CN113473449A - Intelligent connection system based on Internet of things terminal - Google Patents
Intelligent connection system based on Internet of things terminal Download PDFInfo
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W8/00—Network data management
- H04W8/18—Processing of user or subscriber data, e.g. subscribed services, user preferences or user profiles; Transfer of user or subscriber data
- H04W8/20—Transfer of user or subscriber data
- H04W8/205—Transfer to or from user equipment or user record carrier
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W12/00—Security arrangements; Authentication; Protecting privacy or anonymity
- H04W12/06—Authentication
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W12/00—Security arrangements; Authentication; Protecting privacy or anonymity
- H04W12/30—Security of mobile devices; Security of mobile applications
- H04W12/35—Protecting application or service provisioning, e.g. securing SIM application provisioning
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W28/00—Network traffic management; Network resource management
- H04W28/16—Central resource management; Negotiation of resources or communication parameters, e.g. negotiating bandwidth or QoS [Quality of Service]
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- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W48/00—Access restriction; Network selection; Access point selection
- H04W48/16—Discovering, processing access restriction or access information
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W88/00—Devices specially adapted for wireless communication networks, e.g. terminals, base stations or access point devices
- H04W88/16—Gateway arrangements
Abstract
The invention relates to the technical field of network connection, in particular to an intelligent connection system based on an internet of things terminal. The system comprises a basic Internet of things unit, an intelligent connection unit and a quality data unit; the basic Internet of things unit is used for providing terminal equipment and software for supporting the connection and operation of the Internet of things system; the intelligent connection unit is used for building and managing an Internet of things intelligent connection cloud platform; the quality data unit is used for acquiring and calculating data in the running process of the Internet of things in real time. The invention designs that the embedded software collects the relevant basic information of the network and preferentially distributes the optimal network connection resources; the mobile network connection resources are issued through the intelligent connection cloud platform dynamic OTA, so that the cost of the mobile network connection resources is optimal, and the network is best; through machine learning, the accuracy of the algorithm model is improved, the mobile network quality is intelligently detected and evaluated through big data, the optimal mobile network is selected, and the optimal mobile network is dynamically issued through the cloud, so that the intelligent connection of the terminal equipment of the Internet of things is realized.
Description
Technical Field
The invention relates to the technical field of network connection, in particular to an intelligent connection system based on an internet of things terminal.
Background
With the rapid development of the internet of things, more and more devices are accessed through a mobile cellular network, data transmission is performed through a 4G/5G network, and data interaction is performed with a cloud platform. However, as the number of the devices in the internet of things is increased, the network quality of a single operator may not meet the networking requirements of the devices, such as: in a certain position, a base station signal of a home telecommunication operator may be uncovered, which causes that devices connected by using the operator network cannot surf the internet and transmit data, and seriously affects related functions of internet of things services. In the prior art, a SIM card of a home telecom operator is usually inserted into an internet of things device, and authentication/network access is performed through the SIM card, so as to complete mobile network connection of the internet of things device. In the mode, the 4G/5G signal coverage condition of the mobile telecommunication operator corresponding to the SIM card is seriously depended.
In the prior art, polling is performed only through existing network resources of various home telecommunication operators in a SIM (subscriber identity module) inserted by the Internet of things equipment, and an optimal mobile network is selected only according to simple signal intensity. And the prior art has the following main defects: the resources in the physical SIM card need to be cured in advance and cannot be issued from the cloud OTA, so that the network resources are dynamically loaded, and the failure of connection is easily caused by the failure of a certain resource; the network is selected only according to simple signal intensity, and the network which is possibly selected is high in signal intensity, but the connection experience of the equipment of the Internet of things is poor due to the fact that the current base station has a large number of accessed equipment, the network is crowded, and the network speed is low; the Internet of things SIM card needs to be customized and supports one card with multiple numbers, while the common SIM card does not support and cannot display the optimal network connection.
Disclosure of Invention
The invention aims to provide an intelligent connection system based on an internet of things terminal, so as to solve the problems in the background technology.
