Internet of Things communication method and system, terminal and storage medium

Abstract

The invention relates to an Internet of Things communication method and system, a terminal and a storage medium. The method comprises the following steps: acquiring communication parameters and equipment moving states of Internet of Things equipment in real time; inputting the communication parameters and the equipment moving state of the Internet of Things equipment into a pre-selected trained communication model for comparative analysis, and predicting to obtain a communication environment state at the next moment; analyzing the communication environment state at the next moment by using an adaptive algorithm, and determining a communication protocol and a data sending rule of the Internet of Things at the next moment; and according to the communication protocol and the data sending rule of the Internet of Things at the next moment, correspondingly adjusting the Internet of Things equipment. The effect of improving the communication efficiency of the Internet of Things is achieved.

Description

Technical Field

[0001] The present application relates to the technical field of Internet of Things communications, and in particular to an Internet of Things communication method, system, terminal and storage medium. Background Art

[0002] At present, with the rapid development of Internet of Things technology, the communication needs between various smart devices are growing rapidly. At present, Internet of Things communication usually adopts preset communication protocols and fixed sending rules. Although this method is stable, it is not flexible enough in the face of complex and changeable communication scenarios, and the communication efficiency is limited. Existing solutions mostly rely on statically configured device communication parameters and cannot be dynamically adjusted according to actual conditions, resulting in waste or insufficient communication resources in some scenarios.

[0003] The above-mentioned existing technical solutions have the following defects: the existing Internet of Things communication technology has poor adaptability to dynamic communication environments and low communication efficiency, so there is room for improvement. Summary of the invention

[0004] In order to improve the communication efficiency of the Internet of Things, the present application provides an Internet of Things communication method, system, terminal and storage medium.

[0005] The above-mentioned invention objective of the present application is achieved through the following technical solutions: An Internet of Things communication method, the Internet of Things communication method comprising: Obtain the communication parameters and device movement status of IoT devices in real time; The communication parameters and the mobile state of the IoT device are input into a pre-selected and trained communication model for comparison and analysis, and the communication environment state at the next moment is predicted; Analyze the communication environment state at the next moment by using an adaptive algorithm to determine the communication protocol and data transmission rules of the Internet of Things at the next moment; According to the communication protocol and data transmission rules of the Internet of Things at the next moment, the Internet of Things devices are adjusted accordingly.

[0006] By adopting the above technical solution, the communication parameters and device mobility status of the Internet of Things devices are acquired in real time, so as to realize the real-time monitoring of the current communication quality and mobility characteristics of the Internet of Things devices, and provide real-time data support for dynamic communication management; by inputting the communication parameters and device mobility status of the Internet of Things devices into the pre-selected and trained communication model for comparison and analysis, the communication environment state at the next moment is predicted, the device communication trend is predicted, and forward-looking information is provided for communication decision-making, thereby enhancing the prediction ability and response speed of the system; by using an adaptive algorithm to analyze the communication environment state at the next moment, the communication protocol and data sending rules of the Internet of Things at the next moment are determined, and the communication strategy is dynamically adjusted according to the real-time predicted communication environment, the communication protocol and data transmission rules are optimized, and the utilization rate of network resources and the reliability of data transmission are improved; by making corresponding adjustments to the Internet of Things devices according to the communication protocol and data sending rules of the Internet of Things at the next moment, the communication settings of the Internet of Things devices are adaptively adjusted, ensuring that the devices maintain the best communication performance in a changing environment, and enhancing the stability and flexibility of the system.

[0007] In a preferred example, the present application can be further configured as follows: the real-time acquisition of the communication parameters and device movement status of the IoT device includes: Acquire current communication parameters of the IoT device, wherein the communication parameters include signal strength, bandwidth utilization, delay, packet loss rate, and throughput; The current movement state of the IoT device is obtained, where the movement state includes position, speed, direction, acceleration, and movement trajectory.

[0008] By adopting the above technical solution, by obtaining the current communication parameters of the IoT device, it is possible to understand in detail the specific performance of the device in real-time communication, thereby providing an accurate data basis for subsequent optimization; by obtaining the current mobile state of the IoT device, the movement trajectory and movement characteristics of the device can be grasped, providing dynamic data support for communication optimization; by combining these real-time data, it is possible to better evaluate and predict the communication performance of the device in different environments, thereby improving the accuracy of model prediction and the timeliness of adjustment.

