5G-based intelligent Internet of Things communication system and control method thereof

Abstract

The invention discloses a 5G-based intelligent internet-of-things communication system and a control method thereof, and particularly relates to the technical field of internet-of-things communication, and the method comprises the steps: completing the communication with different levels of receiving internet-of-things equipment through controlling the parameters of an antenna unit in transmission internet-of-things equipment, determining the signal characteristics of different levels of communication signals through wavelet analysis, and carrying out the communication with the receiving internet-of-things equipment. The complexity of the communication signal at each level is determined by estimating the probability density function of the signal and using cross entropy analysis, the spectrum information of the communication signal is obtained, the response time of the communication signal is monitored and recorded in real time, an ARMA model is trained based on the response time data of different levels, and the time deviation information of the communication signal is obtained; the spectrum information and the time deviation information of the communication signals under different levels are comprehensively analyzed, the performance of the communication signals under different signals is quantified, the analysis of the communication performance of different levels is facilitated, and the signal transmission quality in various complex environments is optimized.

Description

Technical Field

[0001] The present invention relates to the field of Internet of Things communication technology, and more specifically, to a 5G-based intelligent Internet of Things communication system and a control method thereof. Background Art

[0002] In the 5G smart IoT communication system, signal transmission conditions are changing dynamically and are affected by many factors, such as user distribution, environmental changes, wireless interference, frequency selection, etc. Due to the support for more device connections, high-speed data transmission and lower latency, fluctuations in communication quality are more significant. Communication between IoT devices is no longer limited to a single frequency band or static environment. The directionality, spectrum requirements and changes in signal transmission status between devices require more flexible and intelligent communication control mechanisms. Traditional signal evaluation is usually based on a single signal parameter (such as signal strength, bandwidth, delay, etc.), which cannot fully cope with complex channel changes at different levels.

[0003] In order to solve the above defects, a technical solution is now provided. Summary of the invention

[0004] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a 5G smart IoT communication system and a control method thereof to solve the problems raised in the above-mentioned background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions: A 5G intelligent IoT communication system, specifically comprising a tunable RF front-end module, a spectrum analysis module, a response analysis module and a multi-level channel evaluation module, and signal connections between the modules; A tunable RF front-end module is used to communicate with receiving IoT devices at different levels by controlling the parameters of the antenna unit in the transmitting IoT device, where the different levels refer to the direction and the required frequency band of the receiving IoT device; The spectrum analysis module is used to determine the signal characteristics of communication signals at different levels through wavelet analysis based on the communication signals received by the IoT device, and to determine the complexity of the communication signals at each level by estimating the probability density function of the signal using cross entropy analysis to obtain the spectrum information of the communication signals.

[0006] The response analysis module is used to monitor and record the response time of communication signals in real time, train the ARMA model based on the response time data at different levels, and obtain the time deviation information of communication signals.

[0007] The multi-level channel assessment module is used to comprehensively analyze the spectrum information and time deviation information of communication signals at different levels, build a hierarchical channel assessment model, and quantify the performance of communication signals under different signals.

[0008] In a preferred embodiment, obtaining spectrum information of a communication signal includes: The spectrum information of the signal is represented by the signal energy anomaly coefficient and the signal complex variation coefficient; The acquisition logic of the signal energy anomaly coefficient is: collect the signal within the monitoring time period under the set level, perform wavelet transform on the received signal, and determine the waveform data of the signal under the level frequency band. The expression is: ;in, is the frequency domain waveform data of the signal in the i-th level frequency band, is the time domain waveform data of the signal in the monitoring period at the i-th level, is the scale, the translation parameter is the wavelet function of b, i=1, 2, 3, ..., I, I is a positive integer, i is the number of different levels; The frequency difference coefficient of the signal in the hierarchical frequency band is obtained, and the frequency difference coefficient of the signal in the hierarchical frequency band is marked as: ;in, , is the maximum frequency of the signal during the monitoring period, is the minimum frequency of the signal during the monitoring period; The energy ratio coefficient of the signal in the hierarchical frequency band is obtained, and the energy difference coefficient of the signal in the hierarchical frequency band is marked as: ;in, It is the time point from the minimum frequency to the maximum frequency of the signal during the monitoring period; Calculate the signal energy anomaly coefficient, the calculation formula is: ;in, is the signal energy anomaly coefficient.

