Traffic optimization control method and system applied to carry-on WIFI of Internet of Things

Abstract

The invention relates to a flow optimization control method and system applied to carry-on WIFI of the Internet of Things, and the method comprises the steps: U1, obtaining the data information of the operation state of the Internet of Things in real time in the process that the Internet of Things transmits a data packet through the carry-on WIFI, and obtaining the data information of the transmission speed of the supply flow of the carry-on WIFI in real time; and U2, on the basis of the data information of the operation state of the Internet of Things, predicting the transmission speed of the demand flow of the Internet of Things by adopting an artificial neural network prediction algorithm optimized on the basis of emery migration to obtain the predicted data information of the transmission speed of the demand flow of the Internet of Things. According to the invention, the dynamic management and optimization of the carry-on WiFi traffic of the Internet of Things are realized, and the bandwidth utilization rate and the transmission efficiency are remarkably improved.

Description

TECHNICAL FIELD

[0001] The application relates to the field of traffic control technology of a personal WiFi, in particular to a traffic optimization control method and system of a personal WiFi applied to an Internet of Things. BACKGROUND

[0002] The Internet of Things is a network based on the Internet, a traditional telecommunications network and the like information carriers, and enables all ordinary physical objects capable of being independently addressed to realize interconnection and intercommunication. With the rapid development of the Internet of Things technology, more and more intelligent devices rely on wireless networks for data transmission. However, due to the large number and wide distribution of Internet of Things devices, the traditional network traffic management method cannot meet the needs thereof. The traditional method usually adopts a static allocation strategy, which cannot adjust the traffic allocation according to the real-time running state, resulting in waste of network resources and low transmission efficiency. The personal WiFi can effectively solve the network connection problem of the Internet of Things devices as a convenient wireless access method, but there are still deficiencies in traffic optimization. Therefore, how to design a personal WiFi traffic optimization method suitable for the Internet of Things scene has become a problem to be solved. SUMMARY

[0003] In view of the above problems, the application provides a traffic optimization control method and system of a personal WiFi applied to an Internet of Things, which not only realizes dynamic management and optimization of the personal WiFi traffic of the Internet of Things, but also significantly improves the bandwidth utilization rate and transmission efficiency.

[0004] In order to achieve the above object and other related objects, the technical scheme provided by the application is as follows: A traffic optimization control method of a personal WiFi applied to an Internet of Things, the method comprising: U1. In the process of data packet transmission of the Internet of Things through the personal WiFi, real-time data information of the running state of the Internet of Things is acquired, and real-time data information of the transmission speed of the supply traffic of the personal WiFi is acquired; U2. Based on the data information of the running state of the Internet of Things, a humpback whale migration-based optimization artificial neural network prediction algorithm is used to predict the transmission speed of the demand traffic of the Internet of Things, to obtain data information of the predicted transmission speed of the demand traffic of the Internet of Things; U3. Based on the data information of the predicted transmission speed of the demand traffic of the Internet of Things and the data information of the transmission speed of the supply traffic of the personal WiFi, an improved plant root and stem growth optimization algorithm is used to optimize the transmission speed of the supply traffic of the personal WiFi, to obtain data information of the optimized transmission speed of the supply traffic of the personal WiFi; U4. Constructing a portable WIFI bandwidth utilization representation function Q based on the data information of the optimized transmission speed of the supply flow of the portable WIFI, representing the portable WIFI bandwidth utilization value, to obtain the data information of the portable WIFI bandwidth utilization value.

[0005] Further, the method further comprises: U5. Based on the data information of the portable WIFI bandwidth utilization value, setting a preset threshold, if the portable WIFI bandwidth utilization value is greater than the preset threshold, it will cause congestion in the process of transmitting the Internet of Things data packet, returning to step U3, if the portable WIFI bandwidth utilization value is less than the preset threshold, it is normal, and the transmission of the Internet of Things data packet is carried out.

[0006] Further, the portable WIFI bandwidth utilization representation function Q is, , Wherein, x is the data information of the optimized transmission speed of the supply flow of the portable WIFI, dk1, dk2 and dk3 are weight factors.