[0009] In a preferred example, the present application may be further configured as follows: before inputting the communication parameters and the device movement state of the IoT device into a pre-selected trained communication model for comparison and analysis, and predicting the communication environment state at the next moment, obtaining the pre-trained communication model includes: Acquire historical communication parameters of the IoT device in different environments and mobile states, and preprocess the historical communication parameters of the IoT device in different environments and mobile states to obtain a training set; Building a communication model based on a convolutional neural network, and performing forward propagation and back-propagation training on the communication model using the training set to obtain a communication model after forward propagation and back-propagation training; The model parameters of the communication model trained by forward propagation and back propagation are adjusted using a genetic algorithm to obtain the pre-trained communication model.

[0010] By adopting the above technical solution, by obtaining the historical communication parameters of the IoT device in different environments and mobile states and preprocessing the historical communication parameters, a high-quality training set can be formed to provide a basis for model training; by building a communication model based on a convolutional neural network and using the training set to perform forward propagation and back propagation training on the model, the model's ability to recognize complex data patterns can be improved, thereby improving the accuracy of communication status prediction; by using a genetic algorithm to optimize the parameters of the trained communication model, the model's generalization ability and prediction accuracy can be further improved.

[0011] In a preferred example, the present application can be further configured as follows: the communication parameters and the mobile state of the IoT device are input into a pre-selected and trained communication model for comparison and analysis, and the communication environment state at the next moment is predicted to include: Performing data enhancement processing on the communication parameters of the IoT device to obtain enhanced communication parameters; Extract features from the enhanced communication parameters and the device movement state to obtain feature values ​​of the IoT device; After inputting the characteristic value of the IoT device into a pre-selected and trained communication model for comparison and analysis, the communication environment state at the next moment is predicted.

[0012] By adopting the above technical scheme, by performing data enhancement processing on the communication parameters of the Internet of Things device, the diversity and robustness of the data can be increased, and the adaptability of the model to different situations can be improved; by extracting features from the enhanced communication parameters and the mobile state of the device, the key features can be captured, the input data of the model can be simplified, and the prediction efficiency of the model can be improved; by inputting the extracted feature values ​​into a pre-selected and trained communication model for comparison and analysis, the communication environment state at the next moment can be predicted, and changes in the communication environment can be predicted in advance, providing a basis for adjusting the communication strategy, thereby improving the stability and reliability of communication.

[0013] In a preferred example, the present application can be further configured as follows: the use of an adaptive algorithm to analyze the communication environment state at the next moment to determine the communication protocol and data transmission rules of the Internet of Things at the next moment includes: The Markov decision process is used to analyze the time series of the communication environment state at the next moment, and the reinforcement learning algorithm is used to adjust the selection of the communication protocol to obtain the communication protocol and data sending rules of the Internet of Things at the next moment.

[0014] By adopting the above technical solution, by using the Markov decision process to analyze the time series of the communication environment state at the next moment, it is possible to identify the changing pattern of the environmental state and provide data support for the selection of the communication protocol; by using the reinforcement learning algorithm to adjust the selection of the communication protocol, it is possible to adaptively select the best communication protocol and data sending rules in a dynamic environment, thereby improving the efficiency and stability of communication.

[0015] In a preferred example, the present application can be further configured as follows: the IoT communication method further includes: Using simulation testing to verify the communication protocol and data transmission rules of the Internet of Things at the next moment, and obtain a verification result; According to the verification result, adjusting the parameters of the pre-trained communication model and the adaptive algorithm; When an abnormality occurs in the adjustment of the IoT device, the communication parameters of the IoT device are adjusted to a preset safe state, and an alarm signal is generated and transmitted to the user end.