In order to solve the above technical problem, one of the objectives of the present invention is to provide an intelligent connection system based on an internet of things terminal, including:
the system comprises a basic Internet of things unit, an intelligent connection unit and a quality data unit; the basic internet of things unit, the intelligent connection unit and the quality data unit are sequentially connected through Ethernet communication; the basic Internet of things unit is used for providing terminal equipment, a communication base station, matched software and the like for supporting the connection and operation of the Internet of things system; the intelligent connection unit is used for building an Internet of things intelligent connection cloud platform and providing a corresponding management function; the quality data unit is used for creating a network quality big data system and acquiring and calculating data in the running process of the Internet of things in real time;
the basic internet of things unit comprises a gateway equipment module, an embedded software module, a mobile communication module and a technical support module;
the intelligent connection unit comprises a gateway access module, an acquisition and transmission module, a scheduling management module and a card configuration feedback module;
the quality data unit comprises a data acquisition module, an algorithm model module, a data calculation module and a machine learning module.
As a further improvement of the technical solution, the gateway device module, the embedded software module, the mobile communication module and the technical support module are sequentially connected through ethernet communication and run in parallel; the gateway equipment module is used for providing a connection and compatible platform for various communication technologies through the Internet of things intelligent gateway equipment and providing an equipment control network channel; the embedded software module is used for collecting current network information by loading embedded intelligent software in the Internet of things equipment so as to support intelligent network selection; the mobile communication module is used for establishing a multi-network Internet of things foundation by accessing different mobile network base stations and corresponding communication modules into a system; the technical support module is used for dominating network connection resources and realizing intelligent connection of the terminal equipment of the Internet of things by loading various different network connection technologies.
The network base station mainly comprises mobile, universal, telecommunication and the like.
As a further improvement of the technical solution, in the technical support module, the loaded technology is an eSIM technology or a cloud SIM technology; the eSIM technology is to directly embed a conventional SIM card into an equipment chip to allow a user to more flexibly select or replace an operator; the cloud SIM technology is used for storing and managing a large number of SIM cards in a centralized manner and dynamically allocating the SIM cards to terminal equipment in real time for use, does not need to insert cards or use any equipment, and can automatically access an operator network to provide WiFi service.
As a further improvement of the technical solution, the gateway access module, the acquisition and transmission module, the scheduling management module and the card configuration feedback module are sequentially connected through ethernet communication; the gateway access module is used for receiving MSD information and a connection request reported by the embedded software of the Internet of things equipment through an intelligent gateway arranged on an intelligent connection cloud platform; the acquisition transmission module is used for acquiring current network information acquired and reported by the embedded software and forwarding quality data to a network quality big data system; the scheduling management module is used for scheduling, distributing and managing network connection resources; the card allocation feedback module is used for allocating resources of the optimal network connection according to the network quality data calculated by the network quality big data system.
As a further improvement of the technical solution, a signal output end of the data acquisition module is connected with a signal input end of the algorithm model module, a signal output end of the algorithm model module is connected with a signal input end of the data calculation module, and a signal output end of the data calculation module is connected with a signal input end of the machine learning module; the data acquisition module is used for acquiring current network information data acquired by the embedded software of the Internet of things equipment through the intelligent connection cloud platform, carrying out ETL processing on the data, and storing the data after each processing to generate a large historical database; the algorithm model module is used for loading an optimal numerical algorithm model and calculating optimal network connection resources by combining historical big data; the data calculation module is used for obtaining optimal network index data by combining historical network data through a plurality of main network connection elements and by using a numerical calculation method for approximate solution; the machine learning module is used for optimizing the algorithm model through machine learning so as to improve the accuracy of data calculation.
The ETL is a process of extracting (Extract) and cleaning and converting (Transform) system data, and then loading (Load) the system data into a data warehouse, so as to integrate scattered, random, and standard non-uniform data together, and provide an analysis basis for system decision.
As a further improvement of the technical scheme, in the algorithm model module, the optimal numerical algorithm model adopts an optimal power flow algorithm, and the mathematical model thereof is as follows:
in the formula, F is a scalar target function, G is an equality constraint condition, H is an inequality constraint condition, x is a state variable, and u is a control variable.