[0007] Further, in step U2, the method of predicting the transmission speed of the Internet of Things demand flow by using the artificial neural network prediction algorithm based on the migration of humpback whales comprises: U21. Input the data information of the running state of the Internet of Things into the artificial neural network prediction model for training and learning, reset the fusion weight of the model, and obtain the data information of the reset fusion weight of the model; U22. Based on the data information of the reset fusion weight of the model, initialize the population of humpback whales, determine the parameters of the population of humpback whales, and obtain the data information of the initialized population of humpback whales; U23. Based on the data information of the initialized population of humpback whales, representing the habitat adaptability value of the individual of the population of humpback whales according to the habitat adaptability function of the individual of the population, obtaining the data information of the habitat adaptability value of the individual of the population of humpback whales; U24. Based on the data information of the habitat adaptability value of the individual of the population of humpback whales, enabling the target guiding function to optimize the reset fusion weight of the model, to obtain the optimized artificial neural network prediction model.

[0008] Further, the method of predicting the transmission speed of the Internet of Things demand flow by using the artificial neural network prediction algorithm based on the migration of humpback whales further comprises: U25. Based on the optimized artificial neural network prediction model, input the data information of the running state of the Internet of Things, predict the transmission speed of the Internet of Things demand flow, and obtain the data information of the predicted transmission speed of the Internet of Things demand flow.

[0009] Further, in step U3, the transmission speed of the supply traffic of the body WIFI is optimized by using the improved plant rhizome growth optimization algorithm, including: U31. Based on the data information of the predicted transmission speed of the Internet of Things demand traffic, a position update function G of the main root is established, , Wherein, r t is the data information of the transmission speed of the t time of the predicted Internet of Things demand traffic, δ is a learning factor, γ is a random disturbance coefficient, and η is a Gaussian random number; U32. Based on the data information of the transmission speed of the supply traffic of the body WIFI, a position update function H of the lateral root is established, , Wherein, g t is the data information of the transmission speed of the t time of the supply traffic of the body WIFI, r t is the data information of the transmission speed of the t time of the predicted Internet of Things demand traffic, and μ is a local search coefficient; U33. Based on the position update function G of the main root and the position update function H of the lateral root, the transmission speed of the supply traffic of the body WIFI is optimized according to the resource competition relationship of plant growth, and the data information of the optimized transmission speed of the supply traffic of the body WIFI is obtained.

[0010] In order to achieve the above-mentioned purpose and other related purposes, the application further provides a system for implementing any one of the application of the traffic optimization control method applied to the Internet of Things, the system comprises: A data acquisition module is used for acquiring the data information of the running state of the Internet of Things in real time, and acquiring the data information of the transmission speed of the supply traffic of the body WIFI in real time; An Internet of Things demand traffic transmission speed prediction module is connected with the data acquisition module, and is used for predicting the transmission speed of the Internet of Things demand traffic by using a humpback whale migration optimization artificial neural network prediction algorithm, to obtain the data information of the predicted transmission speed of the Internet of Things demand traffic; A body WIFI supply traffic transmission speed optimization module is connected with the Internet of Things demand traffic transmission speed prediction module, and is used for optimizing the transmission speed of the supply traffic of the body WIFI by using an improved plant rhizome growth optimization algorithm, to obtain the data information of the optimized transmission speed of the supply traffic of the body WIFI; The bandwidth utilization value representation module of the portable WIFI is connected with the transmission speed optimization module of the supply flow of the portable WIFI, and is used for constructing a portable WIFI bandwidth utilization representation function Q, representing the bandwidth utilization value of the portable WIFI, and obtaining data information of the bandwidth utilization value of the portable WIFI. The threshold judgment module is connected with the bandwidth utilization value representation module of the portable WIFI, and is used for setting a preset threshold.

[0011] The present application has the following positive effects: 1. The present application predicts the transmission speed of the demand flow of the Internet of Things by using the humpback whale migration-based artificial neural network prediction algorithm, and optimizes the transmission speed of the supply flow of the portable WIFI by using the improved plant root growth optimization algorithm, which can accurately predict the demand flow of the Internet of Things, ensure the stability of the data transmission of the Internet of Things, optimize the supply flow of the portable WIFI, ensure the smoothness of the data packet transmission of the Internet of Things, and improve the bandwidth utilization.