[0016] By adopting the above technical solution, by using simulation tests to verify the communication protocol and data sending rules of the Internet of Things at the next moment, it is possible to evaluate its effectiveness and reliability before actual application to ensure the application effect; by adjusting the parameters of the pre-trained communication model and the adaptive algorithm according to the verification results, it is possible to continuously optimize the model and algorithm to improve its adaptability and prediction accuracy; by adjusting the communication parameters of the Internet of Things device to a preset safe state when an abnormality occurs in the adjustment of the Internet of Things device, and generating an alarm signal to transmit to the user end, it is possible to take timely measures when problems occur to ensure the safety and normal operation of the equipment, and improve the user's trust and usage experience.

[0017] The second object of the invention is achieved by the following technical solutions: An Internet of Things communication system, the Internet of Things communication system comprising: Real-time monitoring module, used to obtain the communication parameters and device movement status of IoT devices in real time; A prediction module is used to input the communication parameters and device movement status of the IoT device into a pre-selected and trained communication model for comparison and analysis, and predict the communication environment status at the next moment; Determine an output module, which is used to analyze the communication environment state at the next moment by using an adaptive algorithm to determine the communication protocol and data transmission rules of the Internet of Things at the next moment; The adjustment module is used to adjust the Internet of Things device accordingly according to the communication protocol and data transmission rules of the Internet of Things at the next moment.

[0018] By adopting the above technical solution, the communication parameters and device mobility status of the Internet of Things devices are acquired in real time, so as to realize the real-time monitoring of the current communication quality and mobility characteristics of the Internet of Things devices, and provide real-time data support for dynamic communication management; by inputting the communication parameters and device mobility status of the Internet of Things devices into the pre-selected and trained communication model for comparison and analysis, the communication environment state at the next moment is predicted, the device communication trend is predicted, and forward-looking information is provided for communication decision-making, thereby enhancing the prediction ability and response speed of the system; by using an adaptive algorithm to analyze the communication environment state at the next moment, the communication protocol and data sending rules of the Internet of Things at the next moment are determined, and the communication strategy is dynamically adjusted according to the real-time predicted communication environment, the communication protocol and data transmission rules are optimized, and the utilization rate of network resources and the reliability of data transmission are improved; by making corresponding adjustments to the Internet of Things devices according to the communication protocol and data sending rules of the Internet of Things at the next moment, the communication settings of the Internet of Things devices are adaptively adjusted, ensuring that the devices maintain the best communication performance in a changing environment, and enhancing the stability and flexibility of the system.

[0019] The third objective of the present application is achieved through the following technical solutions: A computer terminal comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned Internet of Things communication method when executing the computer program.

[0020] The fourth objective of the present application is achieved through the following technical solutions: A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned Internet of Things communication method are implemented.

[0021] In summary, the present application includes at least one of the following beneficial technical effects: 1. By adopting the above technical solution, the communication parameters and device mobility status of the IoT device are obtained in real time, so as to realize the real-time monitoring of the current communication quality and mobility characteristics of the IoT device, and provide real-time data support for dynamic communication management; by inputting the communication parameters and device mobility status of the IoT device into the pre-selected and trained communication model for comparison and analysis, the communication environment status at the next moment is predicted, the device communication trend is predicted, and forward-looking information is provided for communication decision-making, thereby enhancing the prediction ability and response speed of the system; 2. By using an adaptive algorithm to analyze the communication environment state at the next moment, the communication protocol and data transmission rules of the Internet of Things at the next moment are determined, and the communication strategy is dynamically adjusted according to the real-time predicted communication environment, thereby optimizing the communication protocol and data transmission rules, and improving the utilization rate of network resources and the reliability of data transmission; by making corresponding adjustments to the Internet of Things devices according to the communication protocol and data transmission rules of the Internet of Things at the next moment, adaptive adjustment of the communication settings of the Internet of Things devices is achieved, ensuring that the devices maintain the best communication performance in a changing environment, and enhancing the stability and flexibility of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 This is a flow chart of an Internet of Things communication method in one embodiment of the present application; Figure 2 This is a flowchart for implementing step S10 in an Internet of Things communication method in one embodiment of the present application; Figure 3 This is a flowchart for implementing step S20 in an Internet of Things communication method in an embodiment of the present application; Figure 4 This is a flowchart for implementing step S20 in an Internet of Things communication method in one embodiment of the present application; Figure 5 This is a flowchart for implementing step S30 in an Internet of Things communication method in one embodiment of the present application; Figure 6 This is an implementation flow chart of an Internet of Things communication method in one embodiment of the present application; Figure 7 This is a principle block diagram of an Internet of Things communication system in one embodiment of the present application; Figure 8 It is a schematic diagram of a terminal in an embodiment of the present application. DETAILED DESCRIPTION

[0023] The present application is further described in detail below in conjunction with the accompanying drawings.