The principle of the optimal power flow algorithm model (OPF) is described as follows: under the condition of given network structure, parameters and coefficient load, the control quantity of the system is determined, various inequality constraints are met, and a given objective function for describing the operation benefit of the system is enabled to be extreme, so that the method belongs to a typical nonlinear programming problem.
As a further improvement of the technical solution, the data calculation module adopts a pareto optimal solution algorithm for solving the multi-objective optimization problem, and a mathematical model expression thereof is as follows:
the above formula represents n objective functions, the objective is to minimize the n objective functions, and the above formula enables a constraint set of variables to be understood as a value range of the variables;
in the formula, X belongs to X and is represented as X belonging to a set X, and a solution X meeting the constraint condition is called a feasible solution;
furthermore, f1(x) Expressed as GPS position distance, f2(x) Expressed as the current network signal strength negative value, f3(x) Negative value of the network speed, … …, f, expressed as the current networkp(x) Denoted as the latency of the current network.
As a further improvement of the technical solution, in the machine learning module, for the conventional optimal numerical calculation, a machine learning model of a linear model is adopted, and the mathematical form of the machine learning model is expressed as:
g(x；w)＝wTx；
further, by transforming the output of the linear model using a log-probability function, the following formula is derived:
wherein, the transformed g (x; w) is a nonlinear function derived from a linear function, called generalized linear function, and is suitable for training with log-likelihood loss or cross loss.
As a further improvement of the technical solution, in the machine learning module, a recurrent neural network algorithm model is adopted for directly performing network matching operation on extracted historical data, and an algorithm expression of the recurrent neural network algorithm model is as follows:
wherein s istRepresenting the memory generated at time t, from the input x taken at time ttAnd memory of time t-1t-1The result of the co-operation is U, W, V a parameter matrix.
The invention also aims to provide an operation method of the intelligent connection system based on the terminal of the Internet of things, which comprises the following steps:
s1, acquiring current information of the equipment through the Internet of things equipment (SR001) through embedded software vCOS: including position GPS, current network signal intensity, the latest network Profile of equipment contains: corresponding to operators, network speed, time delay, access IP addresses and the like;
s2, the embedded soft vCOS of the Internet of things equipment controls a network channel, an IP network or a USSD short message through intelligent gateway equipment, reports collected MDS (minimum Set of data) information to the intelligent connection cloud platform, and sends a connection resource request to the intelligent connection cloud platform;
s3, after verifying the terminal validity by the intelligent connection cloud platform, referring to historical big data (including historical network connection quality data, customer complaints and the like) by combining a connection resource allocation strategy, requesting real-time calculation, obtaining connection resources to be allocated according to an optimal numerical algorithm model, obtaining optimal network connection resources (SR002) by real-time calculation, and directly providing the most optimal connection resources;
s4, the intelligent connection cloud platform issues SR002 network connection resources and information (such as IMSI) required by network login through the dynamic card configuration module, and the information is handed to the Internet of things equipment to initiate a mobile network registration request;
s5, completing a series of interactions (refer to 3GPP specifications) between the mobile base station and a communication module matched with the mobile base station and the Internet of things equipment, and when the SIM card is required to process information (such as authentication), embedding software vCOS through the Internet of things equipment, forwarding an authentication request to an intelligent connection cloud platform, switching to received network connection resources, and selecting to complete a mobile network login process;
s6, the Internet of things equipment is successfully connected to the network resources distributed by the intelligent connection cloud platform, and network use conditions (including information such as flow, time, position, network state and operation quality) are reported at regular time;
s7, intelligently connecting the cloud platform, recording, storing and reporting information, supplementing cloud platform big data, optimizing an algorithm model according to a machine learning model algorithm, improving the accuracy of data calculation, and calculating the optimal network of the position at regular time as one of the bases for distributing network resources next time;
and S8, automatically storing the current optimal network Profile by the Internet of things equipment, and directly selecting connection according to time and position matching when the Internet of things equipment requests connection next time.
