the reporting times of the month are less than or equal to 31 times;
s10, an AI modeling module carries out AI modeling on the terminal of the Internet of things according to the reporting behavior of the terminal and the information of the card of the Internet of things, and carries out verification by combining the product information, the card opening information, the user investigation and the like of the platform of the Internet of things, and outputs models of industries and businesses; industry division is carried out on the terminals through the model;
further, the AI modeling includes the steps of:
according to the product function list in the object model, the industry of the terminal can be initially determined. Because each Internet of things industry has own characteristic data, for example, a sharing bicycle can comprise a position function, and an air monitoring terminal can comprise PM2.5, temperature, humidity and other product functions; the functions among industries have overlapping parts, and only the range needs to be identified;
AI analysis is performed according to the flow model, identifying the closest industry. For example, intelligent street lamps are turned on in the dark and turned off after the street lamps are lighted every day. These industry personality data may provide an important support means for industry identification.
Further, industries are identified by analyzing network coverage quality, and each industry has a characteristic due to different environments.
The scene of the terminal can be analyzed according to various network index characteristics such as weak coverage, strong coverage, single cell, multiple cells, low interference, high interference and the like. For example, the scene where the intelligent street lamp is located is a road, the coverage intensity and the quality are good, the intelligent street lamp can accept multi-cell signals, and the interference is strong. The water meter is in a deep coverage area, the signal strength is weak, only a weak signal of one cell can be received, and no other signal interference exists.
Further, inquiring card opening information of the Internet of things card corresponding to the terminal, such as package flow, package name and the like, and capturing industry characteristic information.
In conclusion, the terminal can be accurately distinguished in industry. It should be noted that, AI modeling recognition also performs industry distinction according to various feature rules of industry, so that data verification needs to be performed on an industry model.
Further, there are several ways of data verification: the method comprises the steps that a large amount of verification data, user feedback information of the OneNET platform and manual simulation data reporting can be provided by the known Internet of things project;
s11, the industry comprehensive analysis module can carry out statistical analysis according to different industries and output multi-dimensional analysis of market development conditions of the industries, industry network quality, industry value and the like.
Through accumulated industry information data, the business requirements of the industry can be statistically analyzed, and a guiding effect is provided for package customization; meanwhile, according to the network index analysis of the industry, the provincial company is helped to find out the network short board, so that an impetus can be found for the development of the key industry, and a guiding direction is provided for the popularization of the industry; the business and network index analysis is carried out according to the industry, which is superior to the traditional analysis method, is more suitable for the multi-scene characteristics of the business of the Internet of things, and is convenient for users to better know and optimize the network.
The embodiment of the invention adopts the existing network internet of things equipment to test, and utilizes the SDK to report network parameters, thereby realizing measurement and report of the MR-like report;
the embodiment of the invention adopts the SDK integrated network parameter acquisition interface, the message interaction adopts the interaction mode of the OneNET platform, the terminal does not need to add a new physical interface, does not need to be additionally developed, and can support the network parameter reporting function only by accessing the OneNET platform;
the parameter acquisition instructions and the acquisition frequency are flexibly configured, and can be set according to different Internet of things scenes as required, so that the flow consumption and the power consumption are saved;
when the equipment registers and updates the platform, the acquisition instruction is issued along with the equipment, the equipment does not need to be independently awakened, and the extra power consumption is hardly perceived;
the embodiment of the invention combines various data such as product characteristics, business analysis, flow models, internet of things card information, network parameter information and the like to model, and extracts a business model and a business model.
The coverage scenes of different industries are different, the coverage requirements on the network are also different, the network coverage of the NB-IoT is classified in the scheme in an industry layering manner, and the industry coverage analysis can be performed aiming at different service scenes, so that the aim of balanced development of the industry coverage is fulfilled.
The embodiment of the invention can combine industry data to conduct industry hierarchical classification on NB-IoT network coverage, and can conduct industry coverage analysis aiming at different business scenes, so as to achieve the purpose of balanced development of industry coverage.
The embodiment of the invention can combine industry data to conduct industry hierarchical classification on NB-IoT network coverage, and can conduct industry coverage analysis aiming at different business scenes, so as to achieve the purpose of balanced development of industry coverage.
The embodiment of the invention can combine industry data to conduct industry hierarchical classification on NB-IoT network coverage, and can conduct industry coverage analysis aiming at different business scenes, so as to achieve the purpose of balanced development of industry coverage.
As shown in fig. 4, the wireless network analysis system in the embodiment of the invention mainly includes an OneNET data acquisition module and an NB network analysis platform.