[0024] In one embodiment, if Figure 1 As shown, the present application discloses an Internet of Things communication method, which specifically includes the following steps: S10: Obtain the communication parameters and device movement status of IoT devices in real time.

[0025] Specifically, in order to ensure that IoT devices can communicate effectively in a constantly changing environment, it is first necessary to collect communication parameters such as signal strength, signal-to-noise ratio, transmission rate, etc., as well as the movement status of the device, such as position, speed and acceleration, in real time.

[0026] S20: Input the communication parameters and device movement status of the IoT device into a pre-selected and trained communication model for comparison and analysis, and predict the communication environment status at the next moment.

[0027] Specifically, the communication parameters and device mobility status of the IoT device are input into a pre-trained communication model, and the communication model is used to compare and analyze the input data. Based on the mapping relationship between the learned features and the environmental status, inference is made to predict the communication environment status at the next moment, which includes the signal change trend, possible network congestion, etc., in order to predict the communication environment status at the next moment.

[0028] S30: Analyze the communication environment state at the next moment using an adaptive algorithm to determine the communication protocol and data transmission rules of the Internet of Things at the next moment.

[0029] Specifically, the communication environment state predicted by the model is used as input and analyzed using an adaptive algorithm. The algorithm can dynamically select the optimal communication protocol and data sending rules based on the predicted state and historical optimization experience. The adaptive algorithm determines which communication protocol is most suitable under the predicted communication environment and how the data should be sent, including data packet size, transmission frequency, priority setting, etc.

[0030] S40: According to the communication protocol and data transmission rules of the Internet of Things at the next moment, the Internet of Things devices are adjusted accordingly.

[0031] Specifically, according to the communication protocol and data sending rules determined by the adaptive algorithm, the communication parameters of the IoT device are adjusted in real time, which may include adjusting the transmission power, changing the transmission rate, optimizing the routing path, etc.

[0032] By adopting the above technical solution, the communication parameters and device mobility status of the Internet of Things devices are acquired in real time, so as to realize the real-time monitoring of the current communication quality and mobility characteristics of the Internet of Things devices, and provide real-time data support for dynamic communication management; by inputting the communication parameters and device mobility status of the Internet of Things devices into the pre-selected and trained communication model for comparison and analysis, the communication environment state at the next moment is predicted, the device communication trend is predicted, and forward-looking information is provided for communication decision-making, thereby enhancing the prediction ability and response speed of the system; by using an adaptive algorithm to analyze the communication environment state at the next moment, the communication protocol and data sending rules of the Internet of Things at the next moment are determined, and the communication strategy is dynamically adjusted according to the real-time predicted communication environment, the communication protocol and data transmission rules are optimized, and the utilization rate of network resources and the reliability of data transmission are improved; by making corresponding adjustments to the Internet of Things devices according to the communication protocol and data sending rules of the Internet of Things at the next moment, the communication settings of the Internet of Things devices are adaptively adjusted, ensuring that the devices maintain the best communication performance in a changing environment, and enhancing the stability and flexibility of the system.

[0033] In one embodiment, if Figure 2 As shown, in step S10, the communication parameters and device movement status of the IoT device are obtained in real time, specifically including: S11: Obtain the current communication parameters of the IoT device, including signal strength, bandwidth utilization, delay, packet loss rate, and throughput.

[0034] Specifically, the current communication parameters of the IoT device are obtained, and the communication parameters include signal strength, bandwidth utilization, delay, packet loss rate, and throughput.

[0035] S12: Obtain the current movement state of the IoT device, which includes position, speed, direction, acceleration, and movement trajectory.