The invention also provides an operating device of the intelligent connection system based on the terminal of the internet of things, which comprises a processor, a memory and a computer program stored in the memory and operated on the processor, wherein the processor is used for realizing any intelligent connection system based on the terminal of the internet of things when executing the computer program.
It is a fourth object of the present invention to provide a computer-readable storage medium, which stores a computer program, and the computer program, when executed by a processor, implements any one of the above systems for intelligent connection based on terminals of internet of things.
Compared with the prior art, the invention has the beneficial effects that:
1. in the intelligent connection system based on the terminal of the Internet of things, the terminal equipment side of the Internet of things acquires network related basic information through embedded software, and optimally allocates optimal network connection resources according to the specific information of each piece of Internet of things equipment;
2. in the intelligent connection system based on the terminal of the Internet of things, the cloud end preferentially allocates mobile network connection resources for the terminal equipment of the Internet of things according to network information reported by the terminal equipment and combined with historical Internet surfing data through intelligent analysis, connection resource clouding is realized, the mobile network connection resources are dynamically issued to the side of the equipment of the Internet of things through an OTA (over the air) of an intelligent connection cloud platform, after embedded software of the equipment of the Internet of things receives the mobile network connection resources, current connection of the equipment is changed, newly received network connection resources are started to surf the Internet, and the cost of the mobile network connection resources is optimal and the network is the best;
3. in the intelligent connection system based on the terminal of the Internet of things, data are collected regularly to form mobile network quality big data which serve as the basis of optimal network calculation, meanwhile, the accuracy of an optimal numerical algorithm model is continuously improved through machine learning, the mobile network quality is intelligently detected and evaluated through the big data, an optimal mobile network is selected, mobile network connection resources are dynamically issued by the cloud, and intelligent connection of the terminal equipment of the Internet of things is achieved.
Drawings
FIG. 1 is a block diagram of an exemplary product architecture of the present invention;
FIG. 2 is a block diagram of the overall system apparatus of the present invention;
FIG. 3 is a diagram of one embodiment of a local system device architecture;
FIG. 4 is a diagram of one embodiment of a local system device architecture;
FIG. 5 is a diagram of one embodiment of a local system device architecture;
fig. 6 is a schematic diagram of an exemplary electronic computer architecture of the present invention.
The various reference numbers in the figures mean:
100. a base internet of things unit; 101. a gateway device module; 102. an embedded software module; 103. a mobile communication module; 104. a technical support module;
200. an intelligent connection unit; 201. a gateway access module; 202. a collection transmission module; 203. a scheduling management module; 204. a card allocation feedback module;
300. a quality data unit; 301. a data acquisition module; 302. an algorithm model module; 303. a data calculation module; 304. a machine learning module.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the 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.
Example 1
As shown in fig. 1 to 6, the present embodiment provides an intelligent connection system based on an internet of things terminal, including:
a basic thing networking unit 100, an intelligent connection unit 200 and a quality data unit 300; the basic internet of things unit 100, the intelligent connection unit 200 and the quality data unit 300 are sequentially connected through Ethernet communication; the basic internet of things unit 100 is used for providing terminal equipment, a communication base station, matched software and the like for supporting the connection and operation of the internet of things system; the intelligent connection unit 200 is used for building an internet of things intelligent connection cloud platform and providing a corresponding management function; the quality data unit 300 is used for creating a network quality big data system and acquiring and calculating data in the running process of the internet of things in real time;
the basic internet of things unit 100 comprises a gateway equipment module 101, an embedded software module 102, a mobile communication module 103 and a technical support module 104;
the intelligent connection unit 200 comprises a gateway access module 201, an acquisition and transmission module 202, a scheduling management module 203 and a card configuration feedback module 204;
the quality data unit 300 includes a data acquisition module 301, an algorithm model module 302, a data calculation module 303, and a machine learning module 304.
