A data transmission method, system and device based on portable WiFi network

Abstract

The present invention relates to the technical field of wireless communication and data transmission, and in particular to a data transmission method, system and device based on a portable WiFi network. The method comprises the following steps: obtaining initial multipath channel signal data; performing channel feature extraction on the initial multipath channel signal data to obtain antenna channel feature data; performing phased array antenna beam calculation on the antenna channel feature data to obtain the antenna initial beam pattern; performing spatial filter interference suppression on the antenna initial beam pattern to obtain antenna optimized beam shape data; performing digital beamforming on the antenna optimized beam shape data to obtain antenna beam configuration parameters; performing network topology node analysis on the antenna beam configuration parameters to obtain a portable WiFi node distribution map; performing improved on-demand routing calculation on the portable WiFi node distribution map to obtain an initial routing topology structure. The present invention improves the reliability of data transmission in a portable WiFi network.

Description

Technical Field

[0001] The present invention relates to the technical field of wireless communication and data transmission, and in particular to a data transmission method, system and device based on a portable WiFi network. Background Art

[0002] Portable WiFi brings many conveniences, but it is not without shortcomings. First, a major technical difficulty is the processing of multipath channel signal data. In complex environments, such as urban blocks or indoor spaces, wireless signals often undergo multiple reflections, refractions, and scattering, forming multipath propagation. This causes the direct, reflected, and scattered components of the signal to overlap with each other, making the reception and processing of the signal complicated. Traditional portable WiFi devices often lack effective channel feature extraction and multipath signal processing mechanisms, resulting in reduced signal quality and reduced data transmission efficiency. Secondly, existing portable WiFi networks also have deficiencies in beam management and network topology. Due to the lack of precise beamforming and optimization technology, existing portable WiFi devices are often not accurate enough in signal coverage and transmission direction, which not only affects the transmission quality of the signal, but also increases the possibility of interference. In addition, the insufficient optimization of the network topology leads to inefficient routing calculations and the inability to dynamically adjust the routing according to actual network conditions, thus affecting the stability and efficiency of data transmission. Summary of the invention

[0003] Based on this, it is necessary for the present invention to provide a data transmission method, system and device based on a portable WiFi network to solve at least one of the above technical problems.

[0004] To achieve the above purpose, a data transmission method based on a portable WiFi network includes the following steps:

[0005] Step S1: Acquire initial multipath channel signal data; extract channel characteristics from the initial multipath channel signal data to obtain antenna channel characteristic data;

[0006] Step S2: performing phased array antenna beam calculation on antenna channel characteristic data to obtain an initial antenna beam pattern; performing spatial filter interference suppression on the initial antenna beam pattern to obtain antenna optimized beam shape data; performing digital beam forming on the antenna optimized beam shape data to obtain antenna beam configuration parameters;

[0007] Step S3: Perform network topology node analysis on the antenna beam configuration parameters to obtain a portable WiFi node distribution map; perform improved on-demand routing calculation on the portable WiFi node distribution map to obtain an initial routing topology structure; perform redundant link optimization on the initial routing topology structure to obtain a WiFi hierarchical mesh routing map;

[0008] Step S4: Based on the WiFi hierarchical mesh routing diagram, the dual-frequency channel resources of the portable WiFi are detected to obtain a portable WiFi channel resource status table; bandwidth aggregation calculation is performed on the portable WiFi according to the portable WiFi channel resource status table to obtain a portable WiFi bandwidth allocation plan; time slots are divided for the dual-frequency data streams of the portable WiFi according to the portable WiFi bandwidth allocation plan to obtain a portable WiFi resource scheduling strategy;

[0009] Step S5: Perform distributed network coding on the portable WiFi according to the portable WiFi resource scheduling strategy to obtain data transmission coding data; perform retransmission control on the portable WiFi according to the data transmission coding data to obtain a WiFi reliable transmission control solution.

[0010] The present invention can accurately identify and process multipath channel signal data through channel feature extraction, effectively distinguish direct, reflected and scattered signal components, thereby improving the accuracy and quality of signal reception. This not only reduces signal interference, but also improves the efficiency of data transmission, especially in complex environments, such as urban blocks or indoor spaces. Through phased array antenna beam calculation and digital beamforming technology, the portable WiFi device can accurately control the coverage and transmission direction of the signal. This precise beam management reduces unnecessary signal diffusion, improves signal strength, and also reduces interference with other wireless networks, thereby improving the overall network performance. Through network topology node analysis and on-demand routing calculation, the portable WiFi network can dynamically adjust the route according to the actual network conditions to form a more efficient routing topology. This not only improves the stability of data transmission, but also improves the scalability and flexibility of the network, so that the network can adapt to different usage scenarios and conditions. Through dual-frequency channel resource status detection and bandwidth aggregation calculation, the portable WiFi network can intelligently allocate bandwidth resources and dynamically adjust the bandwidth allocation plan according to actual business needs and channel status. This improves bandwidth utilization while ensuring the quality of service for different services, meeting users' needs for high bandwidth and low latency. Through distributed network coding and retransmission control strategies, the portable WiFi network is provided with stronger data transmission reliability. Through the transmission of coded data and effective retransmission control, accurate data transmission can be ensured even in environments with poor signals or severe interference, reducing the possibility of data loss and errors.

[0011] Preferably, the present invention further provides a data transmission system based on a portable WiFi network, which is used to execute the data transmission method based on a portable WiFi network as described above. The data transmission system based on a portable WiFi network comprises:

[0012] The channel feature extraction module is used to obtain initial multipath channel signal data; extract channel features from the initial multipath channel signal data to obtain antenna channel feature data;

[0013] The beam management module is used to perform phased array antenna beam calculation on antenna channel characteristic data to obtain the antenna initial beam pattern; perform spatial filter interference suppression on the antenna initial beam pattern to obtain antenna optimized beam shape data; perform digital beam shaping on the antenna optimized beam shape data to obtain antenna beam configuration parameters;

[0014] The network topology analysis module is used to perform network topology node analysis on antenna beam configuration parameters to obtain a portable WiFi node distribution map; perform improved on-demand routing calculation on the portable WiFi node distribution map to obtain an initial routing topology structure; perform redundant link optimization on the initial routing topology structure to obtain a WiFi hierarchical mesh routing map;

[0015] The resource scheduling management module is used to detect the status of the dual-frequency channel resources of the portable WiFi based on the WiFi hierarchical mesh routing diagram to obtain the portable WiFi channel resource status table; perform bandwidth aggregation calculation on the portable WiFi according to the portable WiFi channel resource status table to obtain the portable WiFi bandwidth allocation plan; perform time slot division on the dual-frequency data stream of the portable WiFi according to the portable WiFi bandwidth allocation plan to obtain the portable WiFi resource scheduling strategy;

[0016] The data transmission coding module is used to perform distributed network coding on the portable WiFi according to the portable WiFi resource scheduling strategy to obtain data transmission coding data; and to perform retransmission control on the portable WiFi according to the data transmission coding data to obtain a WiFi reliable transmission control solution.

[0017] The present invention can accurately acquire and process multipath channel signal data through a channel feature extraction module, thereby improving the accuracy and quality of signal reception. The application of the beam management module enables the system to accurately calculate and adjust the antenna beam, thereby realizing beam optimization and digital beamforming. The network topology analysis module enables the system to dynamically analyze network topology nodes according to antenna beam configuration parameters, optimize routing calculations, and construct a WiFi hierarchical mesh routing map. This improves the flexibility and scalability of the network, enabling it to adapt to the ever-changing network environment. The resource scheduling management module can intelligently perform bandwidth aggregation and time slot division according to the real-time channel resource status and bandwidth requirements, thereby achieving more efficient resource utilization and service quality assurance. The data transmission coding module enhances the reliability of data transmission through distributed network coding and retransmission control strategies. Even in the case of poor network conditions, accurate and timely data transmission can be ensured, reducing data loss and errors.

[0018] Preferably, the present invention also provides a data transmission device based on a portable WiFi network, applying the data transmission system based on the portable WiFi network as described above, the data transmission device based on the portable WiFi network comprises a portable WiFi device body and a 18650 battery, a battery placement area is provided on the portable WiFi device body, and the 18650 battery is detachably arranged in the battery placement area.

[0019] In the present invention, both ends of the 18650 battery are provided with an anti-reverse discharge circuit, and the anti-reverse discharge circuit includes an NMOS tube and a resistor. When the 18650 battery is correctly installed, the gate of the MOS tube receives a bias voltage through the resistor, and at this time, the MOS tube can conduct the circuit, so that the portable WiFi device body works normally; when the 18650 battery is reversely installed, the MOS tube cannot conduct the circuit, and the portable WiFi device body disconnects the 18650 battery to disconnect from the mainboard, thereby preventing reverse discharge. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Other features, objects and advantages of the present invention will become more apparent from the detailed description made with reference to the following drawings:

[0021] Figure 1 A schematic flow chart of the steps of a data transmission method based on a portable WiFi network according to an embodiment is shown.

[0022] Figure 2 A detailed flow chart of step S3 of an embodiment is shown.

[0023] Figure 3 A detailed flowchart diagram of step S35 of an embodiment is shown. DETAILED DESCRIPTION

[0024] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are 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 technicians in this field without creative work are within the scope of protection of the present invention.