The OneNET data acquisition module comprises a main platform, a provincial platform and a Sass application platform. The mondab of the main platform is a main mondab (database) module, and the mondab of the target platform is a province mondab module. Mongodb is a database based on distributed file storage. The target company may be a province company.
The main platform comprises a main Mongodb module, an acquisition group target group management/task management module, a command distribution service module, a data receiving service module, an API interface module and a statistics service module.
The main Mongodb module is mainly responsible for storing and managing equipment group management information, task management information, statistical information and network parameter information. The main Mongodb module realizes the basic functions of the database.
The acquisition group management/task management module is mainly responsible for carrying out batch management on equipment and configuring tasks, wherein the configuration comprises one or more of acquisition network parameter items, acquisition frequency, trigger time, task equipment control, parameter acquisition instructions and acquisition frequency. The acquisition group management module is mainly responsible for carrying out batch management on equipment, and the task management module is mainly responsible for configuring tasks, wherein the configuration comprises acquisition of network parameter items, acquisition frequency, trigger time, task equipment control and the like; meanwhile, the parameter acquisition instruction and the acquisition frequency can be configured, and can be set according to the scene as required, so that resource waste is avoided.
The command distribution service module is mainly responsible for distributing tasks to the target platform according to task configuration.
The data receiving service module is mainly responsible for synchronizing the data of the provincial platform, and the synchronization process adopts grpc encryption to store the data of the target company into the main mong odb module.
The API interface module is mainly responsible for transmitting the network parameter data stored in the main mongab module to the upper application platform in an interface mode.
The statistical service module is mainly responsible for carrying out statistical analysis on the acquisition task process. In this embodiment, the statistics service module is mainly responsible for performing statistics analysis on the collection task process from dimensions of products, modules, target platforms, and the like.
The target platform comprises an API cache command interface, a target Mongodb module, an acquisition service module, an NB access machine module and terminal equipment.
The API cache command interface is mainly responsible for storing offline commands and replacing or deleting offline commands.
The target Mongodb module is mainly responsible for storing and distributing the acquisition task cache command, managing the access information of the equipment and managing the reported data of the equipment.
The acquisition service module is mainly responsible for issuing off-line commands after the terminal equipment is on line; when the terminal equipment is registered or updated (updated), the acquisition instruction is issued along with the acquisition instruction, the terminal equipment does not need to be independently awakened, and the additional power consumption is hardly perceived.
The NB access machine module is mainly responsible for access management of the terminal equipment and transmits an offline command to the terminal equipment.
And the terminal equipment is used for collecting data according to the instruction of the command after receiving the offline command and storing the collected data to the target platform.
The NB network analysis platform comprises an NB network analysis module and a data acquisition management module.
The NB network analysis module is used for analyzing data, can be used for industry comprehensive analysis, and can carry out statistical analysis according to different industries, wherein the statistical analysis content comprises industry network quality and business requirements of the industries. The statistical analysis can be performed according to different industries, and multidimensional analysis such as market development conditions of the industries, industry network quality, industry value and the like can be output.
The NB network analysis module can realize the functions of the industry comprehensive analysis module in the method embodiment.
The data acquisition management module is used for processing and storing data, and can be responsible for integrating business behavior data and call ticket data of the terminal, classifying businesses according to flow and reporting frequency, and carrying out AI modeling on the terminal of the Internet of things.
The data acquisition management module can realize the functions of the data processing module and the AI modeling module in the embodiment of the method.
The main platform is responsible for equipment group management and task management, and synchronizes the data of the target platform through the data receiving service, and the synchronization process adopts grpc encryption to store the data of the target company into the main mong odb module.
The Sass application platform is responsible for receiving network parameter data stored in the main mongolidb in an API interface mode. And reporting network parameters by adopting the SDK, realizing measurement and reporting of the MR-like (measurement report), and carrying out network analysis on an application platform to support NB-IoT network optimization.
