Data transmission method, system, medium and device based on wifi6

Abstract

The present application relates to a wifi6-based data transmission method, system, medium and device, and relates to the field of data transmission, comprising: monitoring the signal strength parameter of a terminal device in a target environment for data transmission by a wifi6 device; when the signal strength parameter is less than a signal strength threshold, performing signal interference scene prediction according to the signal strength parameter to obtain a signal interference scene; classifying according to the signal interference scene to obtain a stay time and a transmission quality attention coefficient; performing signal enhancement transmission optimization according to the stay time and the transmission quality attention coefficient to obtain an enhanced transmission parameter, and performing enhanced data transmission on the terminal device within the stay time. The present application can solve the problem of being unable to dynamically adjust signal strength according to different use scenarios and poor anti-interference capability in traditional methods, and can adapt to the use requirements of user diversification scenarios.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of data transmission, in particular to a data transmission method, system, medium and device based on wifi6. BACKGROUND

[0002] With the development of communication technology, the popularization range of wifi6 devices is wider and wider, and at the same time, users have higher and higher requirements for the use experience of such devices.

[0003] Although the existing wifi6 technology can realize high-speed data transmission between different wireless devices, its penetration is weak, and the signal strength is insufficient when there is shielding, resulting in poor data transmission quality, and it cannot dynamically adjust the signal strength to adapt to the use requirements of users in diversified scenes according to different use scenarios. In different use scenarios, there is a technical problem of low data transmission quality. SUMMARY

[0004] The present application provides a data transmission method, system, medium and device based on wifi6 to solve the technical problem of low data transmission quality in different use scenarios in the traditional data transmission method based on wifi6.

[0005] The technical solution of the present application to solve the above technical problem is as follows:

[0006] In a first aspect, the present application provides a data transmission method based on wifi6, comprising: monitoring the signal strength parameter of a terminal device in a target environment for data transmission by a wifi6 device, wherein the terminal device transmits data with the wifi6 device; when the signal strength parameter is less than a signal strength threshold, performing signal interference scene prediction according to the signal strength parameter to obtain a signal interference scene; classifying the dwell time and transmission quality attention coefficient according to the signal interference scene to obtain the dwell time and transmission quality attention coefficient; performing signal enhancement transmission optimization according to the dwell time and transmission quality attention coefficient to obtain an enhanced transmission parameter, and performing enhanced data transmission on the terminal device within the dwell time.

[0007] In one embodiment, monitoring the signal strength parameter of a terminal device in a target environment for data transmission by a wifi6 device comprises:

[0008] In a target environment for data transmission by a wifi6 device, a terminal device connected with the wifi6 device is obtained.

[0009] The signal strength parameter of the terminal device for data transmission by the wifi6 device is monitored.

[0010] In an embodiment, when the signal strength parameter is less than the signal strength threshold, a signal interference scene is predicted according to the signal strength parameter, and the signal interference scene is obtained, including:

[0011] It is judged whether the signal strength parameter is less than the signal strength threshold, and if not, the monitoring judgment is continued;

[0012] If yes, the signal strength parameter is input into the pre-trained interference scene predictor to obtain the signal interference scene.

[0013] In an embodiment, the pre-training step of the interference scene predictor includes:

[0014] According to the data transmission record in the target environment in the historical time, a sample signal strength parameter set is collected, and the terminal device position under different sample signal strength parameters is collected and labeled as a sample signal interference scene to obtain a sample signal interference scene set;

[0015] A neural network is used to construct an interference scene predictor;

[0016] The sample signal strength parameter set and the sample signal interference scene set are used to supervise the training and accuracy test of the interference scene predictor, and the pre-training is completed after meeting the requirements.

[0017] In an embodiment, according to the signal interference scene, the stay time and the transmission quality attention coefficient are classified to obtain the stay time and the transmission quality attention coefficient, including:

[0018] According to the terminal device usage record in the target environment in the historical time, a sample signal interference scene set is collected;

[0019] The single average stay time of the terminal device in different sample signal interference scenes and the average operation times of the terminal device are collected to obtain a sample stay time set and a sample operation times set;

[0020] The ratio of each sample operation times to the average value of the sample operation times set is calculated as a sample transmission quality attention coefficient to obtain a sample transmission quality attention coefficient set;

[0021] A mapping relationship between the sample signal interference scene set and the sample stay time set and the sample transmission quality attention coefficient set is constructed to obtain an interference scene classifier;

[0022] The signal interference scene is input into the interference scene classifier to obtain the stay time and the transmission quality attention coefficient through mapping classification.