In this embodiment, the gateway device module 101, the embedded software module 102, the mobile communication module 103, and the technical support module 104 are sequentially connected through ethernet communication and operate in parallel; the gateway device module 101 is configured to provide a connection and compatible platform for multiple communication technologies through an internet of things intelligent gateway device, and provide a device control network channel; the embedded software module 102 is used for collecting current network information by loading embedded intelligent software in the internet of things equipment to support intelligent network selection; the mobile communication module 103 is used for establishing a multi-network internet of things foundation by accessing different mobile network base stations and corresponding communication modules into the system; the technical support module 104 is used for managing network connection resources and realizing intelligent connection of the terminal device of the internet of things by loading various different network connection technologies.
The network base station mainly comprises mobile, universal, telecommunication and the like.
Further, in the technical support module 104, the loaded technology is an eSIM technology or a cloud SIM technology; the eSIM technology is to directly embed a conventional SIM card into an equipment chip to allow a user to more flexibly select or replace an operator; the cloud SIM technology is used for storing and managing a large number of SIM cards in a centralized manner and dynamically allocating the SIM cards to terminal equipment in real time for use, does not need to insert cards or use any equipment, and can automatically access an operator network to provide WiFi service.
In this embodiment, the gateway access module 201, the acquisition and transmission module 202, the scheduling management module 203, and the card configuration feedback module 204 are sequentially connected through ethernet communication; the gateway access module 201 is configured to receive MSD information and a connection request reported by the internet of things device embedded software through an intelligent gateway arranged on an intelligent connection cloud platform; the acquisition and transmission module 202 is used for acquiring current network information acquired and reported by the embedded software and forwarding quality data to the network quality big data system; the scheduling management module 203 is used for scheduling, allocating and managing network connection resources; the card allocation feedback module 204 is configured to perform resource allocation of an optimal network connection according to the network quality data calculated by the network quality big data system.
In this embodiment, the signal output end of the data acquisition module 301 is connected to the signal input end of the algorithm model module 302, the signal output end of the algorithm model module 302 is connected to the signal input end of the data calculation module 303, and the signal output end of the data calculation module 303 is connected to the signal input end of the machine learning module 304; the data acquisition module 301 is used for acquiring current network information data acquired by the internet of things device embedded software through the intelligent connection cloud platform, performing ETL processing on the data, and storing the data after each processing to generate a large historical database; the algorithm model module 302 is used for loading an optimal numerical algorithm model and calculating optimal network connection resources by combining historical big data; the data calculation module 303 is configured to obtain optimal network index data by performing approximate solution by using a numerical calculation method through several main network connection elements in combination with historical network data; the machine learning module 304 is used to optimize the algorithm model by machine learning to improve the accuracy of the data calculation.
The ETL is a process of extracting (Extract) and cleaning and converting (Transform) system data, and then loading (Load) the system data into a data warehouse, so as to integrate scattered, random, and standard non-uniform data together, and provide an analysis basis for system decision.
Specifically, in the algorithm model module 302, the optimal numerical algorithm model adopts an optimal power flow algorithm, and the mathematical model thereof is as follows:
in the formula, F is a scalar target function, G is an equality constraint condition, H is an inequality constraint condition, x is a state variable, and u is a control variable.
The principle of the optimal power flow algorithm model (OPF) is described as follows: under the condition of given network structure, parameters and coefficient load, the control quantity of the system is determined, various inequality constraints are met, and a given objective function for describing the operation benefit of the system is enabled to be extreme, so that the method belongs to a typical nonlinear programming problem.
Specifically, the data calculation module 303 adopts a pareto optimal solution algorithm for solving the multi-objective optimization problem, and a mathematical model expression thereof is as follows:
the above formula represents n objective functions, the objective is to minimize the n objective functions, and the above formula enables a constraint set of variables to be understood as a value range of the variables;
in the formula, X belongs to X and is represented as X belonging to a set X, and a solution X meeting the constraint condition is called a feasible solution;
furthermore, f1(x) Expressed as GPS position distance, f2(x) Expressed as the current network signal strength negative value, f3(x) Negative value of the network speed, … …, f, expressed as the current networkp(x) Denoted as the latency of the current network.