Specifically, the NB-IoT wireless network analysis system described above mainly includes a data acquisition module and an NB network analysis platform; the data acquisition module comprises a main platform, a provincial platform and a Sass application platform; the NB network analysis platform comprises an NB network analysis module and a data acquisition management program; further, the main platform comprises a main Mongodb module, an acquisition group management/task management module, a command distribution service module, a data receiving service module, an API interface module and a statistics service module; the main platform is mainly responsible for equipment group management and task management, and synchronizes the data of the provincial platform through data receiving service, and the synchronization process adopts grpc encryption to store the provincial company data to the mongolidb module; the main mong odb module is mainly responsible for: basic functions of the database include equipment group management information, task management information, statistical information and network parameter information storage and management; the acquisition group management module is mainly responsible for carrying out batch management on equipment, and the task management module is mainly responsible for configuring tasks, wherein the configuration comprises acquisition of network parameter items, acquisition frequency, trigger time, task equipment control and the like; meanwhile, the parameter acquisition instruction and the acquisition frequency can be configured, and can be set according to the scene as required, so that resource waste is avoided; the command distribution service module is mainly responsible for distributing tasks to the provincial platform according to task configuration; the data receiving service module is mainly responsible for synchronizing the data of the provincial platform, and the synchronization process adopts grpc encryption to store the provincial company data to mongolidb; the API interface module is mainly responsible for transmitting the network parameter data stored in the mongolib to an upper application platform in an interface mode; the statistical service module is mainly responsible for carrying out statistical analysis on the acquisition task process in dimensions such as products, modules, provincial platforms and the like; further, the provincial platform comprises an API cache command interface, a Mongodb module, an acquisition service module, an NB access machine module and terminal equipment; the API cache command interface is mainly responsible for storing offline commands and replacing or deleting offline commands; the Mongodb module is mainly responsible for storing and distributing the acquisition task cache command, managing the access information of the equipment and managing the reporting data of the equipment; the acquisition service module is mainly responsible for issuing an off-line command after the terminal equipment is on line; when the terminal equipment registers and updates the platform, the acquisition instruction is issued along with the acquisition instruction, the equipment does not need to be independently awakened, and the extra power consumption is hardly perceived; the NB access machine module is responsible for access management of the terminal equipment; issuing an off-line command to the terminal equipment; the terminal equipment is responsible for collecting data according to the instruction of the command after receiving the offline command, and storing the collected data to the provincial platform; further, the Sass application platform is responsible for receiving network parameter data stored in mongasdb in an API (application program interface) mode; and reporting network parameters by adopting the SDK, measuring and reporting the MR-like report, and carrying out network analysis on an application platform to support NB-IoT network optimization.
The principle of operation of the system according to the invention can be found in the description of the embodiments of the method described above. The implementation manner of the wireless network analysis system according to the embodiment of the present invention is the same as that of the foregoing embodiment, and all the implementation manners in the foregoing embodiment are applicable to the embodiment, so that the same technical effects can be achieved.
According to the wireless network analysis system provided by the embodiment of the invention, network quality evaluation analysis can be performed on the Internet of things equipment, and statistical analysis can be performed on business requirements of industries through accumulated industry information data, so that guidance is provided for package customization; meanwhile, according to network index analysis of the industry, a target company is helped to find a network short board, so that an impetus can be found for development of key industries, and a guiding direction is provided for popularization of the industry; the business and network index analysis is carried out according to the industry, which is superior to the traditional analysis method, is more suitable for the multi-scene characteristics of the business of the Internet of things, and is convenient for users to better know and optimize the network.
Embodiments of the present invention also provide a computer-readable storage medium including instructions which, when executed on a computer, cause the computer to perform the methods provided by the method embodiments described above. All the implementation manners in the above embodiment are applicable to this embodiment, and the same technical effects can be achieved.
In the several embodiments provided in the present invention, it should be understood that the disclosed methods and apparatus may be implemented in other ways. For example, the system embodiments described above are merely illustrative, such as a division of units, merely a division of logic functions, and there may be additional divisions in actual implementation, such as multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical, or other forms.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may be physically included separately, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware, or may be implemented in hardware plus software functional units.
The integrated units implemented in the form of software functional units described above may be stored in a computer readable storage medium. The software functional unit is stored in a storage medium, and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform part of the steps of the transceiving method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only-Memory (ROM), a random access Memory (Random Access Memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
While the foregoing is directed to the preferred embodiments of the present invention, it will be appreciated by those skilled in the art that various modifications and adaptations can be made without departing from the principles of the present invention, and such modifications and adaptations are intended to be comprehended within the scope of the present invention.