[0023] In an embodiment, according to the dwell time and the transmission quality attention coefficient, signal enhancement transmission optimization is performed to obtain enhancement transmission parameters, and enhanced data transmission is performed, including:

[0024] An enhancement transmission parameter space for the wifi6 device to perform enhanced data transmission is obtained, and a first enhancement transmission parameter is randomly generated;

[0025] An enhancement transmission function is constructed, as follows:

[0026]

[0027] wherein, HWF is a transmission fitness, the sum of w1 and w2 is 1, w1 and w2 are energy consumption weight and quality weight respectively, P y is a preset energy consumption of the wifi6 device, T is the dwell time, P is the actual energy consumption of the wifi6 device under the enhancement transmission parameter, K is the transmission quality attention coefficient, and R is the transmission error rate of data transmission between the wifi6 device and the terminal device under the enhancement transmission parameter;

[0028] According to the first enhancement transmission parameter, a first actual energy consumption and a first transmission error rate of the wifi6 device performing data transmission according to the first enhancement transmission parameter are analyzed and obtained;

[0029] According to the first actual energy consumption and the first transmission error rate, a first transmission fitness is calculated based on the enhancement transmission function;

[0030] The iteration optimization of the enhancement transmission parameter is continued by randomly generating the enhancement transmission parameter and calculating the transmission fitness, and after the optimization converges, the enhancement transmission parameter with the maximum transmission fitness is obtained, and the enhanced data transmission is performed on the terminal device within the dwell time.

[0031] In an embodiment, according to the first enhancement transmission parameter, a first actual energy consumption and a first transmission error rate of the wifi6 device performing data transmission according to the enhancement transmission parameter are analyzed and obtained, including:

[0032] According to the historical running data of the wifi6 device, a sample enhancement transmission parameter set and a sample signal strength parameter set are collected, and the average running energy consumption of the wifi6 device under different sample enhancement transmission parameters and the error rate of data transmission of the terminal device under different sample enhancement transmission parameters and sample signal strength parameters are collected, and a sample actual energy consumption set and a sample error rate set are labeled and obtained;

[0033] A mapping relationship between the sample enhancement transmission parameter set and the sample actual energy consumption set is constructed to obtain an energy consumption classification branch;

[0034] Based on deep learning, a transmission performance prediction branch is constructed, the sample enhanced transmission parameter set and the sample signal strength parameter set are used as input features, the sample bit error rate set is used as output features, supervised training and testing are carried out until the requirements are met.

[0035] The first enhanced transmission parameter is input into the energy consumption classification branch, and a first actual energy consumption is obtained by mapping.

[0036] The first enhanced transmission parameter and the signal strength parameter are input into the transmission performance prediction branch, and a first bit error rate is obtained by output. The second aspect of the present application provides a data transmission system based on wifi6, which is used to execute the data transmission method based on wifi6 in the first aspect, and the system comprises:

[0037] A terminal signal strength monitoring module is configured to monitor the signal strength parameter of a terminal device in a target environment for data transmission by a wifi6 device, wherein the terminal device performs data transmission with the wifi6 device.

[0038] A signal interference scene prediction module is configured to perform signal interference scene prediction according to the signal strength parameter when the signal strength parameter is less than a signal strength threshold, and obtain a signal interference scene.

[0039] A signal interference scene classification module is configured to classify the dwell time and the transmission quality attention coefficient according to the signal interference scene, and obtain the dwell time and the transmission quality attention coefficient.

[0040] A signal enhancement transmission optimization module is configured to perform signal enhancement transmission optimization according to the dwell time and the transmission quality attention coefficient, and obtain an enhanced transmission parameter, and perform enhanced data transmission on the terminal device within the dwell time.

[0041] The third aspect of the present application further provides a storage medium, wherein the storage medium stores a computer software program, and the computer software program is executed by a processor to realize the data transmission method based on wifi6 in the first aspect.

[0042] The fourth aspect of the present application further provides an electronic device, comprising:

[0043] A memory is configured to store a computer software program.

[0044] A processor is configured to read and execute the computer software program, and further realize the data transmission method based on wifi6 in the first aspect.

[0045] In the target environment of data transmission by the wifi6 device, the signal strength parameter of the terminal device is monitored, and the accurate start of signal enhancement transmission is carried out; when the signal strength parameter is less than the signal strength threshold, the signal interference scene is predicted according to the signal strength parameter, the signal interference scene is obtained, and the signal interference scene is classified to obtain the residence time and the transmission quality attention coefficient; according to the residence time and the transmission quality attention coefficient, the signal enhancement transmission optimization is carried out, the enhancement transmission parameter is obtained, and the terminal device is enhanced data transmission in the residence time, the targeted enhancement transmission in different scenes is realized, the anti-interference ability of signal transmission is greatly improved, and unnecessary power consumption is avoided while ensuring the signal transmission quality

[0046] In summary, the present application achieves the technical effect of improving the data transmission quality in different scenes and meeting the data transmission demand of diversified scenes of users. BRIEF DESCRIPTION OF DRAWINGS

[0047] Figure 1 The flowchart of the data transmission method based on wifi6 provided by the present application is shown in the figure;

[0048] Figure 2 The structure diagram of the data transmission system based on wifi6 provided by the present application is shown in the figure;

[0049] Figure 3 The schematic diagram of the storage medium provided by the present application is shown in the figure;

[0050] Figure 4 The structure diagram of the electronic device provided by the present application is shown in the figure.