[0025] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0026] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0027] To achieve this, please refer to Figures 1 to 3 The present invention provides a data transmission method based on a portable WiFi network, comprising the following steps:

[0028] Step S1: Acquire initial multipath channel signal data; extract channel characteristics from the initial multipath channel signal data to obtain antenna channel characteristic data;

[0029] Step S2: performing phased array antenna beam calculation on antenna channel characteristic data to obtain an initial antenna beam pattern; performing spatial filter interference suppression on the initial antenna beam pattern to obtain antenna optimized beam shape data; performing digital beam forming on the antenna optimized beam shape data to obtain antenna beam configuration parameters;

[0030] Step S3: Perform network topology node analysis on the antenna beam configuration parameters to obtain a portable WiFi node distribution map; perform improved on-demand routing calculation on the portable WiFi node distribution map to obtain an initial routing topology structure; perform redundant link optimization on the initial routing topology structure to obtain a WiFi hierarchical mesh routing map;

[0031] Step S4: Based on the WiFi hierarchical mesh routing diagram, the dual-frequency channel resources of the portable WiFi are detected to obtain a portable WiFi channel resource status table; bandwidth aggregation calculation is performed on the portable WiFi according to the portable WiFi channel resource status table to obtain a portable WiFi bandwidth allocation plan; time slots are divided for the dual-frequency data streams of the portable WiFi according to the portable WiFi bandwidth allocation plan to obtain a portable WiFi resource scheduling strategy;

[0032] Step S5: Perform distributed network coding on the portable WiFi according to the portable WiFi resource scheduling strategy to obtain data transmission coding data; perform retransmission control on the portable WiFi according to the data transmission coding data to obtain a WiFi reliable transmission control solution.

[0033] In this embodiment, first, a wireless signal scanner (such as Tektronix RSA503A) is used to capture initial multipath channel signal data at multiple locations, and these data include the amplitude, phase and timestamp of the signal. Then, the signal processing toolbox of MATLAB is used to extract channel features from these data, and the signal is converted from the time domain to the frequency domain through Fourier transform, and the main path and multipath components are identified, thereby obtaining antenna channel feature data. The antenna array is modeled using Ansys HFSS electromagnetic simulation software, and the phased array antenna beam is calculated according to the antenna channel feature data to obtain the antenna initial beam pattern. By adjusting the phase and amplitude of each antenna unit, the initial antenna beam pattern is subjected to spatial filter interference suppression, and the filter parameters are optimized using MATLAB's adaptive filter design toolbox to obtain antenna optimized beam shape data. The antenna optimized beam shape data is digitally beamformed using MATLAB's beamforming algorithm to obtain antenna beam configuration parameters. These parameters include weight coefficients for each antenna unit, which are used to adjust the direction and shape of the beam. Use network topology analysis software (such as Riverbed SteelCentral) to analyze the antenna beam configuration parameters and obtain the portable WiFi node distribution map. Analyze the signal strength and connection quality between nodes, and use the Graph Theory algorithm to classify and optimize the layout of nodes. Use Cisco's Network Planning and Optimization Tool to improve the on-demand routing calculation of the portable WiFi node distribution map to obtain the initial routing topology. According to the geographical location and business needs of the nodes, optimize the routing path to reduce latency and improve throughput. Use SolarWinds Network Performance Monitor to optimize the redundant links of the initial routing topology to obtain the WiFi hierarchical mesh routing map. Ensure high availability and load balancing of the network by monitoring network performance and fault detection. Based on the WiFi hierarchical mesh routing map, use Wireshark and custom scripts to detect the status of the dual-band channel resources of the portable WiFi and obtain the portable WiFi channel resource status table. Analyze the channel occupancy and interference level to determine the channel availability and quality. According to the portable WiFi channel resource status table, use MATLAB's optimization toolbox to perform bandwidth aggregation calculation to obtain the portable WiFi bandwidth allocation plan. Considering the service priority and channel conditions, the bandwidth allocation is adjusted dynamically to meet the needs of different services. According to the portable WiFi bandwidth allocation plan, the NS-3 network simulator is used to divide the dual-frequency data stream of the portable WiFi into time slots to obtain the portable WiFi resource scheduling strategy. Different data streams and network conditions are simulated to optimize time slot allocation to improve network throughput and reduce latency.Finally, according to the portable WiFi resource scheduling strategy, Python's Parity-check matrix and coding theory library are used to perform distributed network coding to obtain data transmission coding data. The error detection capability of the data transmission coding data is evaluated, and Reed-Solomon coding is used for forward error correction coding design to obtain a WiFi reliable transmission control scheme.

[0034] Preferably, step S1 comprises the following steps:

[0035] Step S11: deploying an antenna array for the portable WiFi cluster, and performing a multi-frequency channel scan on the environment where the portable WiFi cluster is located according to preset frequency band parameters to obtain initial multipath channel signal data; wherein the initial multipath channel signal data includes main path direct signal data, secondary path reflection signal data, and stray path scattered signal data;

[0036] Specifically, we can assume that there is a portable WiFi cluster consisting of multiple nodes, each equipped with a multi-antenna array. Use a spectrum analyzer that can scan multiple frequency bands. Specifically, two commonly used WiFi bands, 2.4GHz and 5GHz, are selected for scanning. Use software defined radio (SDR) devices, such as USRP (Universal Software Radio Peripheral), to capture signals on these frequency bands. By running a customized scanning program on each node, the main path direct signal data, secondary path reflected signal data, and stray path scattered signal data are collected to obtain a comprehensive multipath channel signal data set.

[0037] Step S12: performing path loss modeling on the main channel direct signal data to obtain a direct channel attenuation characteristic diagram;

[0038] Specifically, a path loss model based on the Rayleigh model can be used to model the main direct signal data. Specifically, open source wireless signal propagation tools such as Wireless InSite or REMCOM's Wireless Insite / X3D are used to simulate the propagation of the main direct signal in an urban environment. The tool allows input of parameters such as building layout, material properties, and antenna height to simulate the attenuation of direct signals. First, a three-dimensional model of a city block is created in the software, including the height of the building, the material, and the layout of the street. A simulated portable WiFi node is placed in the model, and the antenna height and transmission power are set. The software automatically calculates the attenuation of the signal at different distances and directions, and generates a direct channel attenuation characteristic diagram. This chart shows the change in signal strength with distance.

[0039] Step S13: performing multipath delay spread calculation on the secondary path reflection signal data to obtain a reflection channel delay spectrum;

[0040] Specifically, please refer to the sub-steps of step S13 for the detailed implementation process of this embodiment.

[0041] Step S14: performing Doppler frequency shift evaluation on the stray path scattering signal data to obtain a scattering channel frequency shift characteristic diagram;

[0042] Specifically, signal processing libraries in MATLAB or Python, such as NumPy and SciPy, can be used to perform fast Fourier transform (FFT) on the stray path scattering signal data to analyze frequency changes. By comparing the FFT results at different time points, the frequency shift caused by moving objects, namely the Doppler shift, can be identified. These frequency shift data are used to create a scattering channel frequency shift feature map, which shows the frequency shift at different locations and times.

[0043] Step S15: performing channel correlation identification on the direct channel attenuation characteristic graph, the reflected channel delay spectrum and the scattered channel frequency shift characteristic graph to obtain a component channel mutual coupling matrix;

[0044] Specifically, the features of direct, reflected and scattered signals, including parameters such as attenuation, delay and frequency shift, can be extracted using the direct channel attenuation feature map, the reflected channel delay map and the scattered channel frequency shift feature map. These parameters are used as features to input into a trained neural network that can identify and quantify the correlation between different channels. A deep learning framework such as TensorFlow or PyTorch is selected to build and train the neural network. During the training process, a large number of channel data samples are used, which contain channel features collected under different environments and conditions. Through training, the neural network learns how to map these features to a component channel mutual coupling matrix, which describes the strength of correlation between different channels.

[0045] Step S16: performing orthogonal transformation and fusion on the direct channel attenuation characteristic diagram, the reflected channel delay spectrum and the scattered channel frequency shift characteristic diagram according to the component channel mutual coupling matrix to obtain antenna channel characteristic data.

[0046] Specifically, the component channel mutual coupling matrix can be defined in MATLAB, and then the data of the direct channel attenuation characteristic diagram, the reflected channel delay spectrum and the scattered channel frequency shift characteristic diagram are loaded. The built-in function pinv of MATLAB is used to calculate the pseudo-inverse of the matrix to deal with the singular matrix problem. The mtimes function is used to perform matrix multiplication, multiplying the eigenvector of each channel with the component channel mutual coupling matrix to obtain the fused antenna channel characteristic data. These data include path attenuation characteristic data, propagation delay characteristic data and frequency offset characteristic data, which are organized into a structured data table.

[0047] The present invention comprehensively collects multipath channel signal data through multi-frequency channel scanning, accurately analyzes the characteristics of direct, reflected and scattered signals, and identifies the correlation between channels, and finally integrates these characteristic data to improve the efficiency and accuracy of signal processing. This not only enhances the robustness of the network, enabling it to better adapt to complex environments, but also optimizes network resource allocation, improves data transmission rate, and reduces error rate.

[0048] Preferably, step S13 comprises the following steps:

[0049] Step S131: performing multipath power delay evaluation on the secondary path reflection signal data to obtain an incoherent channel delay distribution diagram;

[0050] Specifically, the secondary path reflection signal data can be imported into LabVIEW and analyzed using its built-in signal processing toolkit. The secondary path reflection signal data is subjected to a fast Fourier transform (FFT) to obtain a frequency domain representation, and the peak detection algorithm of LabVIEW is used to identify the delay of the reflection path and the corresponding power. In this way, a non-coherent channel delay distribution diagram can be drawn, which shows the signal power distribution at different delays.