[0036] Specifically, the movement of IoT devices will affect the strength of communication signals, the stability of connections, and the utilization of network resources. Therefore, by obtaining the current movement status of IoT devices, the movement status includes position, speed, direction, acceleration, and movement trajectory, in order to more accurately determine the status of IoT devices.

[0037] In one embodiment, if Figure 3 As shown, before step S20, that is, before inputting the communication parameters and the device movement state of the IoT device into the pre-selected trained communication model for comparison and analysis, and predicting the communication environment state at the next moment, obtaining the pre-trained communication model specifically includes: S201: Obtain historical communication parameters of the IoT device in different environments and mobile states, and preprocess the historical communication parameters of the IoT device in different environments and mobile states to obtain a training set.

[0038] Specifically, historical communication parameter data in different environments and mobile states are collected from IoT devices. These data may include signal strength, bandwidth utilization, latency, packet loss rate, throughput, etc., and the collected historical data are preprocessed to prepare for model training. The preprocessing steps may include: data cleaning: removing invalid or erroneous data records; feature engineering: extracting useful features from raw data, or constructing new features; data standardization: scaling the data to a uniform range, such as 0 to 1, or performing z-score standardization; data set partitioning: dividing the data into training set, validation set, and test set, and finally obtaining a training set for training the model.

[0039] S202: construct a communication model based on a convolutional neural network, and perform forward propagation and back-propagation training on the communication model using a training set to obtain a communication model after forward propagation and back-propagation training.

[0040] Specifically, a communication model is constructed based on a convolutional neural network, and the communication model is forward propagated and trained using a training set, that is, the data is forward propagated through the CNN model, the model makes predictions based on the input data, and generates outputs through activation functions, and uses loss functions such as mean square error or cross entropy to calculate the difference between the model's predicted output and the actual value as an indicator for evaluating the performance of the model. There is also back-propagation training, that is, using the gradient information of the loss function to update the model's weights and bias parameters through a back-propagation algorithm to reduce the prediction error, and finally obtaining a communication model after forward and back-propagation training.

[0041] S203: Using a genetic algorithm to adjust the model parameters of the communication model trained by forward propagation and back propagation to obtain a pre-trained communication model.

[0042] Specifically, a genetic algorithm is used to further adjust the parameters of the convolutional neural network model. The genetic algorithm is an optimization algorithm that simulates the principles of natural selection and genetics. It optimizes the solution to the problem by simulating crossover, mutation, and selection processes. The genetic algorithm searches for the optimal solution in the model parameter space, which may involve the adjustment of the network structure, the selection of hyperparameters, etc., to improve the performance of the model. After forward and back propagation training, and parameter adjustment of the genetic algorithm, a pre-trained communication model is obtained, which can accurately predict the communication performance of IoT devices.

[0043] In one embodiment, if Figure 4As shown, in step S20, the communication parameters and the device movement status of the IoT device are input into the pre-selected trained communication model for comparison and analysis, and the communication environment status at the next moment is predicted, which specifically includes: S21: Perform data enhancement processing on the communication parameters of the IoT device to obtain enhanced communication parameters.

[0044] Specifically, in order to enhance the diversity of original data, improve the generalization ability of the model and reduce overfitting, the communication parameters of IoT devices are subjected to data enhancement processing, including data perturbation, noise addition, time series dislocation, speed change simulation and other technologies to generate more training samples and finally obtain enhanced communication parameters.

[0045] S22: Extract features from the enhanced communication parameters and device mobility status to obtain feature values ​​of the IoT device.

[0046] Specifically, in order to extract abstract information that is helpful for model understanding from the enhanced data, feature extraction is performed from the enhanced communication parameters and device mobility status, including statistical analysis such as mean, variance, time domain analysis such as time delay, trend, frequency domain analysis such as power spectral density, etc., to obtain the characteristic values ​​of IoT devices.

[0047] S23: After inputting the characteristic values ​​of the IoT devices into the pre-selected and trained communication model for comparison and analysis, the communication environment state at the next moment is predicted.