[0009] In a preferred embodiment, the signal complex variation coefficient includes: The logic for obtaining the complex coefficient of variation of the signal is as follows: according to the signal in the monitoring time period at the set level, the frequency change of the signal received by the IoT device is obtained, and the probability density of the received signal frequency is determined using kernel density estimation, and the expression is: ;in, is the probability density of the received signal frequency, pl is the frequency value at different time points in the monitoring period, n=1, 2, 3, ..., N, N is a positive integer, n is the number of different time points in the monitoring period, h is the bandwidth, and K is the Gaussian kernel function; Obtain the frequency change of the transmission signal of the IoT device, and use kernel density estimation to determine the probability density of the transmission signal frequency. The expression is: ;in, is the probability density of the transmission signal frequency; Calculate the complex coefficient of variation, the calculation formula is: ;in, is the signal complexity coefficient of variation.

[0010] In a preferred embodiment, obtaining the time deviation information of the communication signal includes: The time deviation information of the communication signal is represented by a response time deviation coefficient; The logic for obtaining the response time deviation coefficient is as follows: according to the response time data of IoT devices received at different levels, the ARMA model is used for training, the parameters of the ARMA model are optimized by the least squares method, and the error function of the ARMA model is obtained, which is expressed as follows: , is the error function of ARMA models at different levels, Predict the communication response time of receiving IoT devices for the ARMA model, is the actual response time of receiving the communication of the IoT device, m=1, 2, 3, ..., M, M is a positive integer, and m is the number of the response time data; The actual value of the response time of the IoT device received during the monitoring period is obtained, and the actual value of the response time of the IoT device received during the monitoring period is marked as: , where k=1, 2, 3, ..., K, K is a positive integer, k is the number of each response of the receiving IoT device, the trained ARMA model is used to predict the response time within the monitoring time period, and the predicted value of the response time of the receiving IoT device within the monitoring time period is obtained, and the predicted value of the response time of the receiving IoT device within the monitoring time period is marked as: ; Calculate the residual sum of squares of the response time of the receiving IoT device during the monitoring period. The calculation formula is: ;in, is the residual sum of squares of the response time of the IoT devices received during the monitoring period; Calculate the scale coefficient of the error function at different levels. The calculation formula is: ;in, is the scale coefficient of the error function at the same level, is the maximum value of the error function at different levels, is the minimum value of the error function at different levels; Calculate the response time deviation coefficient using the following formula: ;in, is the response time deviation coefficient.

[0011] In a preferred embodiment, constructing a hierarchical channel assessment model includes: Through comprehensive analysis of the spectrum information and time deviation information of communication signals at different levels, the signal energy anomaly coefficient, complex variation coefficient and response time deviation coefficient are weighted and calculated to construct a hierarchical channel assessment model and generate a hierarchical channel assessment coefficient. The calculation formula of the hierarchical channel assessment coefficient is: ;in, is the hierarchical channel estimation coefficient, are the proportional coefficients of signal energy anomaly coefficient, complex variation coefficient, and response time deviation coefficient, respectively. Both are greater than 0.

[0012] In a preferred embodiment, quantifying the performance of the communication signal under different signals includes: A hierarchical channel assessment coefficient threshold is set, and the hierarchical channel assessment coefficients of different levels are compared with the hierarchical channel assessment coefficient threshold. If the hierarchical channel assessment coefficient is greater than the hierarchical channel assessment coefficient threshold, a warning signal is generated; if the hierarchical channel assessment coefficient is less than the hierarchical channel assessment coefficient threshold, no warning signal is generated.