[0051] Step S132: Calculate the delay threshold of the secondary path reflection signal data based on the incoherent channel delay distribution map to obtain a multipath delay segmentation threshold set;

[0052] Specifically, a custom algorithm can be used, which is implemented based on the MATLAB programming environment. This algorithm first analyzes the delay profile of the incoherent channel and identifies points where the signal power drops significantly as potential delay thresholds. A threshold is set, which is the delay when the signal power drops to a certain percentage of the maximum value (for example, 10%). The MATLAB algorithm traverses the delay profile, finds all points that meet this condition, and records the delay values ​​corresponding to these points. These values ​​constitute the multipath delay segmentation threshold set, which defines the boundaries of different reflection paths.

[0053] Step S133: extracting the channel impulse response of the secondary path reflection signal data to obtain non-static time-varying channel response data;

[0054] Specifically, a hardware platform based on USRP (Universal Software Radio Peripheral) can be used in conjunction with the GNU Radio software defined radio (SDR) tool chain to extract the channel impulse response of the secondary path reflection signal data. In the specific operation, the USRP hardware is first configured to receive signals in a specific frequency band and convert them into digital signals. Then, a channel impulse response extraction process is designed in GNU Radio, which includes a matched filter and a sampler. The matched filter is used to maximize the correlation between the signal and the known impulse response template, while the sampler captures the signal sample at the optimal time point. In this way, non-static time-varying channel response data can be obtained, which reflects the characteristics of the channel changing over time, and these data are saved as time series.

[0055] Step S134: performing channel classification on the secondary path reflection signal data according to the multipath delay segmentation threshold set and the non-static time-varying channel response data to obtain an initial multipath delay classification diagram;

[0056] Specifically, the multipath delay segmentation threshold set and the non-static time-varying channel response data can be imported into MATLAB. These data include the timestamps of the signal samples obtained from the previous step and the corresponding delay information. Use the load function of MATLAB to import these data and store them in variables. Use the sort function of MATLAB to sort the signal samples according to their timestamps. Then, the kmeans clustering algorithm of MATLAB is applied to classify the signal samples into different multipath components according to the delay segmentation threshold. The kmeans algorithm can automatically classify the data into corresponding categories according to the number of input thresholds. The delay of each signal sample is compared with the multipath delay segmentation threshold set, and each sample is assigned to the multipath category represented by the closest threshold. This process involves calculating the distance between each sample and the threshold set and selecting the closest threshold. Use the min function of MATLAB to find the minimum distance between each sample and the threshold set and classify them accordingly. Through the application of the kmeans algorithm, an initial multipath delay classification map is created, which clearly shows the signal distribution and characteristics of different multipath components.

[0057] Step S135: performing channel cluster morphological reconstruction on the initial multipath delay classification graph to obtain a delay space channel cluster candidate set;

[0058] Specifically, image processing software, such as MATLAB, can be used to perform channel cluster morphological reconstruction. The initial multipath delay classification map is a two-dimensional delay-amplitude map that shows the signal strength under different time delays. First, the initial multipath delay classification map is converted into a grayscale image, where different grayscale values ​​represent different signal strengths. Morphological operations, such as dilation and erosion, are applied to highlight the cluster structure of the signal. A suitable structural element is selected, such as a 3x3 matrix with a central element of 1 and the rest of 0. This structural element is used to identify and connect adjacent signal clusters. The dilation operation enhances the boundaries of the signal clusters, while the erosion operation is used to eliminate noise and small signal branches. The combination of these operations can help identify the candidate set of delay space channel clusters, that is, a group of signal clusters belonging to the same physical path. Finally, the processed image is converted back to the form of a delay-amplitude map.

[0059] Step S136: locating the channel cluster boundary of the initial multipath delay classification map according to the delay space channel cluster candidate set to obtain a multipath channel cluster boundary map, and performing delay distribution mapping on the multipath channel cluster boundary map to obtain a reflection channel delay spectrum.

[0060] Specifically, the initial multipath delay classification map can be loaded into MATLAB, which shows the delay distribution of the signal. The image file of the delay classification map is read using the imread function of MATLAB and converted into a grayscale image. Next, the graythresh function in MATLAB is used to calculate the global threshold of the image, which is based on the Otsu method, which can automatically determine a threshold to separate the foreground and background of the image. The binarized image is labeled using the bwlabel function to identify different channel clusters. The bwboundaries function of MATLAB is used to find the boundary pixels of each labeled area. This boundary information is used to draw a multipath channel cluster boundary map on the original delay classification map, and the plot function is used to visually mark the boundaries of each channel cluster on the image. The delay distribution is mapped on the multipath channel cluster boundary map. The regionprops function of MATLAB is used to calculate the statistical characteristics of each channel cluster, including their delay center and distribution range. These delay values ​​are mapped to a new spectrum to generate a reflection channel delay spectrum. In this graph, the delay is the horizontal axis and the signal strength is the vertical axis, and the scatter function is used to display different channel clusters with different colors or marks.

[0061] The present invention significantly improves the accuracy of signal recognition, optimizes the signal processing strategy, enhances the time-varying adaptability of the signal, realizes refined channel classification, improves the accuracy of channel modeling, and improves signal coverage and quality by finely analyzing the secondary path reflection signal.

[0062] Preferably, step S2 comprises the following steps:

[0063] Step S21: performing feature decomposition on antenna channel feature data to obtain an orthogonal channel feature space graph;

[0064] Specifically, antenna channel characteristic data can be imported from the data acquisition system. These data include path attenuation characteristic data, propagation delay characteristic data, and frequency offset characteristic data. Use the svd (singular value decomposition) function in the SciPy library in Python to perform characteristic decomposition on these data. Organize the channel characteristic data into a matrix, and then apply SVD decomposition to obtain three matrices: the left singular vector matrix, the singular value matrix, and the right singular vector matrix. The left singular vector matrix represents the spatial characteristics of the signal, while the right singular vector matrix represents the temporal characteristics of the signal. By visualizing these matrices, an orthogonal channel characteristic space diagram can be obtained, which clearly shows the distribution of channel characteristics in space and time.

[0065] Step S22: Calculate the phase weight of each antenna element of the antenna array based on the orthogonal channel characteristic space graph to obtain an antenna element weight matrix;

[0066] Specifically, Python and the NumPy library can be used to determine the best response direction for each antenna element based on the orthogonal channel feature space graph. Use an optimization algorithm, such as gradient descent, to adjust the phase of each antenna element to maximize the received signal quality. Define an objective function that measures the signal quality under the current phase setting. Iteratively adjust the phase of each antenna element until the phase configuration that maximizes the objective function is found. This process involves a lot of linear algebra operations, and matrix operations in the NumPy library are used to perform these calculations efficiently. Finally, an antenna element weight matrix is ​​obtained, which contains the optimal phase weights for each antenna element.

[0067] Step S23: performing spatial scanning modeling on the antenna array to obtain the antenna phased array pattern, and performing beam synthesis on the antenna phased array pattern according to the antenna unit weighting matrix to obtain beam direction prediction data in the time-space domain;

[0068] Specifically, electromagnetic simulation software, such as ANSYS HFSS, can be used to simulate the spatial scanning of the antenna array. A three-dimensional model of the antenna array, including the geometry and material properties of the antenna, is established in HFSS. The scanning tool in the software is used to simulate the radiation pattern of the antenna array in different directions and generate the antenna phased array pattern. This pattern shows the radiation intensity of the antenna array in all directions. MATLAB is used to process the weighting matrix and perform beam synthesis. The pattern data obtained from HFSS is imported into MATLAB, and combined with the antenna unit weighting matrix, a numerical optimization method (such as genetic algorithm or particle swarm optimization) is used to adjust the weight of each antenna unit. Through iterative calculation, the phase and amplitude of the antenna unit are optimized so that the direction and shape of the synthesized beam meet the expected communication requirements. Finally, the spatiotemporal beam direction prediction data is obtained, which describes the distribution of the beam in space and time.

[0069] Step S24: performing beamforming constraints on the spatiotemporal domain beam direction prediction data to obtain an initial antenna beam pattern;

[0070] Specifically, professional wireless communication design software, such as MATLAB's Communication System Toolbox, can be used to perform beamforming constraints. The spatiotemporal beam direction prediction data is imported into MATLAB, and the beamforming objectives are defined, such as minimizing the sidelobe level, controlling the beam width, or optimizing the beam gain in a specific direction. Use MATLAB's built-in functions to set beamforming constraints, such as using wvtool (beam visualization tool) to intuitively adjust and observe the shape of the beam. By adjusting the parameters of the beamforming algorithm, such as weight coefficients and constraints, the direction and shape of the beam are controlled to meet specific communication requirements. For example, it is necessary to ensure that the beam is mainly pointed in the direction of the communication target, while suppressing radiation in other directions to reduce interference. After a series of adjustments and optimizations, the initial beam pattern of the antenna is finally obtained, which describes in detail the radiation characteristics of the beam in different directions.

[0071] Step S25: performing interference source location and feature extraction on the initial antenna beam pattern to obtain interference feature data;

[0072] Specifically, the data of the initial antenna beam pattern can be loaded in MATLAB, and the functions in its signal processing toolbox can be used to analyze the beam pattern. Beamforming technology and spatial spectrum estimation methods, such as the MUSIC (Multiple Signal Classification) algorithm, are used to identify and locate interference sources. In specific operations, USRP is used to capture signals in the environment around the portable WiFi, and then these signals are processed by the MUSIC algorithm in MATLAB to determine the direction of the interference source. The MUSIC algorithm identifies the location of the interference source by calculating the covariance matrix and eigenvalue decomposition of the signal. The direction of the interference source is determined based on the angle-power spectrum output by the algorithm, and its features, such as the frequency, amplitude, and time delay of the signal, are extracted to obtain interference feature data.