[0051] In the drawings, the components represented by each number are described as follows:

[0052] The terminal signal strength monitoring module 11, the signal interference scene prediction module 12, the signal interference scene classification module 13, the signal enhancement transmission optimization module 14, the storage medium 30, the first computer program 31, the electronic device 40, the storage 41, the processor 42, and the second computer program 43. DETAILED DESCRIPTION

[0053] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0054] In the description of the present application, the terms "first", "second" are only for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined with "first", "second" can be explicitly or implicitly included one or more of the features. In the description of the present application, the meaning of "a plurality of" is two or more, unless otherwise specifically limited.

[0055] In the description of the present application, the term "for example" is used to indicate "as an example, illustration or description". Any embodiment described as "for example" in the present application is not necessarily interpreted as more preferred or more advantageous than other embodiments. The following description is given in order to enable any person skilled in the art to implement and use the present application. In the following description, details are listed for the purpose of explanation. It should be understood that those skilled in the art can realize the present application without using these specific details. In other examples, well-known structures and processes will not be described in detail to avoid unnecessary details making the description of the present application obscure. Therefore, the present application is not intended to be limited to the embodiments shown, but is consistent with the broadest scope in accordance with the principles and characteristics disclosed.

[0056] Embodiment one, as Figure 1 shown, the embodiment of the present application provides a data transmission method based on wifi6, which specifically includes the following steps:

[0057] S100: In the target environment for data transmission through the wifi6 device, the signal strength parameter of the terminal device is monitored, wherein the terminal device and the wifi6 device perform data transmission;

[0058] Through the wifi6 device, data transmission is performed in the target environment, and the signal strength of the terminal device is monitored, which aims to collect the signal strength values received by the terminal devices (such as mobile phones, household appliances, etc.) at different distances and different areas (such as living rooms, bedrooms, etc.) from the wifi6 device (such as wireless routers) in the use scenario.

[0059] Further, the step S100 of the present application further includes:

[0060] In the target environment for data transmission through the wifi6 device, the terminal device connected with the wifi6 device is acquired;

[0061] The signal strength parameter of the terminal device performing data transmission through the wifi6 device is monitored.

[0062] In the embodiments of the present application, to monitor the signal strength value received by the terminal device connected with the wifi6 device, the terminal device connected with the wifi6 device needs to be acquired first. Specifically, the AP (wireless controller) can send a Trigger frame (trigger frame) containing a BSRP (buffer status report request) to force the terminal device to feed back the BSR (buffer status report) on the specified RU (resource unit), so that the current active terminal list can be acquired accurately. The method for acquiring the terminal device is a prior art, and will not be described here.

[0063] Then, the signal strength parameter of the data transmission of the terminal device through the wifi6 device needs to be monitored. The parameter for measuring the signal strength can be the power (unit: watt), and the radio frequency power meter or the vector signal analyzer can be used. The power meter probe is connected to the terminal device antenna to capture the instantaneous power of the wifi6 signal received by the terminal device in real time as the signal strength data. Using the same method, the strength of the signal transmitted by the wifi6 device in real time can also be monitored.

[0064] S20: When the signal strength parameter is less than the signal strength threshold, the signal interference scene is predicted according to the signal strength parameter to obtain the signal interference scene.

[0065] In the embodiments of the present application, the signal interference scene is predicted because the strength of the wifi6 signal and the degree of interference are quite different in different scenes due to reasons such as wall obstruction and interference from other signal sources, and the demand for signal quality is also different in different scenes (such as bedroom, study, etc.). For example, the user generally has a higher demand for signal quality in the bedroom because the terminal device (such as a mobile phone) is frequently used, and the user generally has a lower demand for signal quality in the study because the terminal device is less frequently used. Therefore, in order to enhance the data transmission in a targeted manner, the scene where the terminal device is located needs to be divided and predicted.

[0066] Further, the step S200 of the present application further includes:

[0067] It is judged whether the signal strength parameter is less than the signal strength threshold. If not, the monitoring and judgment are continued. If yes, the signal strength parameter is input into the interference scene predictor with pre-training completed to predict the signal interference scene.

[0068] In the embodiments of the present application, it is judged whether the signal strength is less than the signal strength threshold, so as to enhance the data transmission in a targeted manner, and to reduce the power consumption and the device heating and aging to the greatest extent while ensuring the signal transmission quality. The signal strength threshold is the minimum value (such as 5W) of the signal strength, that is, the minimum signal strength value that enables the terminal device connected with the wifi6 device to normally receive the signal.