[0013] In a preferred embodiment, a 5G intelligent IoT communication control method specifically includes the following steps: S1: The receiving IoT device receives the communication signal from the transmission device, performs time-frequency conversion on the signal using wavelet analysis, extracts the signal characteristics in different frequency bands and directions, estimates the probability density of the signal through cross entropy analysis, evaluates the complexity of the signal, and obtains the spectrum information of the communication signal; S2: Monitor and record the response time of each communication in real time, train the ARMA model based on historical response time data, and use the model to predict the response time of different levels and compare it with the actual response time to obtain time deviation information; S3: Combines spectrum information and time deviation information, analyzes signal performance at different levels, and evaluates signal quality at different levels; S4: Adjust the communication parameters of IoT devices based on the hierarchical evaluation results.

[0014] Technical effects and advantages of the present invention: The present invention comprehensively analyzes the signal spectrum information, response time and its changes at different levels, monitors and optimizes the communication quality in real time, evaluates the signal using wavelet analysis, cross entropy and ARMA models, and then adjusts the configuration and beam direction of the antenna unit to achieve dynamic optimization of different directions and frequency bands, and quantifies the performance of communication signals at each level. The present invention can improve the stability of the communication system, reduce latency, and optimize the signal transmission quality in various complex environments, ensuring efficient and reliable communication between IoT devices. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to facilitate understanding by those skilled in the art, the present invention is further described below in conjunction with the accompanying drawings; Figure 1 This is a structural diagram of a 5G intelligent IoT communication system according to the present invention; Figure 2 The present invention is a flow chart of a 5G intelligent IoT communication control method. DETAILED DESCRIPTION

[0016] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. Example

[0017] Figure 1 A structural schematic diagram of a 5G intelligent IoT communication system based on the present invention is given, which specifically includes a tunable RF front-end module, a spectrum analysis module, a response analysis module, and a multi-level channel evaluation module, and signal connections between the modules; A tunable RF front-end module is used to communicate with receiving IoT devices at different levels by controlling the parameters of the antenna unit in the transmitting IoT device, where the different levels refer to the direction and the required frequency band of the receiving IoT device; The spectrum analysis module is used to determine the signal characteristics of communication signals at different levels through wavelet analysis based on the communication signals received by the IoT device, and to determine the complexity of the communication signals at each level by estimating the probability density function of the signal using cross entropy analysis to obtain the spectrum information of the communication signals.

[0018] The response analysis module is used to monitor and record the response time of communication signals in real time, train the ARMA model based on the response time data at different levels, and obtain the time deviation information of communication signals.

[0019] The multi-level channel assessment module is used to comprehensively analyze the spectrum information and time deviation information of communication signals at different levels, build a hierarchical channel assessment model, and quantify the performance of communication signals under different signals.

[0020] In the 5G smart IoT communication system, data exchange, control and communication between IoT devices are achieved through the collaboration of transmitting IoT devices and receiving IoT devices. The transmitting IoT device is equipped with multiple antenna units MIMO (multiple input multiple output) technology and beamforming technology. Multiple antenna units work together through MIMO technology to achieve parallel transmission of multiple data streams on the same spectrum, thereby greatly improving the signal transmission rate and system throughput. By precisely controlling the phase and amplitude of multiple antenna units, the signal is concentrated in a specific direction to enhance the signal transmission capability. By dynamically adjusting the configuration of the antenna unit, the direction of the beam can be precisely controlled to optimize the signal propagation path.

[0021] For receiving IoT devices (such as 5G receiving devices), the signal reception quality may show different characteristics at different levels. By analyzing the signal reception quality at these different levels and adjusting the parameters of the transmitting IoT device antenna unit, the communication quality and system performance can be optimized, including: Phase adjustment: By controlling the phase of the signal transmitted by each antenna unit, a specific beam direction can be formed, and the signal can be concentrated in the direction of the receiving device, thereby reducing interference from other directions; Amplitude adjustment: Adjusting the signal amplitude (gain) of each antenna element can enhance or reduce the signal strength in a specific direction. By dynamically adjusting the signal amplitude, the beam can be deepened or expanded. Beam directivity: By controlling the directivity of the antenna array, the signal can be focused on the area that needs to be covered. For example, when multiple receiving devices are distributed in different locations, the direction of the beam can be flexibly adjusted to maximize the received signal. Number of antenna arrays: Increasing the number of antenna arrays can improve the coverage and data throughput of the system. For example, using massive MIMO can greatly improve signal quality in a multi-user environment. Layout of antenna array: By optimizing the layout of the antenna array (such as the distance and orientation between adjacent units), the signal transmission characteristics can be improved, thereby improving the reliability of transmission.