[0012] 2. The present application constructs a portable WIFI bandwidth utilization representation function Q, represents the bandwidth utilization value of the portable WIFI, sets a preset threshold, and if the bandwidth utilization value of the portable WIFI is greater than the preset threshold, the data packet transmission process of the Internet of Things will be congested, and if the bandwidth utilization value of the portable WIFI is less than the preset threshold, the data packet transmission of the Internet of Things will be normal, which further ensures the stability of the data packet transmission of the Internet of Things, and at the same time, dynamically adjusts the flow allocation strategy to improve the bandwidth utilization. BRIEF DESCRIPTION OF DRAWINGS

[0013] Figure 1 The present application is a method flowchart; Figure 2 The present application is a flowchart of the humpback whale migration-based artificial neural network prediction algorithm; Figure 3 The present application is a flowchart of the improved plant root growth optimization algorithm; Figure 4 The present application is a system framework diagram. DETAILED DESCRIPTION

[0014] The following description of exemplary embodiments of the present disclosure is made in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding. These details should be considered as merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0015] Example 1: Figure 1 As shown, a flow optimization control method for portable WIFI applied to the Internet of Things, the method comprising: U1. During data packet transmission via the portable Wi-Fi, the IoT obtains real-time data on its operating status and the speed of the Wi-Fi's supplied traffic. U2. Based on the data information of the operation status of the Internet of Things, the transmission speed of the Internet of Things demand traffic is predicted using an artificial neural network prediction algorithm based on humpback whale migration optimization to obtain data information on the transmission speed of the predicted Internet of Things demand traffic; U3. Based on the predicted transmission speed of the IoT demand traffic and the data on the transmission speed of the portable Wi-Fi supply traffic, optimize the transmission speed of the portable Wi-Fi supply traffic using an improved plant rhizome growth optimization algorithm to obtain optimized data on the transmission speed of the portable Wi-Fi supply traffic; U4. Based on the data information of the transmission speed of the supplied traffic of the optimized portable WIFI, a portable WIFI bandwidth utilization characterization function Q is constructed to characterize the bandwidth utilization value of the portable WIFI and obtain data information of the bandwidth utilization value of the portable WIFI.

[0016] In this embodiment, if Figure 2 As shown, in step U2, the use of the humpback whale migration optimization-based artificial neural network prediction algorithm to predict the transmission speed of the IoT demand traffic includes: U21. The data information of the operation status of the Internet of Things is input into the artificial neural network prediction model for training and learning, and the fusion weight of the model is reset to obtain the data information of the fusion weight of the reset model; U22. Initialize the humpback whale population based on the data information of the fusion weight of the reset model, determine the parameters of the humpback whale population, and obtain data information of the initialized humpback whale population; U23. Based on the initialized humpback whale population data information, characterize the habitat adaptability values ​​of the individuals in the humpback whale population according to the habitat adaptability function of the individuals in the population to obtain data information on the habitat adaptability values ​​of the individuals in the humpback whale population; U24. Based on the data information of the habitat adaptability value of the individual of the humpback whale population, the target guide function is used to optimize the fusion weight of the reset model, and an optimized artificial neural network prediction model is obtained.

[0017] In the embodiment, the transmission speed of the demand flow of the Internet of Things is predicted by using the artificial neural network prediction algorithm based on the migration of the humpback whale to optimize the transmission speed of the demand flow of the Internet of Things. U25. Based on the optimized artificial neural network prediction model, the data information of the running state of the Internet of Things is input, the transmission speed of the demand flow of the Internet of Things is predicted, and the data information of the predicted transmission speed of the demand flow of the Internet of Things is obtained.

[0018] In the embodiment, as shown in Figure 3 The transmission speed of the supply flow of the body WIFI is optimized by using the improved plant root growth optimization algorithm in step U3, which includes: U31. Based on the data information of the predicted transmission speed of the demand flow of the Internet of Things, the position updating function G of the main root is established, , Wherein, r t is the data information of the predicted transmission speed of the demand flow of the Internet of Things at the t time, δ is the learning factor, γ is the random disturbance coefficient, and η is the Gaussian random number; U32. Based on the data information of the transmission speed of the supply flow of the body WIFI, the position updating function H of the lateral root is established, , Wherein, g t is the data information of the transmission speed of the supply flow of the body WIFI at the t time, r t is the data information of the predicted transmission speed of the demand flow of the Internet of Things at the t time, and μ is the local search coefficient; U33. Based on the position updating function G of the main root and the position updating function H of the lateral root, the transmission speed of the supply flow of the body WIFI is optimized according to the resource competition relationship of plant growth, and the data information of the optimized transmission speed of the supply flow of the body WIFI is obtained.