[0048] Specifically, the integrated feature values ​​are input into a pre-trained communication model, which has learned the relationship between communication parameters and mobile status and communication environment through historical data. After receiving the input feature values, the model performs comparative analysis and uses its internally learned patterns and rules to process the data. Based on the comparative analysis, the model predicts the communication environment status of the IoT device at the next moment, which may include indicators such as signal quality, network delay, and bandwidth utilization.

[0049] In one embodiment, if Figure 5 As shown, in step S30, the communication environment state at the next moment is analyzed by using an adaptive algorithm to determine the communication protocol and data transmission rules of the Internet of Things at the next moment, which specifically includes: S31: Use the Markov decision process to analyze the time series of the communication environment state at the next moment, and use the reinforcement learning algorithm to adjust the selection of the communication protocol to obtain the communication protocol and data sending rules of the Internet of Things at the next moment.

[0050] Specifically, the Markov decision process is used to analyze the time series of the communication environment state at the next moment, and the reinforcement learning algorithm is used to adjust the selection of the communication protocol. The reinforcement learning algorithm selects the most appropriate communication protocol based on the current communication environment state and feedback from historical actions. For example, a more interference-resistant protocol is selected in an environment with weak signals, and ultimately the optimal communication protocol and data sending rules for the Internet of Things at the next moment are obtained.

[0051] In one embodiment, if Figure 6 As shown, the Internet of Things communication method also includes: S51: Use simulation testing to verify the communication protocol and data sending rules of the Internet of Things at the next moment to obtain a verification result.

[0052] Specifically, before actual deployment, simulation tests are performed to simulate the communication performance of IoT devices under different conditions, verify the effectiveness of communication protocols and data sending rules, use software tools or customized simulation environments to simulate device communication and mobility, and test selected communication protocols and data sending rules. After the simulation test is completed, the test result data is collected to evaluate communication performance indicators such as latency, throughput, and packet loss rate.

[0053] S52: According to the verification result, adjust the parameters of the pre-trained communication model and adaptive algorithm.

[0054] Specifically, based on the test results, the pre-trained communication model is fine-tuned to optimize model parameters, improve prediction accuracy, and adjust the parameters of the adaptive algorithm, such as learning rate, exploration rate, etc., to better adapt to the actual communication environment.

[0055] S53: When an abnormality occurs during adjustment of the IoT device, the communication parameters of the IoT device are adjusted to a preset safe state, and an alarm signal is generated and transmitted to the user end.

[0056] Specifically, if in actual applications, the communication performance of IoT devices is abnormal, such as inaccurate predictions or degraded communication quality, the system will take measures to protect the devices and networks and adjust the communication parameters of IoT devices to a preset safe state, which may include reducing the data transmission rate, switching to a more stable communication protocol, adding a retransmission mechanism, etc. When an abnormal situation is detected, the system generates an alarm signal and transmits the alarm information to the user end through appropriate communication channels such as email, SMS or application notifications. After receiving the alarm signal, the user can take appropriate manual intervention measures according to the situation, such as further adjusting the device settings, checking the device status or contacting technical support.

[0057] It should be understood that the size of the serial numbers of the steps in the above embodiments 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.

[0058] In one embodiment, an Internet of Things communication system is provided, and the Internet of Things communication system corresponds one-to-one to an Internet of Things communication method in the above embodiment. Figure 7 As shown, the IoT communication system includes a real-time monitoring module, a prediction module, an output determination module and an adjustment module. The functional modules are described in detail as follows: Real-time monitoring module, used to obtain the communication parameters and device movement status of IoT devices in real time; The prediction module is used to input the communication parameters and device movement status of the IoT device into the pre-selected and trained communication model for comparison and analysis, and predict the communication environment status at the next moment; Determine an output module, which is used to analyze the communication environment state at the next moment by using an adaptive algorithm, and determine the communication protocol and data transmission rules of the Internet of Things at the next moment; The adjustment module is used to make corresponding adjustments to the IoT devices according to the communication protocol and data transmission rules of the IoT at the next moment.

[0059] Optionally, the IoT communication system further includes: The verification module is used to verify the communication protocol and data transmission rules of the Internet of Things at the next moment by using simulation testing to obtain verification results; A parameter adjustment module, used to adjust the parameters of the pre-trained communication model and adaptive algorithm according to the verification results; The alarm module is used to adjust the communication parameters of the IoT device to a preset safe state when an abnormality occurs in the adjustment of the IoT device, and generate an alarm signal to transmit to the user end.