Specifically, in the machine learning module 304, for the conventional optimal numerical calculation, a machine learning model of a linear model is adopted, and the mathematical form of the machine learning model is expressed as:
g(x；w)＝wTx；
further, by transforming the output of the linear model using a log-probability function, the following formula is derived:
wherein, the transformed g (x; w) is a nonlinear function derived from a linear function, called generalized linear function, and is suitable for training with log-likelihood loss or cross loss.
Specifically, in the machine learning module 304, a recurrent neural network algorithm model is adopted for directly performing network matching operation on the extracted historical data, and an algorithm expression is as follows:
wherein s istRepresenting the memory generated at time t, from the input x taken at time ttAnd memory of time t-1t-1The result of the co-operation is U, W, V a parameter matrix.
The embodiment also provides an operation method of the intelligent connection system based on the terminal of the internet of things, which comprises the following steps:
s1, acquiring current information of the equipment through the Internet of things equipment (SR001) through embedded software vCOS: including position GPS, current network signal intensity, the latest network Profile of equipment contains: corresponding to operators, network speed, time delay, access IP addresses and the like;
s2, the embedded soft vCOS of the Internet of things equipment controls a network channel, an IP network or a USSD short message through intelligent gateway equipment, reports collected MDS (minimum Set of data) information to the intelligent connection cloud platform, and sends a connection resource request to the intelligent connection cloud platform;
s3, after verifying the terminal validity by the intelligent connection cloud platform, referring to historical big data (including historical network connection quality data, customer complaints and the like) by combining a connection resource allocation strategy, requesting real-time calculation, obtaining connection resources to be allocated according to an optimal numerical algorithm model, obtaining optimal network connection resources (SR002) by real-time calculation, and directly providing the most optimal connection resources;
s4, the intelligent connection cloud platform issues SR002 network connection resources and information (such as IMSI) required by network login through the dynamic card configuration module, and the information is handed to the Internet of things equipment to initiate a mobile network registration request;
s5, completing a series of interactions (refer to 3GPP specifications) between the mobile base station and a communication module matched with the mobile base station and the Internet of things equipment, and when the SIM card is required to process information (such as authentication), embedding software vCOS through the Internet of things equipment, forwarding an authentication request to an intelligent connection cloud platform, switching to received network connection resources, and selecting to complete a mobile network login process;
s6, the Internet of things equipment is successfully connected to the network resources distributed by the intelligent connection cloud platform, and network use conditions (including information such as flow, time, position, network state and operation quality) are reported at regular time;
s7, intelligently connecting the cloud platform, recording, storing and reporting information, supplementing cloud platform big data, optimizing an algorithm model according to a machine learning model algorithm, improving the accuracy of data calculation, and calculating the optimal network of the position at regular time as one of the bases for distributing network resources next time;
and S8, automatically storing the current optimal network Profile by the Internet of things equipment, and directly selecting connection according to time and position matching when the Internet of things equipment requests connection next time.
As shown in fig. 6, the present embodiment further provides an operating apparatus of an intelligent connection system based on an internet of things terminal, where the apparatus includes a processor, a memory, and a computer program stored in the memory and running on the processor.
The processor comprises one or more than one processing core, the processor is connected with the processor through a bus, the memory is used for storing program instructions, and the intelligent connection system based on the Internet of things terminal is realized when the processor executes the program instructions in the memory.
Alternatively, the memory may be implemented by any type or combination of volatile or non-volatile memory devices, such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disks.
In addition, the invention also provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to realize the intelligent connection system based on the terminal of the internet of things.
Optionally, the present invention further provides a computer program product containing instructions, which when run on a computer, causes the computer to execute the above-mentioned aspects of the system for intelligent connection based on terminals of the internet of things.
It will be understood by those skilled in the art that all or part of the steps for implementing the above embodiments may be implemented by hardware, or may be implemented by a program instructing relevant hardware, and the program may be stored in a computer-readable storage medium, where the above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disk, etc.