[0019] In this embodiment, an Internet of Things system contains 100 sensor nodes, which are connected to the Internet through the WiFi. During the experiment, first, the running state data of each node (such as CPU occupancy, memory usage, etc.) and the supply flow transmission speed data of the WiFi are collected. Then, the demand flow of the Internet of Things is predicted using the humpback whale migration-based transfer optimization artificial neural network prediction algorithm, and the result shows that the prediction error is less than 5%. Next, the supply flow of the WiFi is optimized using the improved plant rhizome growth optimization algorithm, and the bandwidth utilization rate is improved by about 20% after optimization. Finally, the stability of the optimization effect is verified by constructing a bandwidth utilization rate representation function Q.

[0020] In this embodiment, the application can be widely applied to the fields of smart home, industrial Internet of Things, smart city, etc. For example, in the smart home scene, multiple smart devices are connected to the home gateway through the WiFi, and the flow optimization method of the application can ensure that high-priority devices such as video monitoring and voice assistants obtain sufficient bandwidth support, while taking into account the needs of other low-priority devices.

[0021] Embodiment 2: Based on the application of the WiFi flow optimization control method for the Internet of Things in embodiment 1, the application is further described and explained as follows.

[0022] As shown in Figure 1 , a WiFi flow optimization control method for the Internet of Things, the method comprises: U1. During the data packet transmission process of the Internet of Things through the WiFi, real-time data information of the running state of the Internet of Things is obtained, and real-time data information of the transmission speed of the supply flow of the WiFi is obtained; U2. Based on the data information of the running state of the Internet of Things, the transmission speed of the demand flow of the Internet of Things is predicted using the humpback whale migration-based transfer optimization artificial neural network prediction algorithm, and the data information of the predicted transmission speed of the demand flow of the Internet of Things is obtained; U3. Based on the data information of the predicted transmission speed of the demand flow of the Internet of Things and the data information of the transmission speed of the supply flow of the WiFi, the transmission speed of the supply flow of the WiFi is optimized using the improved plant rhizome growth optimization algorithm, and the data information of the optimized transmission speed of the supply flow of the WiFi is obtained; U4. Based on the data information of the optimized transmission speed of the supply flow of the WiFi, a WiFi bandwidth utilization rate representation function Q is constructed to represent the bandwidth utilization rate value of the WiFi, and the data information of the WiFi bandwidth utilization rate value is obtained.

[0023] In this embodiment, the method further comprises: U5. Based on the data information of the body WIFI bandwidth utilization value, a preset threshold is set, if the body WIFI bandwidth utilization value is greater than the preset threshold, the congestion of the Internet of Things data packet transmission process is caused, returning to step U3, if the body WIFI bandwidth utilization value is less than the preset threshold, the transmission of the Internet of Things data packet is normal.

[0024] In the embodiment, the body WIFI bandwidth utilization rate representation function Q is, , Wherein, x is the data information of the transmission speed of the optimized supply flow of the body WIFI, dk1, dk2 and dk3 are weight factors.

[0025] In the embodiment, as Figure 4 shown, the application provides a system for implementing any one of the flow optimization control methods applied to the body WIFI of the Internet of Things, the system comprises: A data acquisition module is used to acquire the data information of the running state of the Internet of Things in real time, and the data information of the transmission speed of the supply flow of the body WIFI in real time; An Internet of Things demand flow transmission speed prediction module is connected with the data acquisition module, and is used to predict the transmission speed of the Internet of Things demand flow by using a humpback whale migration optimization artificial neural network prediction algorithm, to obtain the data information of the predicted transmission speed of the Internet of Things demand flow; A body WIFI supply flow transmission speed optimization module is connected with the Internet of Things demand flow transmission speed prediction module, and is used to optimize the transmission speed of the body WIFI supply flow by using an improved plant root growth optimization algorithm, to obtain the data information of the optimized transmission speed of the body WIFI supply flow; A body WIFI bandwidth utilization value representation module is connected with the body WIFI supply flow transmission speed optimization module, and is used to construct a body WIFI bandwidth utilization rate representation function Q, to represent the bandwidth utilization value of the body WIFI, to obtain the data information of the body WIFI bandwidth utilization value; A threshold judgment module is connected with the body WIFI bandwidth utilization value representation module, and is used to set a preset threshold, if the body WIFI bandwidth utilization value is greater than the preset threshold, the congestion of the Internet of Things data packet transmission process is caused, if the body WIFI bandwidth utilization value is less than the preset threshold, the transmission of the Internet of Things data packet is normal.