[0069] The standard of the "normal transmission signal" can be measured by the bit error rate. For example, the signal transmission is normal when the bit error rate is less than 1%. When the bit error rate of the data transmission between the WiFi 6 device and the connected terminal device is less than 1%, it can be determined that the terminal device can normally receive the signal. Further, the desirable minimum signal strength (such as 5W) when the bit error rate of the data transmission between the WiFi 6 device and the connected terminal device is less than 1% can be set as the threshold of the signal strength, that is, the desirable minimum signal strength under the condition of ensuring the signal transmission quality. The bit error rate is an index for measuring the accuracy of data transmission within a specified time, and the bit error rate = the number of errors in transmission / the total number of codes transmitted * 100%.

[0070] Through the signal strength monitoring method in S100, the signal strength parameter of the terminal device can be obtained, and the signal strength threshold can be determined. When the signal strength parameter, that is, the power, is greater than the set signal strength threshold, it indicates that the signal transmission quality, that is, the bit error rate, is within the normal range, and the monitoring and judgment can continue; when the signal strength parameter is less than the set signal strength threshold, it indicates that the signal transmission quality is poor, that is, the signal is seriously interfered, and the signal needs to be enhanced for transmission according to the specific interference situation to ensure the quality of signal transmission.

[0071] In this embodiment, the prediction of the signal interference situation is realized by the interference scene predictor. By inputting the signal strength parameter into the interference scene predictor, the prediction of the signal interference scene can be realized. The pre-training steps of the interference scene predictor include:

[0072] According to the data transmission records in the target environment in the historical time, a sample signal strength parameter set is collected, and the positions of the terminal devices under different sample signal strength parameters are collected and labeled as sample signal interference scenes to obtain a sample signal interference scene set;

[0073] A neural network is used to construct an interference scene predictor.

[0074] The sample signal strength parameter set and the sample signal interference scene set are used to supervise the training and accuracy test of the interference scene predictor, and the pre-training is completed after meeting the requirements.

[0075] In the embodiments of the present application, the training of the interference scene predictor is to predict the signal interference scene according to the input signal strength parameter, so the interference scene predictor needs to be trained with sample data obtained in the real use environment to ensure its applicability. The sample data includes sample signal strength parameters and sample signal interference scenes corresponding to the signal strength parameters. In the embodiments, the sample signal strength parameters are signal strength parameters of a terminal device in a target environment for data transmission by a wifi6 device; the terminal device position is a position of the terminal device for data transmission by the wifi6 device, such as a common living area like a bathroom, a conference room, a dining room, etc. Specifically, a plane space coordinate system can be constructed with the wifi6 device as the origin in the target scene to determine the position of the terminal device, i.e., the sampling point. The interval of each sampling point is 0.5 m, and 100 groups of signal strength parameters are collected at each sampling point each time (e.g., collected for 10 s at a collection frequency of 10 hz) as time series signal strength data, and labeled according to the coordinates corresponding to each sampling point, such as a bathroom, a conference room, a dining room, etc., as sample signal interference scenes. Multiple time series signal strength data are collected at each sampling point to obtain a sample signal interference scene set, which is divided into a test set and a validation set for subsequent training of the interference scene predictor.

[0076] Specifically, the input layer of the interference scene predictor is time series data of terminal device signal strength parameters; the output layer is a two-dimensional coordinate (x, y) matched with the time series data of the corresponding signal strength parameters and the corresponding interference scene.

[0077] In the embodiments of the present application, the key to constructing the interference scene predictor is to construct the mapping relationship between the time series data of the signal strength and the space coordinates. Specifically, the interference scene predictor can be built in the manner of TCN (Time Convolution Network) + LSTM (Long Short-Term Memory Network).

[0078] Among them, TCN uses causal convolution and dilated convolution to capture patterns in time, and performs better in processing long sequence data, especially in cases with long-term dependencies, for example, TCN can be used to extract macroscopic propagation patterns of signals (such as multipath effect period); and LSTM controls the flow of information through the gating mechanism, so it is good at processing short-term dependencies and dynamic changes in sequences, such as sudden signal fluctuations (such as human body occlusion transient). The combination of the two can achieve accurate prediction of the interference scene. The TCN module can be set to 3-6 layers of residual block layers for deep feature extraction. The number of hidden units of the LSTM module can be set to 128-256, and can be set to a 2-layer bidirectional structure.

[0079] Then, the model is supervised training using the sample signal strength parameter set and the sample signal interference scene set, in the training process of the model, the learning rate is set to 0.001, the optimizer uses Nadam, and the loss function selects Huber loss (delta = 1.0) to balance the influence of outliers. During training, the pre-trained TCN model (such as WaveNet) can be used to initialize the convolutional layer parameters to accelerate convergence; the initial forget gate bias of the LSTM can also be set to 1.0 to enhance long-term memory. The accuracy is used as the evaluation standard of the model, for example, the model training is qualified when the prediction accuracy is greater than 95%. When the accuracy reaches the specified standard, the pre-training is completed, and the interference scene predictor is obtained.

