CN113938917A - Heterogeneous B5G/RFID intelligent resource distribution system applied to industrial Internet of things - Google Patents
Heterogeneous B5G/RFID intelligent resource distribution system applied to industrial Internet of things Download PDFInfo
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Abstract
The invention discloses a heterogeneous B5G/RFID intelligent resource distribution system applied to an industrial Internet of things, and the requirements of the industrial Internet of things on ultra-reliability, low delay and high data transmission rate are realized by using a novel B5G technology. Meanwhile, in order to solve the problems that communication of B5G industrial Internet of things equipment is easily interfered and stable power supply does not exist in a part of dynamic complex industrial scenes, the invention combines low-frequency RFID wireless energy-carrying communication and B5G wireless communication, and provides a heterogeneous B5G/RFID intelligent resource allocation system for various industrial environments. The system is optimized by using a reinforcement learning algorithm, and through continuous algorithm iteration in the industrial Internet of things environment, optimal spectrum allocation of B5G wireless communication, optimal power control of RFID wireless energy-carrying communication and optimal B5G/RFID communication selection in a dynamic complex industrial environment are respectively realized, optimal allocation of various resources is completed in real time, and communication efficiency of the industrial Internet of things is effectively improved.
Description
Technical Field
The invention relates to the field of resource allocation of the Internet of things, in particular to a heterogeneous B5G/RFID intelligent resource allocation system applied to the industrial Internet of things.
Background
With the comprehensive development of industrial automation and informatization, the fourth industrial revolution (industry 4.0) brings the internet of things into an industrial system, intelligent devices (sensors, actuators, machines and robots) can intelligently send data, real-time industrial control and environmental monitoring are realized with minimum human-computer interaction, and the industrial internet of things is formed. To increase the flexibility of mobile devices and reduce infrastructure investment, future factory and other industrial applications will gradually replace traditional wired communication networks with wireless networks. In practical industrial wireless networks, industrial internet of things devices are typically required to face the following challenges: strict delay and reliability requirements, high transmission data rate requirements, stable power supply, and scarce radio spectrum resources.
The developing B5G (Beyond 5G) technology is one of the approaches to address the above challenges. Currently, The fifth generation (5G) mobile communication network has started to be scaled and commercialized worldwide, and The 6G network has attracted attention. B5G is a suitable choice based on the current state of research as a path to the 6G era and its application to the industrial internet of things. The B5G has performance higher than 5G in the aspects of mass-connected machine communication, ultra-reliable networking application with low delay, and the like, but in the aspect of industrial internet of things application, there still exist the problems that part of dynamic complex industrial environments seriously interfere with B5G communication, and small industrial internet of things devices (such as intelligent sensors, small actuators, and the like) are difficult to obtain power supply in the dynamic complex industrial environments.
Therefore, the invention provides a heterogeneous B5G/RFID system, which ensures the communication reliability in a dynamic complex industrial environment by using low-Frequency RFID (radio Frequency identification) communication, provides stable power supply for small industrial Internet of things equipment in the dynamic complex industrial environment while providing the communication reliability by using the mature wireless energy-carrying communication technology of the RFID, and provides a solution for the application of the B5G technology in various industrial environments.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and complete a heterogeneous B5G/RFID intelligent resource distribution system design in an industrial environment. The intelligent resource distribution system applied to the industrial environment is composed of a B5G wireless communication terminal and a low-frequency RFID wireless energy-carrying communication terminal. Under a friendly industrial environment, the B5G communication can meet the requirements of ultra-reliable low-delay and high data transmission rate of industrial Internet of things equipment by improving the utilization rate of frequency spectrum resources. In a dynamic complex industrial communication scene, single B5G communication is easily interfered to cause data loss, and a communication device in a dynamic complex industrial environment is difficult to obtain stable power supply and frequent manual battery replacement is difficult to achieve. Therefore, low-frequency RFID wireless energy-carrying communication is introduced, stable power supply is provided for small industrial Internet of things equipment in a complex industrial scene, and meanwhile, the low-frequency RFID communication is switched to timely under the condition that B5G wireless communication is unstable, so that the reliability requirement of the communication of the small industrial Internet of things equipment in a dynamic complex industrial environment is met. The following is a brief description of a heterogeneous B5G/RFID smart resource allocation system.