[0026] The application further provides a computer readable storage medium, which stores a computer program programmed or configured to execute any one of the application methods for body-mounted WIFI traffic optimization control applied to the Internet of Things.

[0027] Any reference to memory, storage, database, or other medium in the embodiments provided by the present application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration but not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), Synchlink DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct RAM bus dynamic RAM (DRDRAM), and Rambus dynamic RAM (RDRAM), etc.

[0028] In the present embodiment, compared with the prior art, the application has higher prediction accuracy and stronger adaptability, can meet the traffic management requirements in complex Internet of Things scenarios, and combines the blue whale migration optimized artificial neural network with the improved plant rhizome growth optimization algorithm to provide a new solution for Internet of Things traffic optimization.

[0029] In the present embodiment, in order to further verify the effect of the application, the application is compared with the prior art, and the comparison data is shown in the following table:

[0030] As described in the above table, by introducing the artificial intelligence prediction algorithm and the optimization algorithm, the application realizes dynamic management and optimization of body-mounted WIFI traffic of the Internet of Things, and significantly improves the bandwidth utilization rate and transmission efficiency. Compared with the prior art, the application has higher prediction accuracy and stronger adaptability, and can meet the traffic management requirements in complex Internet of Things scenarios.

[0031] In summary, the application not only realizes dynamic management and optimization of body-mounted WIFI traffic of the Internet of Things, but also significantly improves the bandwidth utilization rate and transmission efficiency.

[0032] The above detailed description does not limit the scope of the disclosure. Various modifications, combinations, sub-combinations and alternatives can be made to the detailed description. Any modification, equivalent replacement and improvement etc. made within the spirit and principle of the disclosure shall be included in the scope of the disclosure.

Claims

1. A flow optimization control method for portable WIFI applied to the Internet of Things, characterized in that: The method comprises: U1. During data packet transmission via the portable Wi-Fi, the IoT obtains real-time data on its operating status and the speed of the Wi-Fi's supplied traffic. U2. Based on the data information of the operation status of the Internet of Things, the transmission speed of the Internet of Things demand traffic is predicted using an artificial neural network prediction algorithm based on humpback whale migration optimization to obtain data information on the transmission speed of the predicted Internet of Things demand traffic; U3. Based on the predicted transmission speed of the IoT demand traffic and the data on the transmission speed of the portable Wi-Fi supply traffic, optimize the transmission speed of the portable Wi-Fi supply traffic using an improved plant rhizome growth optimization algorithm to obtain optimized data on the transmission speed of the portable Wi-Fi supply traffic; U4. Based on the data information of the transmission speed of the supplied traffic of the optimized portable WIFI, a portable WIFI bandwidth utilization characterization function Q is constructed to characterize the bandwidth utilization value of the portable WIFI and obtain data information of the bandwidth utilization value of the portable WIFI.

2. The method for optimizing the flow of portable Wi-Fi used in the Internet of Things according to claim 1 is characterized in that: The method further comprises: U5. Based on the data information of the portable WIFI bandwidth utilization value, a preset threshold is set. If the portable WIFI bandwidth utilization value is greater than the preset threshold, it will cause congestion in the IoT data packet transmission process. Return to step U3. If the portable WIFI bandwidth utilization value is less than the preset threshold, it is normal and the IoT data packet transmission is carried out.

3. The method for optimizing the flow of portable Wi-Fi used in the Internet of Things according to claim 1 is characterized in that: The portable WIFI bandwidth utilization characterization function Q is: ， Among them, x is the data information of the transmission speed of the supply traffic of the optimized portable WIFI, and dk1, dk2 and dk3 are weight factors.