[0080] S300: According to the signal interference scene, the dwell time and the transmission quality attention coefficient are classified, and the dwell time and the transmission quality attention coefficient are obtained.

[0081] Further, the step S300 of the present application further comprises:

[0082] According to the terminal equipment use record in the target environment in the historical time, a sample signal interference scene set is collected;

[0083] The single average dwell time of the terminal equipment in different sample signal interference scenes and the average operation times of the terminal equipment are collected, and a sample dwell time set and a sample operation times set are obtained;

[0084] The ratio of each sample operation times to the mean value of the sample operation times set is calculated as a sample transmission quality attention coefficient, and a sample transmission quality attention coefficient set is obtained;

[0085] The mapping relationship between the sample signal interference scene set, the sample dwell time set and the sample transmission quality attention coefficient set is constructed, and an interference scene classifier (input interference scene, output dwell time and transmission quality attention coefficient) is obtained;

[0086] The signal interference scene is input into the interference scene classifier, and the dwell time and the transmission quality attention coefficient are obtained by mapping classification.

[0087] In the embodiments of the present application, the sample signal interference scene set is collected to determine the specific use scene of the terminal device. Then, by calculating the single average stay time and the average operation times of the terminal device in different signal interference scenes in the past period of time (such as 1 month), the demand degree of the user for the transmission quality in different signal interference scenes can be judged, and the attention coefficient of the transmission quality in different signal interference scenes can be obtained. Then, according to the mapping relationship between the sample signal interference scene set, the sample stay time set and the sample transmission quality attention coefficient set, an interference scene classifier can be built, and by inputting the signal interference scene, the stay time and the transmission quality attention coefficient can be predicted.

[0088] The sample signal interference scene set should include all the scenes that can be covered by the wifi6 accessible by the terminal device. For example, in a civil residence, it should include the kitchen, living room, bedroom, bathroom, study and other scenes that can be reached by the terminal device (such as mobile phone, computer, etc.) accessing wifi6.

[0089] The single average stay time of the terminal device in different sample signal interference scenes is the average value of the stay time of each terminal device entering the sample signal interference scene in the interference scene in a period of time (such as 1 day). For example, the kitchen is a signal interference scene, terminal device A enters the kitchen 3 times in a day, and the total stay time is 6h; terminal device B enters the kitchen 2 times, and the total stay time is 2h, then the single average stay time of the terminal device in the sample signal interference scene of the kitchen is (6h+2h) / (3 times+2 times) = 1.6h / time.

[0090] The average operation times of the terminal device in different sample signal interference scenes is the average value of the operation times of each terminal device entering the sample signal interference scene in the interference scene in a period of time (such as 1 day). For example, the kitchen is a signal interference scene, terminal device A operates 4 times in the kitchen in a day, terminal device B operates 3 times in the kitchen, and terminal device C operates 2 times in the kitchen, then the average operation times of the terminal device in the sample signal interference scene of the kitchen is (4+3+2) / 3 = 3 times.

[0091] In the embodiments of the present application, the ratio of the operation frequency of each sample to the average of the operation frequency of the sample set is calculated to determine the demand of the user for signal transmission quality in the sample signal interference scene. Obviously, the more frequently the user operates the terminal device in a certain interference scene, the more the user needs to use the terminal device for signal transmission in the interference scene, and therefore the transmission quality attention coefficient in the scene should be increased accordingly. The index for measuring the frequency of the user's operation of the terminal device in a certain interference scene is the ratio of the operation frequency of each sample to the average of the operation frequency of the sample set.

[0092] For example, the average of the operation frequency of the sample set in 1 day is 12 times, the operation frequency of sample A (such as a bedroom) is 24, and the operation frequency of sample B (such as a kitchen) is 6. For sample A, the sample transmission quality attention coefficient is 24 / 12 = 2; for sample B, the sample transmission quality attention coefficient is 6 / 12 = 0.5. Obviously, in this example, the quality attention coefficient of sample A with a larger operation frequency value is greater than that of sample B with a smaller operation frequency value. By collecting a plurality of such quality attention coefficients, a sample transmission quality attention coefficient set can be obtained.

[0093] Further, the mapping relationship between the signal interference scene set and the set of stay time and transmission quality attention coefficient can construct an interference scene classifier, and by inputting a sample signal interference scene, the stay time and transmission quality attention coefficient of the terminal device in the signal interference scene can be obtained.