[0022] It should be noted that in the 5G smart IoT communication system, the hierarchical division is to more accurately analyze and optimize signal transmission, reception quality and system performance. The hierarchical division can be divided from different perspectives, including: Tiers are divided by frequency: low-band (Sub-6 GHz), millimeter wave, and ultra-high frequency band; Divide levels by direction: Beamforming is achieved by controlling the phase and amplitude of different antenna units to focus signal transmission in a specific direction. The signal can be divided into multiple levels according to different directions and beam control.

[0023] In the 5G smart IoT communication system, signal transmission and reception are affected by many factors, such as channel conditions, device characteristics, user distribution, environmental noise, etc. Hierarchical division and evaluation of different levels are a key step in optimizing these factors, with the aim of improving signal quality, reducing interference, and enhancing the adaptability and responsiveness of the system, including: Signals may perform significantly differently in different frequency bands. For example, low-frequency signals (such as Sub-6GHz) may have stronger penetration and coverage than high-frequency signals (such as millimeter waves), but may be inferior in terms of high-speed data transmission and capacity. Signal strength and transmission quality will vary in different directions. Beamforming is achieved through antenna arrays, which can dynamically adjust the phase and amplitude of antenna units to focus the signal in the required direction and optimize transmission. In wireless communication systems, the state of the channel is constantly changing and may be affected by factors such as the environment, weather, obstacles, and interference from other devices. By dividing the signal into multiple levels, the channel state can be evaluated in detail to identify which frequency bands, directions, and time domains have the worst signal performance.

[0024] The spectrum analysis module is used to determine the signal characteristics of communication signals at different levels through wavelet analysis based on the communication signals received by the IoT device, and to determine the complexity of the communication signals at each level by estimating the probability density function of the signal using cross entropy analysis to obtain the spectrum information of the communication signals.

[0025] The spectrum information of the signal is represented by the signal energy anomaly coefficient and the signal complex variation coefficient.

[0026] After receiving the communication signal received by the IoT device, the signal characteristics of the communication signals at different levels are determined by wavelet analysis to obtain the signal energy anomaly coefficient. The signal energy anomaly coefficient is used to evaluate the performance of the signal at different levels. The advantages of the signal energy anomaly coefficient are: IoT devices (such as 5G receiving devices) receive communication signals from transmission devices. These signals contain information from multiple levels (such as different frequency bands and different directions). Since the signals are affected by noise, interference and attenuation during propagation, the quality of the signals may be uneven. Wavelet analysis is used to decompose the received signal into multiple frequency bands and time scales. Wavelet transform can not only analyze the frequency domain characteristics of the signal, but also provide time domain information, which makes it particularly suitable for processing time-varying signals; By calculating the signal energy anomaly coefficients at each level, the signal quality of different frequency bands and directions can be effectively evaluated, which helps to identify problems in signal transmission, such as interference and attenuation, and guides the transmission IoT devices (such as antenna units, RF modules, etc.) to make optimization adjustments; Unlike traditional Fourier transform, wavelet transform can provide information in both time domain and frequency domain at the same time. It is particularly suitable for analyzing signals with rapid changes in time and frequency. For dynamically changing signals (such as 5G communication signals), wavelet analysis can efficiently capture the local characteristics of the signal, which helps to accurately evaluate the signal quality at different levels (such as frequency bands, directions, etc.).

[0027] The acquisition logic of the signal energy anomaly coefficient is: collect the signal within the monitoring time period under the set level, perform wavelet transform on the received signal, and determine the waveform data of the signal under the level frequency band. The expression is: ;in, is the frequency domain waveform data of the signal in the i-th level frequency band, is the time domain waveform data of the signal in the monitoring period at the i-th level, is the scale, the translation parameter is the wavelet function of b, i=1, 2, 3, ..., I, I is a positive integer, i is the number of different levels; It should be noted that the monitoring period is a specific period of time set by staff in the professional field.