[0073] Step S26: calibrating parameters of a preset spatial filter based on the interference feature data to obtain an anti-interference filter coefficient set;

[0074] Specifically, a prototype of a spatial filter, such as an adaptive beamformer, can be designed based on the interference signature data. The initial parameters of the filter, such as the weight vector and constraints, are defined using the adaptive filter design toolbox of MATLAB. The interference signature data are used to calibrate these parameters to optimize the performance of the filter. Specifically, an algorithm based on the least mean square error (LMS) is used to adjust the weights of the filter so that it can maximize the suppression of the interference signal. The LMS algorithm is implemented in MATLAB, and the weights of the filter are iteratively updated using the interference signature data as input. Through multiple iterations, a set of optimal filter coefficients can be obtained, which can significantly reduce the impact of the interference signal. Finally, a set of anti-interference filter coefficients is obtained, which are used to construct the final filter.

[0075] Step S27: spatially filtering the antenna initial beam pattern according to the anti-interference filter coefficient set to obtain antenna optimized beam shape data;

[0076] Specifically, the data of the antenna's initial beam pattern can be imported into MATLAB and the set of anti-interference filter coefficients can be loaded. These coefficients are applied to each point of the beam pattern using the filter function in MATLAB. This function implements a digital filter that processes the signal according to the set of coefficients of the anti-interference filter to reduce the effects of interference. Each element of the beam pattern is treated as an independent signal, and the filter is then applied to adjust its amplitude and phase to suppress interference. The processed data forms the antenna's optimized beam morphology data, which reflects the filtered beam characteristics, including the pattern shape and radiation pattern after interference is suppressed.

[0077] Step S28: performing beam gain calculation on the antenna optimized beam shape data to obtain a beam power allocation matrix, and performing digital beam forming and parameter extraction on the beam power allocation matrix to obtain antenna beam configuration parameters.

[0078] Specifically, the antenna Toolbox in MATLAB can be used to calculate the beam gain of the antenna optimized beam shape data. This toolbox provides functions such as antennaArray and pattern, which can be used to simulate the radiation pattern of the antenna array and calculate the beam gain. The parameters of the antenna array are defined according to the antenna optimized beam shape data, and the beam gain in different directions is calculated using the pattern function. The antennaArray function is used to define the geometry and electrical characteristics of the antenna array, including the number, location, and type of antenna elements. The dbf (digital beamforming) technique is used to further optimize the shape and gain of the beam. The dbf function in MATLAB is used to adjust the weight of each antenna element to achieve the desired beam shape. This process involves calculating the beam power allocation matrix, which describes the contribution of each antenna element to the total beam power. The wbf (weighted beamforming) algorithm is used to calculate the optimal weight allocation. This algorithm takes into account the direction of the beam and the required gain, as well as the geometry of the antenna array. By applying these weights, the beam power allocation matrix can be obtained, which guides how to allocate power to achieve the optimal beam performance. Finally, MATLAB's dbf function is used to perform digital beamforming and parameter extraction to obtain antenna beam configuration parameters, which include the phase and amplitude settings of each antenna element.

[0079] The present invention improves the accuracy of beamforming through feature decomposition, optimizes the beam synthesis effect, and obtains an antenna initial beam pattern that better meets actual needs by applying beamforming constraints to the spatiotemporal beam direction prediction data. The anti-interference capability is significantly improved through interference source positioning and feature extraction and anti-interference filter parameter calibration, ensuring the communication quality. The antenna beam morphology is further optimized through spatial filtering processing, signal interference is reduced, and transmission efficiency and quality are improved. The beam power allocation is more reasonable through beam gain calculation and beam power allocation matrix optimization, which improves energy utilization efficiency. The flexibility and accuracy of beamforming are enhanced through the application of digital beamforming technology, so that the beam can better adapt to different communication needs.

[0080] Preferably, step S3 comprises the following steps:

[0081] Step S31: Modeling the device signal coverage of the portable WiFi based on the antenna beam configuration parameters to obtain the initial coverage range data of the WiFi signal;

[0082] Specifically, antenna beam configuration parameters, including the direction, width, and gain of the beam, can be input into the iBwaveDesign software. Then, the layout of the building, material properties, and other factors that affect signal propagation are defined. The software uses this information to simulate signal propagation, including direct, reflected, refraction, and scattering. The signal strength distribution data is calculated using the software's built-in path loss model, such as the Okumura-Hata model. This model takes into account the height of the base station, the height of the mobile station, the distance, and the characteristics of the urban environment. The software automatically generates a signal strength distribution map showing the signal strength at different locations. It is also necessary to define the transmit power and frequency of the portable WiFi device, based on which the software calculates the coverage radius data, that is, the distance at which the signal strength drops to a specific threshold (for example, -75 dBm). The software also simulates interference from other wireless devices and generates interference strength data. Through these simulations, the initial coverage range data of the WiFi signal can be obtained, including signal strength distribution data, coverage radius data, and interference strength data.

[0083] Step S32: performing node density clustering on the initial coverage data of the WiFi signal to obtain a hierarchical node clustering distribution map;

[0084] Specifically, the signal strength distribution data, coverage radius data, and interference strength data can be imported into ArcGIS, and a geographic database is created. Use ArcGIS's clustering analysis tools, such as DBSCAN (Density-Based Spatial Clustering of Applications with Noise), to cluster these data. DBSCAN is a density-based clustering algorithm that identifies clusters based on the density of data points, rather than a predefined number of clusters or a fixed radius. Set the parameters of the DBSCAN algorithm, such as neighborhood size and minimum number of points, to suit the data analysis needs. The algorithm automatically groups data points into different clusters, each representing an area with similar signal characteristics. After clustering is complete, use ArcGIS's visualization tools to create a hierarchical node cluster distribution map. This distribution map shows the boundaries and centers of different clusters, as well as the characteristics of each cluster, such as signal strength and interference level. You can also adjust the style and symbols of the map to clearly distinguish different clusters and levels.

[0085] Step S33: Calculate the node connectivity of the portable WiFi cluster based on the hierarchical node cluster distribution diagram to obtain the portable WiFi node connectivity matrix, and reconstruct the hierarchical structure of the portable WiFi node connectivity matrix to obtain the WiFi self-organizing network hierarchical division diagram;

[0086] Specifically, the nodes in the hierarchical node cluster distribution map can be imported into Gephi (network analysis software) as vertices in the network, and edges are defined based on the signal coverage overlap and physical distance between nodes. The edge weight function of Gephi is used to represent the connectivity strength between nodes, and the weight is based on the signal strength and interference level. The community detection algorithm in Gephi, such as the Louvain method, is used to identify community structures in the network, which represent groups of nodes with high connectivity. The algorithm outputs a node connectivity matrix, which contains the connectivity weights between each pair of nodes. The node connectivity matrix is ​​hierarchically reconstructed to obtain the hierarchical partitioning diagram of the WiFi self-organizing network. In this process, the hierarchical clustering function of Gephi is used, which groups nodes into different levels according to their connectivity. Each layer represents a logical level in the network, for example, the top layer contains core nodes, and the bottom layer contains edge nodes. Through this process, a hierarchical partitioning diagram of the WiFi self-organizing network can be obtained, which shows the hierarchical structure and relative positions of nodes in the network.

[0087] Step S34: assigning roles to the portable WiFi cluster according to the WiFi self-organizing network hierarchy division diagram to obtain a portable WiFi node distribution diagram;

[0088] Specifically, three roles can be defined: core nodes, relay nodes, and edge nodes. Core nodes are those nodes located in the center of the network with high connectivity; relay nodes are those nodes that connect core nodes and edge nodes; and edge nodes are those nodes located at the edge of the network with low connectivity. Use Gephi's attribute table function to assign roles to each node. Nodes in the WiFi self-organizing network hierarchical partitioning diagram are classified into different roles based on indicators such as the node's degree (i.e., the number of connected edges), betweenness centrality (i.e., the frequency of the node as a bridge in the network), and closeness centrality (i.e., the average distance from the node to all other nodes in the network). For example, nodes with high degree and betweenness centrality are marked as core nodes, while nodes with high closeness centrality are marked as edge nodes. After completing the role assignment, a portable WiFi node distribution map is generated, which includes core node data, relay node data, and edge node data. This distribution map shows the location and role of each node.

[0089] Step S35: performing an improved on-demand routing calculation on the portable WiFi node distribution map to obtain an initial routing topology structure;

[0090] Specifically, please refer to the sub-steps of step S35 for the detailed implementation process of this embodiment.

[0091] Step S36: Evaluate the link redundancy of the initial routing topology structure to obtain a redundant link candidate set, and construct a backup path based on the redundant link candidate set to obtain a WiFi-level mesh routing graph.

[0092] Specifically, you can use network analysis and simulation tools, such as NS-3 (Network Simulator 3), which is an open source network simulation environment that allows detailed simulation and analysis of network topology and routing algorithms. Import the configuration file of the initial routing topology structure into NS-3. This file contains the connection information and link properties of all nodes in the network. Use NS-3's visualization tools, such as NetAnim, to visually view the network topology and identify critical links and potential redundant paths. Use link state algorithms in NS-3, such as OSPF (Open Shortest Path First), to evaluate the redundancy of each link in the network. The OSPF algorithm can calculate the shortest path between each pair of nodes and identify backup paths. Configure the OSPF algorithm to collect status information of all links in the network and calculate the usage frequency and load of each link. Based on this information, identify those links with high redundancy, that is, those links that can be used as backups when the primary path fails. Use NS-3's scripting function to define a redundant link candidate set, which contains all links identified as having high redundancy. Construct backup paths based on this candidate set. Write a program to simulate link failures and automatically recalculate routes to restore connectivity using redundant links. This program simulates multiple failure scenarios and generates the best backup paths for each scenario. Finally, use the output function of NS-3 to generate a WiFi hierarchical mesh routing diagram that shows the primary and backup paths. Set different colors and line types to distinguish between primary and backup paths, as well as different levels of link redundancy.