[0094] Specifically, since the sample parameters are already available, the construction of the interference scene classifier mainly establishes a mapping relationship for extracting the stay time and transmission quality attention coefficient according to the interference scene. A relational database such as MySQL can be used to build it. Specifically, a table containing fields such as interference scene description, stay time, and transmission quality coefficient needs to be created using MySQL. The existing data is inserted into the database, and then a function is written using a programming language (such as Python) to query the database according to the input interference scene and extract the stay time and transmission quality coefficient. In this way, the mapping relationship between the signal interference scene set and the set of stay time and transmission quality attention coefficient can be realized. After the construction is completed, different interference scenes are tested to ensure that the query result is correct.

[0095] Further, since the interference scene predictor has been trained in the foregoing S200 step, the input signal strength parameter can output the corresponding interference scene; and by inputting the interference scene into the interference scene classifier built in this step, the stay time and transmission quality attention coefficient can be outputted to realize the targeted signal enhancement transmission optimization according to different signal strengths and scenes.

[0096] S400: according to the dwell time and the transmission quality concern coefficient, performing signal enhancement transmission optimization to obtain an enhanced transmission parameter, and performing enhanced data transmission on the terminal device within the dwell time.

[0097] Further, the step S400 of the present application further comprises:

[0098] obtaining an enhanced transmission parameter space of the wifi6 device for enhanced data transmission, and randomly generating a first enhanced transmission parameter;

[0099] constructing an enhanced transmission function as follows:

[0100]

[0101] wherein, HWF is a transmission fitness, the sum of w1 and w2 is 1, w1 and w2 are energy consumption weight and quality weight respectively, P y is a preset energy consumption of the wifi6 device, T is the dwell time, P is the actual energy consumption of the wifi6 device under the enhanced transmission parameter, K is the transmission quality concern coefficient, and R is the transmission error rate of data transmission between the wifi6 device and the terminal device under the enhanced transmission parameter.

[0102] In the embodiment of the present application, the signal enhancement transmission optimization is mainly realized by adjusting the enhanced transmission parameter, and in the embodiment, the signal transmission strength is adjusted, that is, the signal transmission power of the wifi6 device is adjusted. In order to balance the problem of energy consumption increase caused by power increase, the embodiment constructs an enhanced transmission function to calculate the fitness of the enhanced transmission parameter, and obtains the optimal enhanced transmission parameter to balance the quality and energy consumption of signal transmission, wherein the error rate is used as an index to measure the quality of signal transmission.

[0103] Firstly, the enhanced transmission parameter space is determined by the inherent parameters of the wifi6 device. For example, in the embodiment, the transmission is enhanced by increasing the transmission power, and the upper limit of the enhanced transmission parameter depends on the rated signal transmission power of the wifi6 device, such as 10W. The lower limit of the enhanced transmission parameter is the current real-time power, such as 5W. After obtaining the enhanced transmission parameter space, a plurality of first enhanced transmission parameters (such as 6W, 7W, 8W) are randomly generated in the enhanced transmission parameter space (such as 5-10W), and the transmission fitness thereof is calculated by the enhanced transmission function to screen out the optimal enhanced transmission parameter.

[0104] Secondly, the enhanced transmission function is used to calculate the transmission fitness of the wifi6 device under a certain enhanced transmission parameter, wherein the first term on the right side of the equation is the energy consumption fitness, and the increase of the enhanced transmission parameter will cause the increase of P (the actual energy consumption of the wifi6 device under the enhanced transmission parameter) in the formula, thereby causing the decrease of the energy consumption fitness, and vice versa; the second term on the right side of the equation is the transmission quality fitness, and the increase of the enhanced transmission parameter will cause the decrease of R (the transmission error rate of the data transmission between the wifi6 device and the terminal device under the enhanced transmission parameter) in the formula, thereby causing the increase of the quality fitness, and vice versa; the sum of the two terms on the right side of the equation is HWF (the transmission fitness). In the formula, T (the residence time) and K (the transmission quality attention coefficient) under a specific interference scene are constant values greater than 0, such as the residence time can be 4h and the transmission quality attention coefficient can be 0.8 under the kitchen scene, and the values of T and K will change with the change of the specific interference scene; the greater the value of T (the residence time) of an interference scene, the lower the energy consumption fitness; the greater the value of K (the transmission quality attention coefficient), the higher the transmission quality fitness; and vice versa.

[0105] The w1 and w2 are energy consumption weight and quality weight respectively, and the sum of the two is 1, which is used to adjust the weight of energy consumption and quality; if more attention is paid to energy consumption during data transmission, w1 can be set to be greater than w2, such as w1=0.7 and w2=0.3; if more attention is paid to transmission quality, w1 can be set to be less than w2, such as w1=0.4 and w2=0.6.