[0028] The frequency difference coefficient of the signal in the hierarchical frequency band is obtained, and the frequency difference coefficient of the signal in the hierarchical frequency band is marked as: ;in, , is the maximum frequency of the signal during the monitoring period, is the minimum frequency of the signal during the monitoring period; The energy ratio coefficient of the signal in the hierarchical frequency band is obtained, and the energy difference coefficient of the signal in the hierarchical frequency band is marked as: ;in, It is the time point from the minimum frequency to the maximum frequency of the signal during the monitoring period; It should be noted that the larger the frequency difference coefficient, the worse the signal performance, especially when the signal spectrum changes too complexly, the signal quality may be affected by factors such as noise and multipath effects, resulting in a decrease in communication quality. The larger the energy share coefficient, the more energy the signal has concentrated in the frequency band of interest, indicating that the signal in this frequency band is stronger and the communication quality is better. The lower the energy share coefficient, the more dispersed the signal energy distribution is, or the signal has strong interference in other frequency bands, resulting in lower signal energy in the target frequency band.

[0029] Calculate the signal energy anomaly coefficient, the calculation formula is: ;in, is the signal energy anomaly coefficient.

[0030] After receiving the communication signal received by the IoT device, the signal complex variation coefficient is determined by estimating the probability density function of the signal using cross entropy analysis. The signal complex variation coefficient is used to evaluate the stability and noise level of the transmitted signal to the received signal. The advantages of the signal complex variation coefficient are: The signal complex variation coefficient can effectively quantify the stability and change of the signal by analyzing the probability density difference between the transmitted signal and the received signal through cross entropy. If the signal changes greatly during the transmission process (such as being interfered or attenuated), the cross entropy is large, and the signal complex variation coefficient increases. This quantitative characteristic makes it possible to objectively evaluate the signal quality; By analyzing the difference between the received signal and the transmitted signal, the signal complex variation coefficient can reflect the noise, interference, attenuation and other factors in the system. When the noise level is high, the signal complex variation coefficient will be higher. Therefore, the signal quality and noise situation can be judged by this coefficient, so as to take corresponding remedial measures. The signal complex variation coefficient can be dynamically calculated over time, which helps to monitor the health of the communication system in real time, especially in dynamic environments (such as 5G networks and complex scenarios of IoT devices), and can reflect the real-time changes and stability of the signal; The complex variation coefficient calculated by cross entropy can help locate potential problems in signal transmission, such as attenuation, distortion, and multipath interference. This provides quantitative analysis support for network optimization, so that the root cause of the problem can be effectively diagnosed and corresponding adjustments can be made. In 5G and IoT communication systems, there may be a large number of different types of receiving devices. By comparing the signal quality of different receiving devices, the complex variation coefficient can provide targeted signal optimization suggestions for different devices, which helps the system perform load balancing, beamforming or other optimization operations.

[0031] The logic for obtaining the complex coefficient of variation of the signal is as follows: according to the signal in the monitoring time period at the set level, the frequency change of the signal received by the IoT device is obtained, and the probability density of the received signal frequency is determined using kernel density estimation, and the expression is: ;in, is the probability density of the received signal frequency, pl is the frequency value at different time points in the monitoring period, n=1, 2, 3, ..., N, N is a positive integer, n is the number of different time points in the monitoring period, h is the bandwidth, and K is the Gaussian kernel function; Obtain the frequency change of the transmission signal of the IoT device, and use kernel density estimation to determine the probability density of the transmission signal frequency. The expression is: ;in, is the probability density of the transmission signal frequency; Calculate the complex coefficient of variation, the calculation formula is: ;in, is the signal complexity coefficient of variation.

[0032] The response analysis module is used to monitor and record the response time of communication signals in real time, train the ARMA model based on the response time data at different levels, and obtain the time deviation information of communication signals.

[0033] The time deviation information of the communication signal is represented by the response time deviation coefficient.