4. The method for optimizing the flow of portable Wi-Fi used in the Internet of Things according to claim 1, characterized in that: In step U2, the prediction of the transmission speed of the IoT demand traffic using the humpback whale migration optimization artificial neural network prediction algorithm includes: U21. The data information of the operation status of the Internet of Things is input into the artificial neural network prediction model for training and learning, and the fusion weight of the model is reset to obtain the data information of the fusion weight of the reset model; U22. Initialize the humpback whale population based on the data information of the fusion weight of the reset model, determine the parameters of the humpback whale population, and obtain data information of the initialized humpback whale population; U23. Based on the initialized humpback whale population data information, characterize the habitat adaptability values ​​of the individuals in the humpback whale population according to the habitat adaptability function of the individuals in the population to obtain data information on the habitat adaptability values ​​of the individuals in the humpback whale population; U24. Based on the data information of the habitat adaptability values ​​of individuals of the humpback whale population, the target guidance function is enabled to optimize the fusion weights of the reset model to obtain an optimized artificial neural network prediction model.

5. The method for optimizing the flow of portable Wi-Fi used in the Internet of Things according to claim 4 is characterized in that: The method of predicting the transmission speed of IoT demand traffic using an artificial neural network prediction algorithm based on humpback whale migration optimization also includes: U25. Based on the optimized artificial neural network prediction model, the data information of the operating status of the Internet of Things is input, the transmission speed of the Internet of Things demand traffic is predicted, and the data information of the predicted transmission speed of the Internet of Things demand traffic is obtained.

6. The method for optimizing the flow of portable Wi-Fi used in the Internet of Things according to claim 1, characterized in that: In step U3, the optimization of the transmission speed of the supplied traffic of the portable WIFI using the improved plant rhizome growth optimization algorithm includes: U31. Based on the data information of the transmission speed of the predicted IoT demand flow, establish the main root position update function G, ， Among them, r t is the data information of the transmission speed of the predicted IoT demand traffic at the tth moment, δ is the learning factor, γ is the random disturbance coefficient, and η is the Gaussian random number; U32. Based on the data information of the transmission speed of the supply flow of the portable WIFI, establish the location update function H of the side root, ， Among them, g t is the data information of the transmission speed of the portable WIFI at the tth moment, r t is the data information of the transmission speed of the predicted IoT demand traffic at the tth moment, μ is the local search coefficient; U33. Based on the main root position update function G and the lateral root position update function H, the transmission speed of the supply traffic of the portable WIFI is optimized according to the resource competition relationship of plant growth, and data information of the optimized transmission speed of the supply traffic of the portable WIFI is obtained.

7. A system for implementing the traffic optimization control method for portable WIFI applied to the Internet of Things according to any one of claims 1 to 6, characterized in that: The system comprises: The data acquisition module is used to obtain data information about the operation status of the Internet of Things in real time, and to obtain data information about the transmission speed of the supply flow of the portable WIFI in real time; The transmission speed prediction module of the Internet of Things demand traffic is connected to the data acquisition module and is used to predict the transmission speed of the Internet of Things demand traffic using an artificial neural network prediction algorithm based on humpback whale migration optimization to obtain data information on the transmission speed of the predicted Internet of Things demand traffic; A transmission speed optimization module for the supply flow of the portable Wi-Fi is connected to the transmission speed prediction module for the demand flow of the Internet of Things, and is used to optimize the transmission speed of the supply flow of the portable Wi-Fi using an improved plant rhizome growth optimization algorithm to obtain data information on the transmission speed of the supply flow of the portable Wi-Fi after optimization; a portable Wi-Fi bandwidth utilization value characterization module, connected to the portable Wi-Fi supply flow transmission speed optimization module, for constructing a portable Wi-Fi bandwidth utilization value characterization function Q, characterizing the portable Wi-Fi bandwidth utilization value, and obtaining data information of the portable Wi-Fi bandwidth utilization value; The threshold judgment module is connected to the characterization module of the portable WIFI bandwidth utilization value and is used to set a preset threshold. If the portable WIFI bandwidth utilization value is greater than the preset threshold, congestion will occur in the IoT data packet transmission process. If the portable WIFI bandwidth utilization value is less than the preset threshold, it is normal and the IoT data packet transmission is carried out.

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