The foregoing shows and describes the general principles, essential features, and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, and the preferred embodiments of the present invention are described in the above embodiments and the description, and are not intended to limit the present invention. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (10)
1. System of intelligent connection based on thing networking terminal, its characterized in that: the method comprises the following steps:
the system comprises a basic Internet of things unit (100), an intelligent connection unit (200) and a quality data unit (300); the basic Internet of things unit (100), the intelligent connecting unit (200) and the quality data unit (300) are sequentially connected through Ethernet communication; the basic Internet of things unit (100) is used for providing terminal equipment, a communication base station, matched software and the like for connecting and operating the support Internet of things system; the intelligent connection unit (200) is used for building an Internet of things intelligent connection cloud platform and providing a corresponding management function; the quality data unit (300) is used for creating a network quality big data system and acquiring and calculating data in the running process of the Internet of things in real time;
the basic internet of things unit (100) comprises a gateway equipment module (101), an embedded software module (102), a mobile communication module (103) and a technical support module (104);
the intelligent connection unit (200) comprises a gateway access module (201), an acquisition transmission module (202), a scheduling management module (203) and a card configuration feedback module (204);
the quality data unit (300) comprises a data acquisition module (301), an algorithm model module (302), a data calculation module (303) and a machine learning module (304).
2. The system for intelligent connection based on the internet of things terminal as claimed in claim 1, wherein: the gateway equipment module (101), the embedded software module (102), the mobile communication module (103) and the technical support module (104) are sequentially connected through Ethernet communication and run in parallel; the gateway equipment module (101) is used for providing a connection and compatible platform for various communication technologies through the Internet of things intelligent gateway equipment and providing an equipment control network channel; the embedded software module (102) is used for collecting current network information by loading embedded intelligent software in the Internet of things equipment so as to support intelligent network selection; the mobile communication module (103) is used for establishing a multi-network Internet of things foundation by accessing different mobile network base stations and corresponding communication modules into a system; the technology support module (104) is used for dominating network connection resources and realizing intelligent connection of the terminal equipment of the Internet of things by loading various different network connection technologies.
3. The system for intelligent connection based on the internet of things terminal as claimed in claim 2, wherein: in the technical support module (104), the loaded technology is an eSIM technology or a cloud SIM technology; the eSIM technology is to directly embed a conventional SIM card into an equipment chip to allow a user to more flexibly select or replace an operator; the cloud SIM technology is used for storing and managing a large number of SIM cards in a centralized manner and dynamically allocating the SIM cards to terminal equipment in real time for use, does not need to insert cards or use any equipment, and can automatically access an operator network to provide WiFi service.
4. The system for intelligent connection based on the internet of things terminal as claimed in claim 1, wherein: the gateway access module (201), the acquisition transmission module (202), the scheduling management module (203) and the card configuration feedback module (204) are sequentially connected through Ethernet communication; the gateway access module (201) is used for receiving MSD information and a connection request reported by embedded software of the Internet of things equipment through an intelligent gateway arranged on an intelligent connection cloud platform; the acquisition transmission module (202) is used for acquiring current network information acquired and reported by embedded software and forwarding quality data to a network quality big data system; the scheduling management module (203) is used for scheduling, distributing and managing network connection resources; the card allocation feedback module (204) is used for allocating resources of the optimal network connection according to the network quality data calculated by the network quality big data system.
5. The system for intelligent connection based on the internet of things terminal as claimed in claim 1, wherein: the signal output end of the data acquisition module (301) is connected with the signal input end of the algorithm model module (302), the signal output end of the algorithm model module (302) is connected with the signal input end of the data calculation module (303), and the signal output end of the data calculation module (303) is connected with the signal input end of the machine learning module (304); the data acquisition module (301) is used for acquiring current network information data acquired by the embedded software of the Internet of things equipment through the intelligent connection cloud platform, carrying out ETL processing on the data, and storing the data after each processing to generate a large historical database; the algorithm model module (302) is used for loading an optimal numerical algorithm model and calculating optimal network connection resources by combining historical big data; the data calculation module (303) is used for obtaining optimal network index data by combining historical network data through a plurality of main network connection elements and using a numerical calculation method to approximately solve; the machine learning module (304) is used for optimizing the algorithm model through machine learning so as to improve the accuracy of data calculation.