Under friendly industrial environment, the industrial internet of things equipment carries out B5G wireless communication, and in order to utilize the spectrum resources of B5G communication to the maximum extent, when the spectrum resources are distributed, the industrial internet of things equipment is classified according to different communication service quality requirements: some industrial internet-of-things equipment needing real-time accurate control, such as intelligent robots, actuators and the like, optimize the reliability and the performance in the aspect of low delay; other devices that need to transmit high quality data such as pictures, audio, video, etc. optimize their performance in terms of high data rate: in an actual industrial scene, a plurality of dynamic complex industrial environments usually exist, the environments include scenes that environment states are changeable, such as underground petroleum, complex industrial production scenes and the like, and large-scale signal fading and a large amount of electromagnetic noise interference are often caused, and in the industrial environments, small industrial internet of things devices (mainly, an intelligent sensor for real-time monitoring, a small execution device and the like) need to perform B5G communication, and meanwhile, an RFID wireless energy-carrying communication module is installed, so that energy is provided for the small industrial internet of things devices in real time, and when B5G communication cannot be performed, a stable and reliable low-frequency information transmission channel is provided through intelligent power control of the RFID wireless energy-carrying communication.
Establishing a model for an industrial Internet of things environment consisting of the two communication environments, incorporating the spectrum allocation problem of B5G wireless communication, the power control problem of RFID wireless energy-carrying communication and the B5G/RFID communication selection problem into the model, converting the optimization problem of the communication system into a Markov decision process, and further performing iteration and optimization in the environment by using a reinforcement learning algorithm. Finally, the requirements of ultra-reliability, low delay and high transmission rate of various devices in different industrial environments are met, and stable power supply is provided for small industrial Internet of things devices in dynamic complex industrial environments.
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FIG. 1 is a schematic diagram of a system model of the present invention.
FIG. 2 is a schematic diagram of the reinforcement learning algorithm of the present invention.
FIG. 3 is a schematic diagram of the connection relationship of the system modules of the present invention
Detailed Description
The invention will be described in further detail below with reference to the figures and specific embodiments.
Fig. 1 is a schematic diagram of a system model according to the present invention. In a friendly industrial environment, in order to improve the performance of B5G communication as much as possible, the devices are divided into two groups according to communication requirements, one group is an ultra-reliable low-delay device, the other group is a high-data transmission rate device, and the system respectively optimizes the reliability, the delay and the transmission rate of the two groups of devices so as to realize the optimization of the efficiency of the communication system. In a dynamic complex environment, the industrial Internet of things equipment preferentially performs B5G communication, when the signal to interference plus noise ratio of communication fails to reach a threshold value due to the dynamic change of the environment and the communication is extremely unstable, the system rapidly switches the B5G transmission channel into the RFID transmission channel, the reliability requirement is preferentially considered, and data is transmitted to the controller in time to the greatest extent under the condition that the data is not lost, so that the danger that the data cannot be transmitted in an emergency condition is avoided, the stability of the communication system is guaranteed, and the safe operation of an industrial field is further realized.
As shown in fig. 2, which is a schematic diagram of a reinforcement learning algorithm of the present invention, first, a node B5G, an RFID node, and an industrial internet of things device are used as an agent; taking the channel working state (power size, whether in idle state, etc.), the channel quality (mainly signal-to-interference plus noise ratio SINR), the service quality (QoS, including delay, reliability, data rate, etc.) as the state observation value; spectrum allocation, power allocation and heterogeneous communication selection (B5G communication and RFID communication are selected) are taken as a behavior space; and setting various reward functions by using the state observation values according to different requirements of industrial Internet of things equipment, and converting the optimization problem of the communication system into a Markov decision process. On the basis, the reinforcement learning algorithm is set as follows: the heterogeneous B5G/RFID system extracts the state observation value and obtains the reward value through sufficient interaction and learning with the environment of the industrial Internet of things. Meanwhile, action value is evaluated based on the feedback state, action is taken in the action space, and corresponding decision is made in a self-adaptive manner. In addition, the industrial environment state is changeable, the communication system needs to continuously interact with the environment to obtain a next-level observation result, and the overall communication efficiency of the communication system is optimized through the loop iteration self-adaptive dynamic environment.
Fig. 3 is a schematic diagram showing connection of system modules of the present invention, where the diagram includes a node B5G, an RFID node, an industrial internet of things device in a friendly environment, and an industrial internet of things device in a complex environment. Under a friendly industrial environment, the industrial internet of things equipment acquires data through the information acquisition and processing module, transmits the data to the B5G antenna module after processing the data, transmits the information to the B5G node through wireless transmission, and the B5G node controller acquires a required state observation value through the data received by the antenna module, and then takes action through the reinforcement learning module, and further allocates frequency spectrum resources. Under a complex industrial environment, the industrial Internet of things equipment collects information through an information acquisition processing module, and selects a B5G/RFID communication mode through an intelligent communication selection module to transmit the information; for the RFID node, the controller acquires a state observation value through the RFID antenna module and takes action through the reinforcement learning module so as to perform intelligent power control and ensure normal communication; in addition, wireless signals and energy transmitted to the industrial Internet of things equipment by the nodes can be shunted through the RFID antenna module at the equipment end, and the shunted energy is charged for the power supply module, so that the normal work of the equipment is guaranteed.