[0034] The ARMA model is trained by response time data at different levels. The response time data at different levels are obtained by simulating the IoT system, network topology and device configuration through simulation tools, and the response time data of receiving devices at different levels are obtained. The ARMA model is trained to obtain ARMA models at different levels. The response time deviation coefficient is used to evaluate the delay performance of communication signal response at different levels. The advantages of the response time deviation coefficient are: The response time deviation coefficient reflects the fitting effect of the training model on the data, and measures the relative difference between the model's fitting ability on historical data and its prediction ability on new data (monitoring time period); By comparing the ratio of training residuals to monitoring residuals, the generalization ability of the model can be evaluated. A good model should be able to fit the training data well and make reasonable predictions for the future (or monitoring data). If the ratio is large, it may mean that the model is only suitable for training data and has poor prediction effect on new data. The response time deviation coefficient can reveal network instability, such as during high traffic periods, when the network is heavily loaded, or when there is signal attenuation due to environmental interference (such as weather, obstacles, etc.), which may cause the signal response time to become longer, thereby increasing the residual sum of squares.

[0035] The logic for obtaining the response time deviation coefficient is as follows: according to the response time data of IoT devices received at different levels, the ARMA model is used for training, the parameters of the ARMA model are optimized by the least squares method, and the error function of the ARMA model is obtained, which is expressed as follows: , is the error function of ARMA models at different levels, Predict the communication response time of receiving IoT devices for the ARMA model, is the actual response time of receiving the communication of the IoT device, m=1, 2, 3, ..., M, M is a positive integer, and m is the number of the response time data; The actual value of the response time of the IoT device received during the monitoring period is obtained, and the actual value of the response time of the IoT device received during the monitoring period is marked as: , where k=1, 2, 3, ..., K, K is a positive integer, k is the number of each response of the receiving IoT device, the trained ARMA model is used to predict the response time within the monitoring time period, and the predicted value of the response time of the receiving IoT device within the monitoring time period is obtained, and the predicted value of the response time of the receiving IoT device within the monitoring time period is marked as: ; Calculate the residual sum of squares of the response time of the receiving IoT device during the monitoring period. The calculation formula is: ;in, is the residual sum of squares of the response time of the IoT devices received during the monitoring period; Calculate the scale coefficient of the error function at different levels. The calculation formula is: ;in, is the scale coefficient of the error function at the same level, is the maximum value of the error function at different levels, is the minimum value of the error function at different levels; Calculate the response time deviation coefficient using the following formula: ;in, is the response time deviation coefficient.

[0036] The multi-level channel assessment module is used to comprehensively analyze the spectrum information and time deviation information of communication signals at different levels, build a hierarchical channel assessment model, and quantify the performance of communication signals under different signals.

[0037] Through comprehensive analysis of the spectrum information and time deviation information of communication signals at different levels, the signal energy anomaly coefficient, complex variation coefficient and response time deviation coefficient are weighted and calculated to construct a hierarchical channel assessment model and generate a hierarchical channel assessment coefficient. The calculation formula of the hierarchical channel assessment coefficient is: ;in, is the hierarchical channel estimation coefficient, are the proportional coefficients of signal energy anomaly coefficient, complex variation coefficient, and response time deviation coefficient, respectively. Both are greater than 0.

[0038] Set the hierarchical channel evaluation coefficient threshold, and compare the hierarchical channel evaluation coefficients of different levels with the hierarchical channel evaluation coefficient threshold. If the hierarchical channel evaluation coefficient is greater than the hierarchical channel evaluation coefficient threshold, a warning signal is generated, indicating that the communication quality of the level is poor, and it may be necessary to adjust the configuration of the antenna unit, control the direction of the beam, and optimize the communication of the signal. If the hierarchical channel evaluation coefficient is less than the hierarchical channel evaluation coefficient threshold, no warning signal is generated, indicating that the communication quality of the current level is good, and there is no need to adjust the parameters of the antenna unit in the transmission IoT device through the tunable RF front-end module.