6. The system for intelligent connection based on the terminal of the internet of things of claim 5, wherein: in the algorithm model module (302), the optimal numerical algorithm model adopts an optimal power flow algorithm, and the mathematical model is as follows:
in the formula, F is a scalar target function, G is an equality constraint condition, H is an inequality constraint condition, x is a state variable, and u is a control variable.
7. The system for intelligent connection based on the terminal of the internet of things of claim 5, wherein: the data calculation module (303) adopts a pareto optimal solution algorithm for solving a multi-objective optimization problem, and a mathematical model expression of the data calculation module is as follows:
the above formula represents n objective functions, the objective is to minimize the n objective functions, and the above formula enables a constraint set of variables to be understood as a value range of the variables;
in the formula, X belongs to X and is represented as X belonging to a set X, and a solution X meeting the constraint condition is called a feasible solution;
furthermore, f1(x) Expressed as GPS position distance, f2(x) Expressed as the current network signal strength negative value, f3(x) Negative value of the network speed, … …, f, expressed as the current networkp(x) Denoted as the latency of the current network.
8. The system for intelligent connection based on the terminal of the internet of things of claim 5, wherein: in the machine learning module (304), aiming at the conventional optimal numerical calculation, a machine learning model of a linear model is adopted, and the mathematical form of the machine learning model is expressed as:
g(x；w)＝wTx；
further, by transforming the output of the linear model using a log-probability function, the following formula is derived:
wherein, the transformed g (x; w) is a nonlinear function derived from a linear function, called generalized linear function, and is suitable for training with log-likelihood loss or cross loss.
9. The system for intelligent connection based on the terminal of the internet of things of claim 5, wherein: in the machine learning module (304), a recurrent neural network algorithm model is adopted for directly carrying out network matching operation aiming at extracted historical data, and the algorithm expression is as follows:
wherein s istRepresenting the memory generated at time t, from the input x taken at time ttAnd memory of time t-1t-1The result of the co-operation is U, W, V a parameter matrix.
10. The system for intelligent connection based on the internet of things terminal as claimed in claim 1, wherein: the operation method of the system comprises the following steps:
s1, acquiring current information of the equipment through the Internet of things equipment (SR001) through embedded software vCOS: including position GPS, current network signal intensity, the latest network Profile of equipment contains: corresponding to operators, network speed, time delay, access IP addresses and the like;
s2, the embedded soft vCOS of the Internet of things equipment controls a network channel, an IP network or a USSD short message through intelligent gateway equipment, reports collected MDS (minimum Set of data) information to the intelligent connection cloud platform, and sends a connection resource request to the intelligent connection cloud platform;
s3, after verifying the terminal validity by the intelligent connection cloud platform, referring to historical big data (including historical network connection quality data, customer complaints and the like) by combining a connection resource allocation strategy, requesting real-time calculation, obtaining connection resources to be allocated according to an optimal numerical algorithm model, obtaining optimal network connection resources (SR002) by real-time calculation, and directly providing the most optimal connection resources;
s4, the intelligent connection cloud platform issues SR002 network connection resources and information (such as IMSI) required by network login through the dynamic card configuration module, and the information is handed to the Internet of things equipment to initiate a mobile network registration request;
s5, completing a series of interactions (refer to 3GPP specifications) between the mobile base station and a communication module matched with the mobile base station and the Internet of things equipment, and when the SIM card is required to process information (such as authentication), embedding software vCOS through the Internet of things equipment, forwarding an authentication request to an intelligent connection cloud platform, switching to received network connection resources, and selecting to complete a mobile network login process;
s6, the Internet of things equipment is successfully connected to the network resources distributed by the intelligent connection cloud platform, and network use conditions (including information such as flow, time, position, network state and operation quality) are reported at regular time;
s7, intelligently connecting the cloud platform, recording, storing and reporting information, supplementing cloud platform big data, optimizing an algorithm model according to a machine learning model algorithm, improving the accuracy of data calculation, and calculating the optimal network of the position at regular time as one of the bases for distributing network resources next time;
and S8, automatically storing the current optimal network Profile by the Internet of things equipment, and directly selecting connection according to time and position matching when the Internet of things equipment requests connection next time.
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