[0093] The present invention provides detailed WiFi signal coverage data for network planning and optimization through signal coverage modeling, including signal strength, coverage radius and interference strength, so as to achieve optimized node layout, identify network hotspots and coverage blind spots. By enhancing network connectivity and clear node role allocation, the stability and reliability of the network are improved, and resource allocation and traffic management are optimized. The fault tolerance and anti-interference of the network are further improved through improved routing calculation and link redundancy evaluation, while the construction of an efficient hierarchical mesh routing diagram improves data transmission speed and load balancing, and enhances the throughput and efficiency of the network.

[0094] Preferably, step S35 includes the following steps:

[0095] Step S351: performing routing request broadcast calculation on the portable WiFi node distribution map to obtain a routing request propagation map;

[0096] Specifically, you can use a network simulation tool, such as GNS3 (Graphical Network Simulator 3), which allows you to simulate real network devices and protocol behaviors. Import the portable WiFi node distribution map into GNS3 and configure the parameters of each node, including the device's IP address, subnet mask, and other network configuration information. Use the routing protocol function of GNS3 to simulate the broadcast process of route requests. Select OSPF (Open Shortest Path First) as the routing protocol because it can handle route request broadcasts in large-scale networks. In GNS3, configure OSPF protocol parameters, including area ID, cost value, and Hello / Dead interval. Then, start the route request broadcast process. The OSPF protocol automatically calculates the shortest path from each node to all other nodes and broadcasts the routing information in the network. As the route request propagates, GNS3 records the path and time of the route request and generates a route request propagation graph. This graph shows the propagation process of the route request from the source node to the destination node, including the nodes and links passed through.

[0097] Step S352: performing weighted hop calculation on the wireless terminal based on the routing request propagation graph to obtain a network hop weight matrix;

[0098] Specifically, the data of the routing request propagation graph can be imported into Python, and the network graph can be constructed using the NetworkX library. NetworkX is a powerful network analysis tool that allows the creation and manipulation of complex network structures. Use the shortest_path_length function of NetworkX to calculate the shortest path length, i.e., the number of hops, between each pair of nodes in the network. This function returns a dictionary containing the number of hops between each pair of nodes. Use the Pandas library to create a hop weight matrix, which is a two-dimensional array in which each element represents the number of hops between the corresponding node pair. Define a weight function that assigns weights based on the number of hops. For example, a simple linear weight function can be defined in which the fewer the hops, the greater the weight. The weight assignment can also be adjusted by considering the load of the network and the link quality. Using this weight function, the weighted hops of each node pair are calculated, and the hop weight matrix is ​​updated. Finally, the Python Matplotlib library is used to visualize the hop weight matrix. Through this process, a detailed network hop weight matrix can be obtained.

[0099] Step S353: performing link quality assessment on the portable WiFi cluster according to the portable WiFi node distribution map to obtain link status assessment data;

[0100] Specifically, Wireshark can be deployed on each portable WiFi node to capture wireless communication packets, which contain information such as signal strength, signal-to-noise ratio (SNR), and transmission rate. Use Wi-Fi Analyzer to perform signal mapping on each node to collect data on signal coverage and interference levels. This data is aggregated into a central database and analyzed using a Python script. In the script, a series of link quality indicators are defined, including signal strength threshold, signal-to-noise ratio threshold, and packet loss rate. Each link is scored based on these indicators, and link status evaluation data is generated. For example, if the signal strength is higher than a preset threshold, the signal-to-noise ratio is higher than a certain standard, and the packet loss rate is lower than a certain percentage, the link is rated as high quality. The data is presented in a table, with each link having a corresponding quality score.

[0101] Step S354: performing comprehensive routing metric calculation based on the network hop weight matrix and link state evaluation data to obtain a hierarchical routing priority table;

[0102] Specifically, the network hop weight matrix and link status evaluation data can be imported into a network management platform, such as Cisco's Network Assistant, and a comprehensive routing metric model is created in Excel. In Excel, a formula is used to combine the hop weight and link quality score. For example, a comprehensive score is calculated for each link, which is the weighted sum of the hop weight and the link quality score. Different weights are assigned to the hop count and link quality to reflect their relative importance in routing decisions. For example, a higher weight is given to link quality because it directly affects the reliability of data transmission. All routing paths are sorted according to the comprehensive score, and a hierarchical routing priority table is generated. This table lists all paths from the source node to the destination node, as well as their priority scores. The higher the priority score of a path, the more priority the path is given in routing decisions. This table shows which routing paths are optimal and which are alternatives under the current network status.

[0103] Step S355: performing path selection on the hierarchical routing priority table to obtain an optimal routing path set, and generating a routing table based on the optimal routing path set to obtain an initial routing topology structure.

[0104] Specifically, you can use network routing management software, such as Quagga, which is an open source routing software suite that supports multiple routing protocols. Import the hierarchical routing priority table into Quagga and configure BGP (Border Gateway Protocol) as the routing protocol because it can handle path selection and route aggregation in large-scale networks. In Quagga, set the path selection policy, which includes giving priority to the path with the highest priority score while considering the diversity of the path to avoid single points of failure. Using Quagga's path selection algorithm, factors such as the priority score of the path, the number of hops of the path, the bandwidth and reliability of the link are combined. The algorithm automatically evaluates all potential paths and selects the path with the highest score as the optimal path. After completing the path selection, you can get the optimal routing path set, which is the best choice for data transmission. Use Quagga's routing table generation function to convert the optimal routing path set into a routing table. This routing table contains the next hop information and path priority of each node in the network. Next, use a network configuration and management system, such as Ansible, to automate the deployment process of the routing table. Ansible is an automation tool that allows scripts (playbooks) to be written to configure network devices. Write a playbook that defines how to push the routing table to each device in the network. In the playbook, use Ansible's template function to dynamically generate configuration files for each device, which contain detailed information about the routing table. Run the Ansible playbook, which automatically logs into each network device and applies the new routing table to the device's operating system. This process ensures that all devices in the network have updated their routing information, resulting in consistent and accurate routing decisions. Through this process, an initial routing topology can be obtained, which reflects the current optimal routing paths and device configurations in the network.

[0105] The present invention realizes rapid response to node distribution graph and rapid processing of routing requests through efficient routing request processing, thereby improving the processing efficiency of routing requests. Through accurate evaluation of hop weighting and link quality, the communication cost and link status between wireless terminals can be more accurately evaluated. The advantages and disadvantages of routing are further evaluated from multiple dimensions through comprehensive routing metric calculation, thereby improving the accuracy of routing selection. Through optimized routing selection and dynamic routing table generation, the network can flexibly adapt to environmental changes and traffic fluctuations, thereby improving the adaptability and stability of the network.

[0106] Preferably, step S4 comprises the following steps:

[0107] Step S41: Scan the dual-frequency channels of the portable WiFi for channel idleness according to the WiFi hierarchical mesh routing diagram to obtain dual-frequency channel occupancy rate data;

[0108] Specifically, wireless network analysis tools, such as inSSIDer or Wireshark, can be used to perform channel idleness scans. These tools are deployed on each portable WiFi node in the network to collect data on wireless channels. The inSSIDer software is configured to scan channels on the 2.4GHz and 5GHz frequency bands and record the signal activity on each channel. During the scan, the software measures the signal strength and idle time on each channel to calculate the channel idleness. Set the scan time interval and duration to ensure that the collected data can represent the channel usage of the network in different time periods. By analyzing this data, dual-band channel occupancy data can be obtained, which includes the percentage of idle time and occupied time for each channel.

[0109] Step S42: performing interference measurement on the portable WiFi based on the dual-frequency channel occupancy data to obtain a dual-frequency channel electromagnetic interference spectrum, and performing signal-to-noise ratio evaluation on the dual-frequency channel electromagnetic interference spectrum to obtain a dual-frequency channel quality indicator set;

[0110] Specifically, the R&S FSH8 spectrum analyzer, a high-performance wireless signal analysis tool, can be used. The spectrum analyzer is deployed at key locations in the portable WiFi cluster network, including the network center and edge nodes, to ensure comprehensive coverage and capture interference conditions throughout the network. Configure the spectrum analyzer to scan dual-band channels, paying special attention to those channels marked as high occupancy based on the dual-band channel occupancy data, because these channels are more likely to be interfered with. During the scan, the signal strength and interference level on each channel are recorded, with special attention to the noise level and interference signals from other wireless devices on those channels with high occupancy. By analyzing this data, a dual-band channel electromagnetic interference spectrum can be obtained, which shows in detail the total interference level on each channel, including the interference of highly occupied channels. Next, the signal-to-noise ratio (SNR) evaluation function of the spectrum analyzer is used to calculate the SNR of each channel. For channels with high occupancy, pay special attention to the changes in the SNR, because the signal quality on these channels directly affects the performance of the network. The SNR is an important indicator for measuring signal quality, which reflects the ratio of signal strength to background noise. Based on these measurement results, a set of dual-frequency channel quality indicators can be obtained, which include the signal-to-noise ratio and interference level of each channel.