[0106] According to the first enhanced transmission parameter, the first actual energy consumption and the first transmission error rate of the wifi6 device during data transmission according to the first enhanced transmission parameter are analyzed and obtained. This includes:

[0107] According to the historical operation data of the wifi6 device, a sample enhanced transmission parameter set and a sample signal strength parameter set are collected, and the average running energy consumption of the wifi6 device under different sample enhanced transmission parameters and the error rate of the terminal device data transmission under different sample enhanced transmission parameters and sample signal strength parameters are collected, and the sample actual energy consumption set and the sample error rate set are labeled and obtained;

[0108] The mapping relationship between the sample enhanced transmission parameter set and the sample actual energy consumption set is constructed to obtain the energy consumption classification branch;

[0109] Based on deep learning, a transmission performance prediction branch is constructed, the sample enhanced transmission parameter set and the sample signal strength parameter set are used as input features, and the sample error rate set is used as output features, and supervised training and testing are performed until the requirements are met;

[0110] The first enhanced transmission parameter is input into the energy consumption classification branch to obtain the first actual energy consumption;

[0111] The first enhanced transmission parameter and the signal strength parameter are input into the transmission performance prediction branch, and a first bit error rate is output.

[0112] In the embodiment of the present application, after obtaining a plurality of random first enhanced transmission parameters according to the enhanced transmission parameter space, the transmission fitness of the wifi6 device under these first enhanced transmission parameters needs to be calculated to evaluate the enhanced transmission parameter. According to the foregoing enhanced transmission function, the transmission fitness needs to derive the actual energy consumption of the wifi6 device and the bit error rate of data transmission under the corresponding conditions, that is, the first actual energy consumption and the first transmission bit error rate. The actual energy consumption of the wifi6 device can be calculated by reading the real-time current and voltage, that is, W=UI; and the calculation method of the bit error rate is: bit error rate=error code in transmission / total code transmitted*100%.

[0113] Then, by collecting a plurality of sample enhanced parameters, a sample enhanced transmission parameter set can be obtained; by labeling the average energy consumption of the wifi6 device under each sample enhanced transmission parameter within a period of time (such as 24 hours), a sample actual energy consumption set can be obtained. By labeling the bit error rate of the terminal device data transmission under the sample enhanced transmission parameter and the sample signal strength parameter, a sample bit error rate set can be obtained. The above sample actual energy consumption set and sample bit error rate set are respectively used to build the energy consumption classification branch and the transmission performance prediction branch.

[0114] In the embodiment of the present application, the energy consumption classification branch mainly constructs the mapping relationship between the sample enhanced transmission parameter set and the sample actual energy consumption set, and can also be built using a relational database such as MySQL. Specifically, a table containing sample enhanced transmission parameters and sample actual energy consumption is created using MySQL, the existing data is inserted into the database, and then a function is written using a programming language (such as Python) to query the database according to the input enhanced transmission parameter and extract the actual energy consumption. The mapping relationship between the sample enhanced transmission parameter set and the sample actual energy consumption set can be realized. After the building is completed, different sample enhanced transmission parameters are tested to ensure that the query result is correct.

[0115] The first enhanced transmission parameter is input into the energy consumption classification branch, and the first actual energy consumption can be obtained.

[0116] In the embodiment of the present application, the transmission performance prediction branch mainly predicts the bit error rate in data transmission according to the enhanced transmission parameter and the signal strength parameter. The input features are the sample enhanced transmission parameter set and the sample signal strength parameter set, and the output features are the sample bit error rate set. Gradient Boosting Decision Tree (GBDT) can be used to build a transmission performance prediction model. GBDT is an iterative decision tree algorithm that constructs a group of weak learners (trees) and accumulates the results of multiple decision trees as the final prediction output.

[0117] Specifically, in the construction of the model, since the target is to predict the specific value of the error rate, a regression type gradient boosting tree can be selected. The total number of trees is initially set to about 200 (which can be adjusted during the training process); the complexity of each tree is controlled at a medium level (for example, each tree is divided into a maximum of 5 layers); 80% of the data is randomly selected each time for training to prevent over-reliance on specific samples. When more trees are added but the validation error no longer decreases, the training is terminated in advance to save time.

[0118] Further, during training, the sample enhanced transmission parameter set and the sample signal strength parameter set can be input, and the error rate predicted by the model is compared with the labeled true error rate to evaluate the training level of the model. The evaluation index of the model can be set as the average error, that is, the average gap between all predicted values and true values (for example, the average error of the predicted error rate is less than 0.1% to indicate that the prediction is basically accurate), and when the average gap between the predicted value and the true value is large, the model parameters are modified and the training is continued until the model converges to obtain the transmission performance prediction branch.

[0119] By inputting the first enhanced transmission parameter and the signal strength parameter into the transmission performance prediction branch, the first error rate can be obtained.

[0120] According to the first actual energy consumption, the first transmission error rate, and based on the enhanced transmission function, a first transmission fitness is calculated.