[0039] The present invention comprehensively analyzes the signal spectrum information, response time and its changes at different levels, monitors and optimizes the communication quality in real time, evaluates the signal using wavelet analysis, cross entropy and ARMA models, and then adjusts the configuration and beam direction of the antenna unit to achieve dynamic optimization of different directions and frequency bands, and quantifies the performance of communication signals at each level. The present invention can improve the stability of the communication system, reduce latency, and optimize the signal transmission quality in various complex environments, ensuring efficient and reliable communication between IoT devices.

[0040] Example 2 like Figure 2 The present invention provides a 5G intelligent IoT communication control method, which specifically includes the following steps: S1: The receiving IoT device receives the communication signal from the transmission device, performs time-frequency conversion on the signal using wavelet analysis, extracts the signal characteristics in different frequency bands and directions, estimates the probability density of the signal through cross entropy analysis, evaluates the complexity of the signal, and obtains the spectrum information of the communication signal; S2: Monitor and record the response time of each communication in real time, train the ARMA model based on historical response time data, and use the model to predict the response time of different levels and compare it with the actual response time to obtain time deviation information; S3: Combines spectrum information and time deviation information, analyzes signal performance at different levels, and evaluates signal quality at different levels; S4: Adjust the communication parameters of IoT devices based on the hierarchical evaluation results.

[0041] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0042] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0043] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0044] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0045] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0046] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0047] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0048] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A 5G intelligent IoT communication system, characterized in that: Specifically, it includes a tunable RF front-end module, a spectrum analysis module, a response analysis module, and a multi-level channel evaluation module, and signal connections between the modules; A tunable RF front-end module is used to communicate with receiving IoT devices at different levels by controlling the parameters of the antenna unit in the transmitting IoT device, where the different levels refer to the direction and the required frequency band of the receiving IoT device; The spectrum analysis module is used to determine the signal characteristics of communication signals at different levels through wavelet analysis based on the communication signals received by the IoT device, and to determine the complexity of the communication signals at each level by estimating the probability density function of the signals using cross entropy analysis to obtain the spectrum information of the communication signals; The response analysis module is used to monitor and record the response time of communication signals in real time, train the ARMA model based on the response time data at different levels, and obtain the time deviation information of communication signals; The multi-level channel assessment module is used to comprehensively analyze the spectrum information and time deviation information of communication signals at different levels, build a hierarchical channel assessment model, and quantify the performance of communication signals under different signals.

2. According to claim 1, a 5G intelligent IoT communication system is characterized in that: Obtain spectrum information of communication signals, including: The spectrum information of the signal is represented by the signal energy anomaly coefficient and the signal complex variation coefficient; The acquisition logic of the signal energy anomaly coefficient is: collect the signal within the monitoring time period under the set level, perform wavelet transform on the received signal, and determine the waveform data of the signal under the level frequency band. The expression is: ;in, is the frequency domain waveform data of the signal in the i-th level frequency band, is the time domain waveform data of the signal in the monitoring period at the i-th level, is the scale, the translation parameter is the wavelet function of b, i=1, 2, 3, ..., I, I is a positive integer, i is the number of different levels; The frequency difference coefficient of the signal in the hierarchical frequency band is obtained, and the frequency difference coefficient of the signal in the hierarchical frequency band is marked as: ;in, , is the maximum frequency of the signal during the monitoring period, is the minimum frequency of the signal during the monitoring period; The energy ratio coefficient of the signal in the hierarchical frequency band is obtained, and the energy difference coefficient of the signal in the hierarchical frequency band is marked as: ;in, It is the time point from the minimum frequency to the maximum frequency of the signal during the monitoring period; Calculate the signal energy anomaly coefficient, the calculation formula is: ;in, is the signal energy anomaly coefficient.