Claims (5)
1. Heterogeneous B5G/RFID intelligent resource distribution system applied to industrial Internet of things, which is characterized in that: the system uses B5G in the industrial Internet of things, and realizes the requirements for ultra-reliable low delay and high data transmission rate by improving the signal-to-noise ratio and data transmission rate of communication and reducing the total delay of communication; the system utilizes RFID wireless energy carrying communication to provide a reliable information transmission channel and stable energy supply for small industrial equipment in a dynamic complex industrial environment; the system combines the B5G wireless communication technology with the RFID wireless energy-carrying communication technology, uses the B5G communication in a friendly industrial environment to improve the communication efficiency, and in a dynamic complex industrial environment, small industrial equipment is simultaneously provided with the B5G communication module and the RFID wireless energy-carrying communication module so as to realize timely switching of communication modes in the dynamic complex industrial environment, perform reliable data transmission and simultaneously meet the requirement of real-time communication transmission to the maximum extent; the system utilizes a reinforcement learning algorithm for optimization, and realizes optimization of frequency spectrum allocation of B5G wireless communication, power allocation of RFID wireless energy-carrying communication and real-time selection of B5G/RFID communication in a dynamic complex industrial environment by continuously interacting with the environment and performing algorithm iteration.
2. The heterogeneous B5G/RFID intelligent resource distribution system applied to industrial Internet of things of claim 1, wherein: the industrial Internet of things and the B5G technology are combined, different requirements of different devices in the industrial Internet of things on communication service quality are met in a targeted mode through the B5G technology, and communication efficiency of the industrial Internet of things is improved.
3. The heterogeneous B5G/RFID intelligent resource distribution system applied to industrial Internet of things of claim 1, wherein: the RFID wireless energy-carrying communication is applied to dynamic complex industrial environments, the RFID receiver can use modes such as power division and time slot switching, a reliable information transmission channel can be provided when small-sized industrial equipment cannot perform B5G communication, and stable power supply can be continuously provided for the small-sized equipment.
4. The heterogeneous B5G/RFID intelligent resource distribution system applied to industrial Internet of things of claim 1, wherein: the B5G wireless communication technology and the RFID wireless energy carrying communication technology are combined to construct a heterogeneous B5G/RFID communication system so as to meet the different requirements of similar industrial equipment and the challenges brought by various industrial environments to the communication performance of the industrial Internet of things.
5. The heterogeneous B5G/RFID intelligent resource distribution system applied to industrial Internet of things of claim 1, wherein: a constructed heterogeneous B5G/RFID system is optimized by using a reinforcement learning algorithm, a state observation value is extracted through full interaction with the environment of the industrial Internet of things, an incentive value is obtained, actions are continuously taken according to environment changes, and optimal selection of frequency spectrum, power and B5G/RFID communication is carried out in real time.
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Cited By (4)
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| CN114356535A (en) * | 2022-03-16 | 2022-04-15 | 北京锦诚世纪咨询服务有限公司 | Resource management method and device for wireless sensor network |
| CN117376355A (en) * | 2023-10-31 | 2024-01-09 | 重庆理工大学 | B5G mass Internet of things resource allocation method and system based on hypergraph |
| CN117440442A (en) * | 2023-10-31 | 2024-01-23 | 重庆理工大学 | Internet of things resource conflict-free distribution method and system based on graph reinforcement learning |
| CN118034673A (en) * | 2024-04-11 | 2024-05-14 | 四川湖山电器股份有限公司 | Visual editing generation and dynamic analysis system for equipment model of Internet of things |
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN114356535A (en) * | 2022-03-16 | 2022-04-15 | 北京锦诚世纪咨询服务有限公司 | Resource management method and device for wireless sensor network |
| CN117376355A (en) * | 2023-10-31 | 2024-01-09 | 重庆理工大学 | B5G mass Internet of things resource allocation method and system based on hypergraph |
| CN117440442A (en) * | 2023-10-31 | 2024-01-23 | 重庆理工大学 | Internet of things resource conflict-free distribution method and system based on graph reinforcement learning |
| CN117376355B (en) * | 2023-10-31 | 2024-07-09 | 重庆理工大学 | B5G mass Internet of things resource allocation method and system based on hypergraph |
| CN118034673A (en) * | 2024-04-11 | 2024-05-14 | 四川湖山电器股份有限公司 | Visual editing generation and dynamic analysis system for equipment model of Internet of things |
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