[0111] Step S43: classifying the status of the dual-frequency channel resources of the portable WiFi according to the dual-frequency channel quality indicator set to obtain a portable WiFi channel resource status table;

[0112] Specifically, you can import a set of dual-band channel quality indicators into NPM (SolarWinds Network PerformanceMonitor), which include the signal-to-noise ratio and interference level of each channel. Configure NPM's classification rules to classify channels into three categories based on the thresholds of the signal-to-noise ratio and interference level: available channels, candidate channels, and restricted channels. For example, set channels with a signal-to-noise ratio above a certain value and an interference level below a certain value as "available channels", channels with a signal-to-noise ratio and interference level in the medium range as "candidate channels", and channels with a low signal-to-noise ratio or high interference level as "restricted channels". NPM automatically classifies each channel according to these rules and generates a portable WiFi channel resource status table. This table includes the status classification of all channels, where the "available channel list" contains all channels classified as available, the "candidate channel list" contains all channels classified as alternatives, and the "restricted channel list" contains all channels classified as restricted.

[0113] Step S44: Identify the bandwidth demand of the portable WiFi according to the portable WiFi channel resource status table to obtain channel service demand data;

[0114] Specifically, Wireshark can be deployed on each portable WiFi node in the network. Wireshark is configured to monitor network traffic within a specific time period and record the packet size, transmission rate, and traffic type on each channel. According to the information in the portable WiFi channel resource status table, priority is given to monitoring channels marked as high interference or low quality, because these channels require more bandwidth resources to maintain service quality. At the same time, attention is also paid to channels with high bandwidth utilization in history, because these channels carry a large amount of business traffic. By analyzing the data collected by Wireshark, the business type and bandwidth requirements on each channel are identified. For example, it is found that there are more video streaming services on some channels, which indicates that these channels require higher bandwidth to support high-quality video transmission. For other channels, it is found that they are mainly text and email services, and the bandwidth requirements of these services are relatively low. Based on these analysis results, channel business demand data can be obtained, which not only includes the business type and traffic size on each channel, but also combines the channel quality information in the portable WiFi channel resource status table.

[0115] Step S45: performing bandwidth aggregation calculation on the portable WiFi according to the channel service demand data to obtain a portable WiFi channel combination strategy;

[0116] Specifically, channel service demand data can be imported into network planning and optimization tools, such as Cisco's Network Planning and Optimization Tool, which includes the service type, traffic size, and bandwidth requirements on each channel. The tool is configured to analyze this data and calculate the optimal bandwidth aggregation strategy based on service priority and bandwidth requirements. For example, high-priority services, such as video conferencing traffic, are assigned to channels with the highest available bandwidth, while low-priority services, such as email transmission, are assigned to channels with lower bandwidth. The tool provides an algorithm that considers the urgency and importance of service requirements, as well as the current load and quality of the channel, to recommend a channel combination strategy. Through this algorithm, the portable WiFi channel combination strategy can be obtained.

[0117] Step S46: Allocate resources for the portable WiFi channel combination strategy to obtain a portable WiFi bandwidth allocation plan;

[0118] Specifically, the channel combination policy can be imported into a wireless network management system, such as Ruckus ZoneDirector, and the bandwidth parameters of each channel are configured. Use the Zone Director's user interface to define the bandwidth limit and priority settings for each channel. For example, set a higher bandwidth cap for channels carrying video traffic and assign a higher priority to these channels. For channels carrying low-priority services, set a lower bandwidth cap to ensure that the bandwidth requirements of critical services are met. Zone Director automatically allocates bandwidth resources in the network based on these settings. It monitors the real-time performance of the network and dynamically adjusts bandwidth allocation in response to changes in network conditions. For example, if the load of a high-priority channel suddenly increases, Zone Director can automatically adjust the bandwidth allocation of other channels to ensure that the performance of critical services is not affected. Through this process, a portable WiFi bandwidth allocation plan can be obtained, which describes in detail the bandwidth parameters of each channel and the allocation of service traffic.

[0119] Step S47: performing time slot division calculation on the dual-frequency channel resources of the portable WiFi according to the portable WiFi bandwidth allocation plan to obtain a dual-frequency channel time slot allocation table;

[0120] Specifically, bandwidth allocation schemes can be imported into wireless network configuration tools, such as Ubiquiti's AirOS, which include bandwidth caps and service types for each channel. AirOS is configured to time-slot channels in the 2.4GHz and 5GHz bands. For example, it is decided to allocate different time slots to channels 1, 6, and 11 in the 2.4GHz band to avoid overlap and interference between channels. For the 5GHz band, channels 36, 40, 44, and 48 are selected and assigned different time slots. AirOS allows precise control of the working time and rest time of each channel to achieve time slot division. Use the time slot division function of AirOS to calculate the working time ratio of each channel, which is based on the bandwidth demand of each channel and the overall load of the network. For example, if a channel carries a lot of video traffic, it is allocated more working time. In this way, a dual-band channel time slot allocation table can be obtained, which details the time slot division of each channel.

[0121] Step S48: Perform QoS constraints on the dual-frequency channel time slot allocation table to obtain a portable WiFi resource scheduling strategy.

[0122] Specifically, the time slot allocation table can be imported into a network management platform, such as HP's Network Node Manageri (NNMi), and QoS parameters such as latency, jitter, and packet loss rate for different service types are defined. NNMi is configured to monitor network performance and prioritize service traffic based on QoS parameters. For example, the highest priority is set for real-time video conferencing traffic because it is very sensitive to latency and jitter. For services that are not very latency-sensitive, such as file transfer, a lower priority is set. NNMi allows specific QoS rules to be defined for each service type and automatically applies these rules to the time slot allocation table. Through NNMi's resource scheduling function, the time slot allocation table is adjusted according to the QoS rules to ensure that the performance requirements of key services are met. For example, more time slots are allocated to high-priority services, or additional bandwidth is provided to them when the network load is high. The final result is a portable WiFi resource scheduling policy, which details how channel resources are allocated between different service types to meet their QoS requirements.

[0123] The present invention improves channel utilization by scanning the idleness of dual-frequency channels, and provides an important basis for channel resource optimization through accurate interference and signal-to-noise ratio evaluation. Through the classification of channel quality indicator sets, available, alternative and restricted channels are clearly distinguished, thereby realizing the reasonable allocation and use of channel resources. By meeting the bandwidth requirements of key services according to bandwidth demand identification and bandwidth aggregation calculation, a flexible channel combination strategy is formulated to adapt to different network conditions and business requirements. Through efficient resource allocation and accurate time slot division calculation, the accuracy and efficiency of network performance and resource scheduling are further improved. The time slot allocation table with QoS constraints ensures the service quality of key services and meets the service requirements for delay and reliability.

[0124] Preferably, step S5 comprises the following steps:

[0125] Step S51: performing transmission flow feature identification on the portable WiFi according to the portable WiFi resource scheduling strategy to obtain a transmission data flow feature set;

[0126] Specifically, Wireshark can be deployed at each key node in the entire portable WiFi cluster network to capture the passing data packets. As a network protocol analyzer, Wireshark can record the attributes of each data packet in detail, including key information such as source address, destination address, port number, protocol type, etc. The captured data is exported as a PCAP file, which is the standard capture file format used by Wireshark. Python scripts are used in conjunction with the Pandas library to parse these PCAP files. In the script, specific functions are written to extract the service type, priority, and latency requirements of each data stream. For example, service types such as video streaming, voice calls, or file transfers are identified based on port numbers and protocol types. For priority data, the priority of each data stream is marked according to preset rules (such as DSCP values). For latency requirement data, the latency sensitivity of each data stream is determined based on the service type and network standard (such as the latency requirement of VoIP). In this process, special attention is paid to the priority and latency requirements defined in the portable WiFi resource scheduling policy to ensure that the analysis is consistent with the actual scheduling requirements of the network. For example, if the resource scheduling policy specifies that VoIP traffic requires the highest priority and the lowest latency, the script will mark these traffic accordingly and ensure that they are correctly classified in the feature set. Through these analyses, a transmission data flow feature set can be obtained, which contains business type data, priority data, and latency requirement data.

[0127] Step S52: constructing a distributed network layer coding rule based on the transmission data stream feature set to obtain a portable WiFi network coding rule table;

[0128] Specifically, the transmission data stream feature set can be imported into a network configuration tool, such as Cisco's PrimeInfrastructure, and encoding rule templates for different service types are defined in the user interface. For example, high-throughput encoding rules are set for video streams, low-latency encoding rules are set for voice calls, and efficient error correction encoding rules are set for file transfers. Python scripts and Scapy libraries are used to automate the encoding rule construction process. The script automatically selects the appropriate encoding rule template and generates specific encoding rules based on the service type, priority, and latency requirement data in the transmission data stream feature set. For example, for high-priority video streams, the script selects an encoding rule that includes header compression and error detection; for latency-sensitive voice calls, the script selects an encoding rule that includes fast forwarding and minimum delay. Through this process, a portable WiFi network encoding rule table can be obtained, which describes the encoding rules for each service type in detail.

[0129] Step S53: Generate a coefficient matrix according to the portable WiFi network coding rule table to obtain a portable WiFi initial coding matrix, and perform rank optimization calculation on the portable WiFi initial coding matrix to obtain a portable WiFi network coding coefficient set;

[0130] Specifically, the portable WiFi network coding rule table can be imported into mathematical modeling and optimization tools, such as MATLAB. These rule tables contain coding parameters and requirements corresponding to different service types. According to these rules, a function is defined that can generate an initial coefficient matrix based on the service type and network conditions. This matrix contains various parameters for network coding, such as coding rate, coding length, and check bits. Next, the MATLAB linear algebra toolbox is used to perform rank optimization calculations on the initial coding matrix. The goal is to find a matrix with the minimum rank that can meet the coding requirements of all service types while maintaining the robustness and efficiency of the network. The singular value decomposition (SVD) method is applied to reduce the rank of the matrix while maintaining its coding function. Through this process, the portable WiFi network coding coefficient set can be obtained.