3. A 5G intelligent IoT communication system according to claim 2, characterized in that: Signal complex coefficient of variation, including: The logic for obtaining the complex coefficient of variation of the signal is as follows: according to the signal in the monitoring time period at the set level, the frequency change of the signal received by the IoT device is obtained, and the probability density of the received signal frequency is determined using kernel density estimation, and the expression is: ;in, is the probability density of the received signal frequency, pl is the frequency value at different time points in the monitoring period, n=1, 2, 3, ..., N, N is a positive integer, n is the number of different time points in the monitoring period, h is the bandwidth, and K is the Gaussian kernel function; Obtain the frequency change of the transmission signal of the IoT device, and use kernel density estimation to determine the probability density of the transmission signal frequency. The expression is: ;in, is the probability density of the transmission signal frequency; Calculate the complex coefficient of variation, the calculation formula is: ;in, is the signal complexity coefficient of variation.

4. A 5G intelligent IoT communication system according to claim 3, characterized in that: Obtain the time deviation information of the communication signal, including: The time deviation information of the communication signal is represented by a response time deviation coefficient; The logic for obtaining the response time deviation coefficient is as follows: according to the response time data of IoT devices received at different levels, the ARMA model is used for training, the parameters of the ARMA model are optimized by the least squares method, and the error function of the ARMA model is obtained, which is expressed as follows: , is the error function of ARMA models at different levels, Predict the communication response time of receiving IoT devices for the ARMA model, is the actual response time of receiving the communication of the IoT device, m=1, 2, 3, ..., M, M is a positive integer, and m is the number of the response time data; The actual value of the response time of the IoT device received during the monitoring period is obtained, and the actual value of the response time of the IoT device received during the monitoring period is marked as: , where k=1, 2, 3, ..., K, K is a positive integer, k is the number of each response of the receiving IoT device, the trained ARMA model is used to predict the response time within the monitoring time period, and the predicted value of the response time of the receiving IoT device within the monitoring time period is obtained, and the predicted value of the response time of the receiving IoT device within the monitoring time period is marked as: ; Calculate the residual sum of squares of the response time of the receiving IoT device during the monitoring period. The calculation formula is: ;in, is the residual sum of squares of the response time of the IoT devices received during the monitoring period; Calculate the scale coefficient of the error function at different levels. The calculation formula is: ;in, is the scale coefficient of the error function at the same level, is the maximum value of the error function at different levels, is the minimum value of the error function at different levels; Calculate the response time deviation coefficient using the following formula: ;in, is the response time deviation coefficient.

5. A 5G intelligent IoT communication system according to claim 4, characterized in that: Construct a hierarchical channel assessment model, including: Through comprehensive analysis of the spectrum information and time deviation information of communication signals at different levels, the signal energy anomaly coefficient, complex variation coefficient and response time deviation coefficient are weighted and calculated to construct a hierarchical channel assessment model and generate a hierarchical channel assessment coefficient. The calculation formula of the hierarchical channel assessment coefficient is: ;in, is the hierarchical channel estimation coefficient, are the proportional coefficients of signal energy anomaly coefficient, complex variation coefficient, and response time deviation coefficient, respectively. Both are greater than 0.

6. A 5G intelligent IoT communication system according to claim 5, characterized in that: Quantify the performance of communication signals under different signals, including: A hierarchical channel assessment coefficient threshold is set, and the hierarchical channel assessment coefficients of different levels are compared with the hierarchical channel assessment coefficient threshold. If the hierarchical channel assessment coefficient is greater than the hierarchical channel assessment coefficient threshold, a warning signal is generated; if the hierarchical channel assessment coefficient is less than the hierarchical channel assessment coefficient threshold, no warning signal is generated.

7. A 5G smart IoT communication control method, used to implement a 5G smart IoT communication system according to any one of claims 1 to 6, characterized in that: The specific steps include: S1: The receiving IoT device receives the communication signal from the transmission device, performs time-frequency conversion on the signal using wavelet analysis, extracts the signal characteristics in different frequency bands and directions, estimates the probability density of the signal through cross entropy analysis, evaluates the complexity of the signal, and obtains the spectrum information of the communication signal; S2: Monitor and record the response time of each communication in real time, train the ARMA model based on historical response time data, and use the model to predict the response time of different levels and compare it with the actual response time to obtain time deviation information; S3: Combines spectrum information and time deviation information, analyzes signal performance at different levels, and evaluates signal quality at different levels; S4: Adjust the communication parameters of IoT devices based on the hierarchical evaluation results.

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