Claims (9)
1. A wireless network analysis system, characterized in that the wireless network analysis system comprises an OneNET data acquisition module and an NB-IoT network analysis platform;
the OneNET data acquisition module comprises a main platform, a target platform and an application platform;
the main platform comprises:
the main database module is in charge of storing and managing equipment group management information, task management information, statistical information and network parameter information;
the acquisition group management/task management module is responsible for carrying out batch management on equipment and configuring tasks, wherein the configuration comprises one or more of acquisition network parameter items, acquisition frequency, trigger time, task equipment control, parameter acquisition instructions and acquisition frequency;
the command distribution service module is responsible for distributing the task to the target platform according to the task configuration;
the data receiving service module is responsible for synchronizing the data of the target platform and storing the data of the target platform into the main database module;
the API interface module is responsible for transmitting the network parameter data stored in the main database module to the upper application platform in an interface mode;
the statistical service module is responsible for carrying out statistical analysis on the task collecting process;
the target platform comprises:
the API cache command interface is in charge of storing offline commands and replacing or deleting the offline commands;
the target database module is responsible for the storage and distribution of the acquisition task cache command, the management of equipment access information and the management of equipment report data;
the acquisition service module is responsible for issuing an offline command after the terminal equipment is online; when the terminal equipment is registered or updated, the acquisition instruction is issued concomitantly, and the terminal equipment does not need to be independently awakened;
the NB-IoT access machine module is responsible for the access management of the terminal equipment and issues an offline command to the terminal equipment;
the terminal equipment is used for collecting data according to the instruction of the command after receiving the offline command and storing the collected data to the target platform;
the NB-IoT network analysis platform comprises an NB-IoT network analysis module and a data acquisition management module;
the application platform is responsible for receiving network parameter data stored in the main database in a manner of API interface.
2. A wireless network analysis method applied to the wireless network analysis system according to claim 1, the method comprising:
the data acquisition management module performs acquisition task configuration and issues the acquisition task configuration to the OneNET platform; the acquisition task configuration comprises an acquisition instruction and an acquisition frequency;
the OneNET platform issues the acquisition task to a device cache command list;
acquiring an acquisition task when registering or updating the equipment terminal;
the equipment terminal reads relevant network parameters according to the acquisition task and reports the parameters to the data acquisition management module through the OneNET platform;
the data acquisition management module processes and stores the data;
the data acquisition management module communicates the data to the NB-IoT network analysis platform or other application platform in an application program interface API manner.
3. The wireless network analysis method of claim 2, further comprising the steps of:
the OneNET data acquisition module also sets the following reporting parameters of the SDK at the equipment side:
a. when the platform side does not issue parameter reporting settings, the SDK of the equipment side defaults to read all data of relevant network parameters of the equipment side and report the data in the process of reporting the data;
b. if the platform side issues a write command, the device side SDK correspondingly sets network data for subsequent re-reporting according to the bit corresponding to the command field.
4. The wireless network analysis method of claim 2, further comprising the steps of:
the OnneT platform also extracts basic service information reported by the equipment terminal according to the service configuration of the equipment terminal;
the OnneT platform creates a ticket according to the extracted network parameter information and the basic service information and sends the ticket to the data processing module, wherein the ticket comprises a service package type, package service condition and other card opening information which are opened by the Internet of things card;
the data processing module integrates business behavior data and call ticket data of the terminal ， Classifying the services according to the traffic and reporting frequency;
the AI modeling module performs AI modeling on the terminal of the Internet of things according to the reporting behavior of the terminal and the information of the card of the Internet of things, performs verification by combining one or more of product information, card opening information and user investigation information of the platform of the Internet of things, outputs an industry model, and performs industry division on the terminal through the model;
the industry comprehensive analysis module performs statistical analysis according to different industries, and the statistical analysis content comprises industry network quality and business requirements of the industries.
5. The wireless network analysis method of claim 4, wherein AI modeling the internet of things terminal comprises the steps of:
according to a product function list in the object model, initially determining the industry of the terminal;
AI analysis is performed according to the flow model, identifying the closest industry.
6. The wireless network analysis method of claim 5, wherein AI modeling the internet of things terminal further comprises the steps of:
identifying industries by analyzing network coverage quality; or/and inquiring the card opening information of the Internet of things card corresponding to the terminal, and grabbing the industry characteristic information.
7. The wireless network analysis method according to claim 4, wherein the classifying the traffic according to the traffic and the reporting frequency is specifically: the service is divided into four types, namely a small flow long period, a large flow long period, a small flow short period and a large flow short period, and the classification algorithm is as follows:
small flow long period: the single month flow is less than or equal to 1Mb, and the single month reporting times are more than 31 times;
large flow rate long period: the flow rate of a month is more than 1Mb, and the reporting times of the month are more than 31 times;
small flow short period: the single month flow is less than or equal to 1Mb, and the single month reporting frequency is less than or equal to 31 times;
high flow short period: the single month flow is larger than 1Mb, and the single month reporting frequency is smaller than or equal to 31 times.
8. The wireless network analysis method according to claim 2, wherein the step of issuing the collection task to the device cache command list by the OneNET platform is specifically as follows:
if the device has a cache command which is not executed, updating the latest cache command; if the device does not have an unexecuted cache command, the cache command is added.
9. A computer readable storage medium comprising instructions which, when executed on a computer, cause the computer to perform the method of any of claims 2 to 8.
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