[0131] Step S54: performing distributed coding operations on the portable WiFi cluster according to the portable WiFi network coding coefficient set to obtain data transmission coding data;

[0132] Specifically, the portable WiFi network coding coefficient sets can be imported into the Hadoop cluster, and these coefficient sets contain optimized coding parameters. Configure Hadoop's MapReduce programming model to process network coding tasks in parallel. The MapReduce model decomposes the big data processing task into two main steps: Map step and Reduce step. In the Map step, the coding coefficient set is applied to each data stream to generate the source data coding result and the relay coding result. Each Map task processes a part of the data and applies the corresponding coding rules to generate the encoded data blocks. In the Reduce step, these encoded data blocks are merged and decoding rule data is generated. These decoding rule data guide the receiving end on how to correctly decode and reorganize the original data. Through the distributed processing capabilities of the Hadoop cluster, the encoding operation of large-scale data is efficiently completed, and the data transmission encoding data is obtained. These data include the source data encoding result, the relay encoding result and the decoding rule data.

[0133] Step S55: Evaluate the error detection capability of the data transmission coded data to obtain a collaborative transmission error detection threshold set;

[0134] Specifically, the data transmission coding data can be imported into a communication system simulation tool, such as OPNET Modeler, and a simulation environment is set up that simulates the actual wireless communication channel, including various interferences and noises. The simulation parameters, such as signal-to-noise ratio (SNR), signal attenuation, and multipath effects, are configured to reflect the actual network conditions. During the simulation, different coding data are applied and the error rate during the transmission is monitored. The error detection capabilities of different coding schemes are evaluated using bit error rate (BER) and signal-to-noise ratio as the main performance indicators. By adjusting the coding parameters, such as code length and check bits, the effects of these changes on the bit error rate are observed, and the minimum coding strength required to achieve a specific bit error rate target at different signal-to-noise ratio levels is recorded. Through these simulation experiments, a set of cooperative transmission error detection thresholds can be obtained, which include the minimum coding parameters required to maintain data integrity under different network conditions.

[0135] Step S56: Perform forward error correction coding design based on the collaborative transmission error detection threshold set to obtain a portable WiFi error control strategy;

[0136] Specifically, the error detection threshold set can be imported into the coding design software, such as the Communication System Toolbox of MATLAB, and the appropriate FEC coding scheme is selected according to these thresholds. For example, convolutional codes, Turbo codes, or LDPC (Low Density Parity Check) codes are selected, which provide a good balance between different bit error rates and coding efficiency. These FEC codes are implemented using the coding design function of MATLAB, and simulation tests of coding performance are performed. In the test, the introduction of errors during data transmission is simulated, and the designed FEC code is applied to correct these errors. The error correction performance during encoding and decoding is monitored, especially the coding gain and residual bit error rate. Based on these test results, the FEC coding parameters, such as code rate and constraint length, are optimized to meet the requirements of the error detection threshold set. Finally, the portable WiFi error control strategies are obtained, which include the optimal FEC coding scheme and parameter settings for efficient error control and data protection in actual network environments.

[0137] Step S57: establishing a retransmission buffer pool for the data transmission coded data according to the portable WiFi error control strategy to obtain a data packet retransmission queue;

[0138] Specifically, the error control strategy can be imported into a network management platform, such as Zenoss Core, and the monitoring rules for data transmission encoding data are configured. These rules include packet loss rate, error rate and retransmission request. ZenossCore can monitor the data transmission in the network in real time and automatically mark the data packets that need to be retransmitted according to the preset error control strategy. Use the data processing function of Zenoss Core to establish a retransmission buffer pool, which temporarily stores all data packets that need to be retransmitted. For each data packet that needs to be retransmitted, its original sending time, reception confirmation status and number of retransmissions are recorded. Zenoss Core's database system is used to store this information and provides a user interface to manage and view the retransmission queue. Through this process, a packet retransmission queue can be obtained, which lists in detail all data packets to be retransmitted and their related information.

[0139] Step S58: Prioritize and schedule the data packet retransmission queue to obtain a WiFi reliable transmission control solution.

[0140] Specifically, the packet retransmission queue can be imported into a wireless network optimization tool, such as Riverbed SteelCentral Controller, and configured with priority sorting rules. These rules are based on the service type, priority, and latency requirements of the data packet. For example, voice and video traffic are set to high priority, while file transfer and email are set to low priority. SteelCentral Controller uses these rules to sort the packets in the retransmission queue and dynamically adjust the retransmission scheduling based on the real-time status of the network. For example, when the network load is low, it will prioritize high-priority packets; when the network load is high, it will balance the retransmission of high-priority and low-priority packets to avoid network congestion. Through this process, a WiFi reliable transmission control scheme can be obtained, which describes the retransmission priority and scheduling strategy of the data packet in detail.

[0141] The present invention realizes accurate control of different service types, priorities and delay requirements through transmission stream management, thereby improving the pertinence and efficiency of data transmission. Customized coding rules make the coding process more in line with actual transmission requirements, enhancing the adaptability and flexibility of data transmission. The reliability of data transmission is improved through optimized coding efficiency and enhanced data transmission robustness, especially in the face of channel changes and interference. The accuracy of data transmission is significantly improved and data loss is reduced through error detection and control capabilities and an effective data retransmission mechanism.

[0142] Preferably, the present invention further provides a data transmission system based on a portable WiFi network, which is used to execute the data transmission method based on a portable WiFi network as described above. The data transmission system based on a portable WiFi network comprises:

[0143] The channel feature extraction module is used to obtain initial multipath channel signal data; extract channel features from the initial multipath channel signal data to obtain antenna channel feature data;

[0144] The beam management module is used to perform phased array antenna beam calculation on antenna channel characteristic data to obtain the antenna initial beam pattern; perform spatial filter interference suppression on the antenna initial beam pattern to obtain antenna optimized beam shape data; perform digital beam shaping on the antenna optimized beam shape data to obtain antenna beam configuration parameters;

[0145] The network topology analysis module is used to perform network topology node analysis on antenna beam configuration parameters to obtain a portable WiFi node distribution map; perform improved on-demand routing calculation on the portable WiFi node distribution map to obtain an initial routing topology structure; perform redundant link optimization on the initial routing topology structure to obtain a WiFi hierarchical mesh routing map;

[0146] The resource scheduling management module is used to detect the status of the dual-frequency channel resources of the portable WiFi based on the WiFi hierarchical mesh routing diagram to obtain the portable WiFi channel resource status table; perform bandwidth aggregation calculation on the portable WiFi according to the portable WiFi channel resource status table to obtain the portable WiFi bandwidth allocation plan; perform time slot division on the dual-frequency data stream of the portable WiFi according to the portable WiFi bandwidth allocation plan to obtain the portable WiFi resource scheduling strategy;

[0147] The data transmission coding module is used to perform distributed network coding on the portable WiFi according to the portable WiFi resource scheduling strategy to obtain data transmission coding data; and to perform retransmission control on the portable WiFi according to the data transmission coding data to obtain a WiFi reliable transmission control solution.

[0148] Preferably, the present invention also provides a data transmission device based on a portable WiFi network, applying the data transmission system based on the portable WiFi network as described above, the data transmission device based on the portable WiFi network comprises a portable WiFi device body and a 18650 battery, a battery placement area is provided on the portable WiFi device body, and the 18650 battery is detachably arranged in the battery placement area.

[0149] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0150] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A data transmission method based on a portable WiFi network, characterized in that: The following steps are involved: Step S1: Acquire initial multipath channel signal data; extract channel characteristics from the initial multipath channel signal data to obtain antenna channel characteristic data; Step S2: performing phased array antenna beam calculation on antenna channel characteristic data to obtain an initial antenna beam pattern; performing spatial filter interference suppression on the initial antenna beam pattern to obtain antenna optimized beam shape data; performing digital beam forming on the antenna optimized beam shape data to obtain antenna beam configuration parameters; Step S3: Perform network topology node analysis on the antenna beam configuration parameters to obtain a portable WiFi node distribution map; Improve the on-demand routing calculation of the portable WiFi node distribution map to obtain the initial routing topology structure; Optimize the redundant links of the initial routing topology to obtain a WiFi-level mesh routing graph; Step S4: performing status detection on the dual-frequency channel resources of the portable WiFi based on the WiFi hierarchical mesh routing diagram to obtain a portable WiFi channel resource status table; performing bandwidth aggregation calculation on the portable WiFi according to the portable WiFi channel resource status table to obtain a portable WiFi bandwidth allocation plan; The dual-frequency data streams of the portable WiFi are divided into time slots according to the portable WiFi bandwidth allocation plan to obtain the portable WiFi resource scheduling strategy; Step S5: Perform distributed network coding on the portable WiFi according to the portable WiFi resource scheduling strategy to obtain data transmission coding data; perform retransmission control on the portable WiFi according to the data transmission coding data to obtain a WiFi reliable transmission control solution.