[0060] A training set acquisition module is used to acquire historical communication parameters of IoT devices in different environments and mobile states, and pre-process the historical communication parameters of IoT devices in different environments and mobile states to obtain a training set; A model training module is used to build a communication model based on a convolutional neural network, and to perform forward propagation and back propagation training on the communication model using a training set to obtain a communication model after forward propagation and back propagation training; The model optimization module is used to adjust the model parameters of the communication model after forward propagation and back propagation training using a genetic algorithm to obtain a pre-trained communication model.

[0061] Optional real-time monitoring modules include: The communication parameter acquisition submodule is used to obtain the current communication parameters of the IoT device, including signal strength, bandwidth utilization, delay, packet loss rate, and throughput; The mobile status acquisition submodule is used to obtain the current mobile status of the IoT device. The mobile status includes position, speed, direction, acceleration and movement trajectory.

[0062] Optionally, the prediction module includes: The enhancement submodule is used to perform data enhancement processing on the communication parameters of the IoT device to obtain enhanced communication parameters; The feature extraction submodule is used to extract features from the enhanced communication parameters and device mobility status to obtain the feature values ​​of the IoT devices; The comparison submodule is used to input the characteristic values ​​of the IoT devices into the pre-selected and trained communication model for comparison and analysis, and then predict the communication environment state at the next moment.

[0063] Optionally, determining the output module includes: The analysis submodule is used to analyze the time series of the communication environment state at the next moment using the Markov decision process, and to adjust the selection of the communication protocol using the reinforcement learning algorithm to obtain the communication protocol and data sending rules of the Internet of Things at the next moment.

[0064] For the specific definition of an IoT communication system, please refer to the definition of an IoT communication method above, which will not be repeated here. Each module in the above IoT communication system can be implemented in whole or in part by software, hardware and a combination thereof. The above modules can be embedded in or independent of the processor in the computer terminal in hardware form, or stored in the memory in the computer terminal in software form, so that the processor can call and execute the operations corresponding to the above modules.

[0065] In one embodiment, a computer terminal is provided. The computer terminal may be a server, and its internal structure diagram may be as follows: Figure 8 As shown. The computer terminal includes a processor, a memory, a network interface and a database connected through a system bus. Among them, the processor of the computer terminal is used to provide computing and control capabilities. The memory of the computer terminal includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer terminal is used to communicate with external devices through a network connection. When the computer program is executed by the processor, an Internet of Things communication method is implemented.

[0066] In one embodiment, a computer terminal is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the following steps when executing the computer program: Obtain the communication parameters and device movement status of IoT devices in real time; The communication parameters and the mobile state of the IoT device are input into a pre-selected and trained communication model for comparison and analysis, and the communication environment state at the next moment is predicted; Analyze the communication environment state at the next moment by using an adaptive algorithm to determine the communication protocol and data transmission rules of the Internet of Things at the next moment; According to the communication protocol and data transmission rules of the Internet of Things at the next moment, the Internet of Things devices are adjusted accordingly.

[0067] In one embodiment, a computer readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented: Obtain the communication parameters and device movement status of IoT devices in real time; The communication parameters and the mobile state of the IoT device are input into a pre-selected and trained communication model for comparison and analysis, and the communication environment state at the next moment is predicted; Analyze the communication environment state at the next moment by using an adaptive algorithm to determine the communication protocol and data transmission rules of the Internet of Things at the next moment; According to the communication protocol and data transmission rules of the Internet of Things at the next moment, the Internet of Things devices are adjusted accordingly.

[0068] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0069] Those skilled in the art will clearly understand that for the sake of convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the system can be divided into different functional units or modules to complete all or part of the functions described above.

[0070] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. An Internet of Things communication method, characterized in that: The Internet of Things communication method comprises: Obtain the communication parameters and device movement status of IoT devices in real time; The communication parameters and the mobile state of the IoT device are input into a pre-selected and trained communication model for comparison and analysis, and the communication environment state at the next moment is predicted; Analyze the communication environment state at the next moment by using an adaptive algorithm to determine the communication protocol and data transmission rules of the Internet of Things at the next moment; According to the communication protocol and data sending rules of the Internet of Things at the next moment, the Internet of Things devices are adjusted accordingly.