2. The data transmission method based on the portable WiFi network according to claim 1 is characterized in that: Step S1 includes the following steps: Step S11: deploying an antenna array for the portable WiFi cluster, and performing a multi-frequency channel scan on the environment where the portable WiFi cluster is located according to preset frequency band parameters to obtain initial multipath channel signal data; wherein the initial multipath channel signal data includes main path direct signal data, secondary path reflection signal data, and stray path scattered signal data; Step S12: performing path loss modeling on the main channel direct signal data to obtain a direct channel attenuation characteristic diagram; Step S13: performing multipath delay spread calculation on the secondary path reflection signal data to obtain a reflection channel delay spectrum; Step S14: performing Doppler frequency shift evaluation on the stray path scattering signal data to obtain a scattering channel frequency shift characteristic diagram; Step S15: performing channel correlation identification on the direct channel attenuation characteristic graph, the reflected channel delay spectrum and the scattered channel frequency shift characteristic graph to obtain a component channel mutual coupling matrix; Step S16: performing orthogonal transformation and fusion on the direct channel attenuation characteristic diagram, the reflected channel delay spectrum and the scattered channel frequency shift characteristic diagram according to the component channel mutual coupling matrix to obtain antenna channel characteristic data.

3. The data transmission method based on the portable WiFi network according to claim 2 is characterized in that: Step S13 includes the following steps: Step S131: performing multipath power delay evaluation on the secondary path reflection signal data to obtain an incoherent channel delay distribution diagram; Step S132: Calculate the delay threshold of the secondary path reflection signal data based on the incoherent channel delay distribution map to obtain a multipath delay segmentation threshold set; Step S133: extracting the channel impulse response of the secondary path reflection signal data to obtain non-static time-varying channel response data; Step S134: performing channel classification on the secondary path reflection signal data according to the multipath delay segmentation threshold set and the non-static time-varying channel response data to obtain an initial multipath delay classification diagram; Step S135: performing channel cluster morphological reconstruction on the initial multipath delay classification graph to obtain a delay space channel cluster candidate set; Step S136: locating the channel cluster boundary of the initial multipath delay classification map according to the delay space channel cluster candidate set to obtain a multipath channel cluster boundary map, and performing delay distribution mapping on the multipath channel cluster boundary map to obtain a reflection channel delay spectrum.

4. The data transmission method based on the portable WiFi network according to claim 1 is characterized in that: Step S2 includes the following steps: Step S21: performing feature decomposition on antenna channel feature data to obtain an orthogonal channel feature space graph; Step S22: Calculate the phase weight of each antenna element of the antenna array based on the orthogonal channel characteristic space graph to obtain an antenna element weight matrix; Step S23: performing spatial scanning modeling on the antenna array to obtain the antenna phased array pattern, and performing beam synthesis on the antenna phased array pattern according to the antenna unit weighting matrix to obtain beam direction prediction data in the time-space domain; Step S24: performing beamforming constraints on the spatiotemporal domain beam direction prediction data to obtain an initial antenna beam pattern; Step S25: performing interference source location and feature extraction on the initial antenna beam pattern to obtain interference feature data; Step S26: calibrating parameters of a preset spatial filter based on the interference feature data to obtain an anti-interference filter coefficient set; Step S27: spatially filtering the antenna initial beam pattern according to the anti-interference filter coefficient set to obtain antenna optimized beam shape data; Step S28: performing beam gain calculation on the antenna optimized beam shape data to obtain a beam power allocation matrix, and performing digital beam forming and parameter extraction on the beam power allocation matrix to obtain antenna beam configuration parameters.

5. The data transmission method based on the portable WiFi network according to claim 1 is characterized in that: Step S3 includes the following steps: Step S31: Modeling the device signal coverage of the portable WiFi based on the antenna beam configuration parameters to obtain the initial coverage range data of the WiFi signal; Step S32: performing node density clustering on the initial coverage data of the WiFi signal to obtain a hierarchical node clustering distribution map; Step S33: Calculate the node connectivity of the portable WiFi cluster based on the hierarchical node cluster distribution diagram to obtain the portable WiFi node connectivity matrix, and reconstruct the hierarchical structure of the portable WiFi node connectivity matrix to obtain the WiFi self-organizing network hierarchical division diagram; Step S34: assigning roles to the portable WiFi cluster according to the WiFi self-organizing network hierarchical division diagram to obtain a portable WiFi node distribution diagram; Step S35: performing an improved on-demand routing calculation on the portable WiFi node distribution map to obtain an initial routing topology structure; Step S36: Evaluate the link redundancy of the initial routing topology to obtain a redundant link candidate set, and construct a backup path based on the redundant link candidate set to obtain a WiFi-level mesh routing graph.

6. The data transmission method based on the portable WiFi network according to claim 5 is characterized in that: Step S35 includes the following steps: Step S351: performing routing request broadcast calculation on the portable WiFi node distribution map to obtain a routing request propagation map; Step S352: performing weighted hop calculation on the wireless terminal based on the routing request propagation graph to obtain a network hop weight matrix; Step S353: performing link quality assessment on the portable WiFi cluster according to the portable WiFi node distribution map to obtain link status assessment data; Step S354: performing comprehensive routing metric calculation based on the network hop weight matrix and link state evaluation data to obtain a hierarchical routing priority table; Step S355: performing path selection on the hierarchical routing priority table to obtain an optimal routing path set, and generating a routing table based on the optimal routing path set to obtain an initial routing topology structure.

7. The data transmission method based on the portable WiFi network according to claim 1 is characterized in that: Step S4 includes the following steps: Step S41: Scan the dual-frequency channels of the portable WiFi for channel idleness according to the WiFi hierarchical mesh routing diagram to obtain dual-frequency channel occupancy rate data; Step S42: performing interference measurement on the portable WiFi based on the dual-frequency channel occupancy data to obtain a dual-frequency channel electromagnetic interference spectrum, and performing signal-to-noise ratio evaluation on the dual-frequency channel electromagnetic interference spectrum to obtain a dual-frequency channel quality indicator set; Step S43: classifying the status of the dual-frequency channel resources of the portable WiFi according to the dual-frequency channel quality indicator set to obtain a portable WiFi channel resource status table; Step S44: Identify the bandwidth demand of the portable WiFi according to the portable WiFi channel resource status table to obtain channel service demand data; Step S45: performing bandwidth aggregation calculation on the portable WiFi according to the channel service demand data to obtain a portable WiFi channel combination strategy; Step S46: Allocate resources for the portable WiFi channel combination strategy to obtain a portable WiFi bandwidth allocation plan; Step S47: performing time slot division calculation on the dual-frequency channel resources of the portable WiFi according to the portable WiFi bandwidth allocation plan to obtain a dual-frequency channel time slot allocation table; Step S48: Perform QoS constraints on the dual-frequency channel time slot allocation table to obtain a portable WiFi resource scheduling strategy.

8. The data transmission method based on the portable WiFi network according to claim 1, characterized in that: Step S5 includes the following steps: Step S51: performing transmission flow feature identification on the portable WiFi according to the portable WiFi resource scheduling strategy to obtain a transmission data flow feature set; Step S52: constructing a distributed network layer coding rule based on the transmission data stream feature set to obtain a portable WiFi network coding rule table; Step S53: Generate a coefficient matrix according to the portable WiFi network coding rule table to obtain a portable WiFi initial coding matrix, and perform rank optimization calculation on the portable WiFi initial coding matrix to obtain a portable WiFi network coding coefficient set; Step S54: performing distributed coding operations on the portable WiFi cluster according to the portable WiFi network coding coefficient set to obtain data transmission coding data; Step S55: Evaluate the error detection capability of the data transmission coded data to obtain a collaborative transmission error detection threshold set; Step S56: Perform forward error correction coding design based on the collaborative transmission error detection threshold set to obtain a portable WiFi error control strategy; Step S57: establishing a retransmission buffer pool for the data transmission coded data according to the portable WiFi error control strategy to obtain a data packet retransmission queue; Step S58: Prioritize and schedule the data packet retransmission queue to obtain a WiFi reliable transmission control solution.

9. A data transmission system based on a portable WiFi network, characterized in that: Used to execute the data transmission method based on the portable WiFi network as claimed in claim 1, the data transmission system based on the portable WiFi network comprises: The channel feature extraction module is used to obtain initial multipath channel signal data; extract channel features from the initial multipath channel signal data to obtain antenna channel feature data; The beam management module is used to perform phased array antenna beam calculation on antenna channel characteristic data to obtain the antenna initial beam pattern; perform spatial filter interference suppression on the antenna initial beam pattern to obtain antenna optimized beam shape data; perform digital beam shaping on the antenna optimized beam shape data to obtain antenna beam configuration parameters; The network topology analysis module is used to perform network topology node analysis on antenna beam configuration parameters to obtain a portable WiFi node distribution map; perform improved on-demand routing calculation on the portable WiFi node distribution map to obtain an initial routing topology structure; perform redundant link optimization on the initial routing topology structure to obtain a WiFi hierarchical mesh routing map; The resource scheduling management module is used to detect the status of the dual-frequency channel resources of the portable WiFi based on the WiFi hierarchical mesh routing diagram to obtain the portable WiFi channel resource status table; perform bandwidth aggregation calculation on the portable WiFi according to the portable WiFi channel resource status table to obtain the portable WiFi bandwidth allocation plan; perform time slot division on the dual-frequency data stream of the portable WiFi according to the portable WiFi bandwidth allocation plan to obtain the portable WiFi resource scheduling strategy; The data transmission coding module is used to perform distributed network coding on the portable WiFi according to the portable WiFi resource scheduling strategy to obtain data transmission coding data; and to perform retransmission control on the portable WiFi according to the data transmission coding data to obtain a WiFi reliable transmission control solution.

10. A data transmission device based on a portable WiFi network, characterized in that: The data transmission system based on the portable WiFi network as described in claim 9 is applied, and the data transmission device based on the portable WiFi network includes a portable WiFi device body and a 18650 battery. A battery placement area is provided on the portable WiFi device body, and the 18650 battery is detachably arranged in the battery placement area.

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