2. The Internet of Things communication method according to claim 1, characterized in that: The real-time acquisition of the communication parameters and device movement status of the IoT device includes: Acquire current communication parameters of the IoT device, wherein the communication parameters include signal strength, bandwidth utilization, delay, packet loss rate, and throughput; The current movement state of the IoT device is obtained, where the movement state includes position, speed, direction, acceleration, and movement trajectory.

3. The Internet of Things communication method according to claim 1, characterized in that: Before inputting the communication parameters and the mobile state of the IoT device into a pre-selected trained communication model for comparison and analysis, and predicting the communication environment state at the next moment, obtaining the pre-trained communication model includes: Acquire historical communication parameters of the IoT device in different environments and mobile states, and preprocess the historical communication parameters of the IoT device in different environments and mobile states to obtain a training set; Building a communication model based on a convolutional neural network, and performing forward propagation and back-propagation training on the communication model using the training set to obtain a communication model after forward propagation and back-propagation training; The model parameters of the communication model trained by forward propagation and back propagation are adjusted using a genetic algorithm to obtain the pre-trained communication model.

4. The Internet of Things communication method according to claim 3, characterized in that: The communication parameters and the mobile state of the IoT device are input into a pre-selected and trained communication model for comparison and analysis, and the communication environment state at the next moment is predicted to include: Performing data enhancement processing on the communication parameters of the IoT device to obtain enhanced communication parameters; Extract features from the enhanced communication parameters and the device movement state to obtain feature values ​​of the IoT device; After inputting the characteristic value of the IoT device into a pre-selected and trained communication model for comparison and analysis, the communication environment state at the next moment is predicted.

5. The Internet of Things communication method according to claim 4, characterized in that: The method of analyzing the communication environment state at the next moment by using an adaptive algorithm to determine the communication protocol and data transmission rules of the Internet of Things at the next moment includes: The Markov decision process is used to analyze the time series of the communication environment state at the next moment, and the reinforcement learning algorithm is used to adjust the selection of the communication protocol to obtain the communication protocol and data sending rules of the Internet of Things at the next moment.

6. The Internet of Things communication method according to claim 5, characterized in that: The Internet of Things communication method further includes: Using simulation testing to verify the communication protocol and data transmission rules of the Internet of Things at the next moment, and obtain a verification result; According to the verification result, adjusting the parameters of the pre-trained communication model and the adaptive algorithm; When an abnormality occurs in the adjustment of the IoT device, the communication parameters of the IoT device are adjusted to a preset safe state, and an alarm signal is generated and transmitted to the user end.

7. An Internet of Things communication system, characterized in that: The Internet of Things communication system comprises: Real-time monitoring module, used to obtain the communication parameters and device movement status of IoT devices in real time; A prediction module is used to input the communication parameters and device movement status of the IoT device into a pre-selected and trained communication model for comparison and analysis, and predict the communication environment status at the next moment; Determine an output module, which is used to analyze the communication environment state at the next moment by using an adaptive algorithm to determine the communication protocol and data transmission rules of the Internet of Things at the next moment; The adjustment module is used to adjust the Internet of Things device accordingly according to the communication protocol and data transmission rules of the Internet of Things at the next moment.

8. The Internet of Things communication system according to claim 7, characterized in that: The Internet of Things communication system further includes: A verification module, used to verify the communication protocol and data transmission rules of the Internet of Things at the next moment by using a simulation test to obtain a verification result; A parameter adjustment module, used to adjust the parameters of the pre-trained communication model and the adaptive algorithm according to the verification result; The alarm module is used to adjust the communication parameters of the IoT device to a preset safe state when an abnormality occurs in the adjustment of the IoT device, and to generate an alarm signal to transmit to the user end.

9. A computer terminal comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the Internet of Things communication method as described in any one of claims 1 to 6 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of an Internet of Things communication method as described in any one of claims 1 to 6 are implemented.

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