[0121] The enhanced transmission parameter is randomly generated and the transmission fitness is calculated, and the iteration optimization of the enhanced transmission parameter is performed. After the optimization converges, the enhanced transmission parameter with the maximum transmission fitness is obtained, and the enhanced data transmission is performed on the terminal device within the residence time.

[0122] Specifically, by inputting the first actual energy consumption (P) and the first transmission error rate (R) corresponding to a certain enhanced transmission parameter into the enhanced transmission function in a specific interference scenario (to obtain the T value and the K value), and defining the weights w1 and w2, the corresponding first transmission fitness can be calculated. By inputting a plurality of randomly generated enhanced transmission parameters into the enhanced transmission parameter according to the above method, the transmission fitness corresponding to the plurality of enhanced transmission parameter values can be obtained. To obtain the best enhanced transmission parameter, a large number of randomly generated enhanced transmission parameters need to be input for calculation, and after each calculation, the range of the input enhanced transmission parameter value is limited according to the distribution interval of the output transmission fitness, and the range of the enhanced transmission parameter value is continuously narrowed until the optimal enhanced transmission parameter is obtained.

[0123] Then the enhanced transmission parameter with the maximum transmission fitness is used for enhanced transmission when the corresponding terminal device stays in the interference scene. For example, the maximum enhanced transmission parameter of a terminal device (mobile phone) in a certain interference scene (such as a kitchen) is 7W (the transmission fitness is the largest when the power of the wifi6 device is 7W when transmitting data to the terminal device).

[0124] Using the enhanced transmission parameter with the maximum transmission fitness, the enhanced anti-interference capability and the transmission quality can be ensured, and the energy consumption can be reduced to reduce the device life and performance decline caused by excessive heating when the device is running at high power.

[0125] Embodiment two, as shown in Figure 2 Based on the same inventive concept of the wifi6-based data transmission method provided in embodiment one, the present embodiment also provides a wifi6-based data transmission system, which comprises:

[0126] A terminal signal strength monitoring module 11 is configured to monitor the signal strength parameter of a terminal device in a target environment for data transmission by a wifi6 device, wherein the terminal device transmits data with the wifi6 device.

[0127] A signal interference scene prediction module 12 is configured to, when the signal strength parameter is less than a signal strength threshold, perform signal interference scene prediction according to the signal strength parameter to obtain a signal interference scene.

[0128] A signal interference scene classification module 13 is configured to classify the signal interference scene according to the signal interference scene to obtain a stay time and a transmission quality attention coefficient.

[0129] A signal enhancement transmission optimization module 14 is configured to perform signal enhancement transmission optimization according to the stay time and the transmission quality attention coefficient to obtain an enhanced transmission parameter, and perform enhanced data transmission to the terminal device within the stay time.

[0130] In one embodiment, the terminal signal strength monitoring module 11 is further configured to:

[0131] In the target environment for data transmission by the wifi6 device, obtain a terminal device connected to the wifi6 device.

[0132] Monitor the signal strength parameter of the terminal device for data transmission by the wifi6 device.

[0133] In one embodiment, the signal interference scene prediction module 12 is further configured to:

[0134] Determine whether the signal strength parameter is less than a signal strength threshold, and if not, continue to monitor and determine.

[0135] If yes, the signal strength parameter is input into a pre-trained interference scenario predictor to obtain a predicted signal interference scenario.

[0136] The pre-training step of the interference scenario predictor comprises:

[0137] According to the data transmission record of the target environment in the historical time, a sample signal strength parameter set is collected, and the terminal device position under different sample signal strength parameters is collected and labeled as a sample signal interference scenario to obtain a sample signal interference scenario set;

[0138] A neural network is used to construct an interference scenario predictor.

[0139] The sample signal strength parameter set and the sample signal interference scenario set are used to supervise the training and accuracy test of the interference scenario predictor, and the pre-training is completed after meeting the requirements.

[0140] In one embodiment, the signal interference scenario classification module 13 is further configured to:

[0141] According to the terminal device usage record in the target environment in the historical time, a sample signal interference scenario set is collected;

[0142] The single average stay time of the terminal device in different sample signal interference scenarios and the average operation times of the terminal device are collected to obtain a sample stay time set and a sample operation times set;

[0143] The ratio of each sample operation times to the average value of the sample operation times set is calculated as a sample transmission quality attention coefficient to obtain a sample transmission quality attention coefficient set;

[0144] A mapping relationship between the sample signal interference scenario set and the sample stay time set and the sample transmission quality attention coefficient set is constructed to obtain an interference scenario classifier;

[0145] The signal interference scenario is input into the interference scenario classifier to obtain the stay time and the transmission quality attention coefficient through mapping and classification.

[0146] In one embodiment, the signal enhancement transmission optimization module 14 is further configured to:

[0147] An enhanced transmission parameter space for enhanced data transmission of a wifi6 device is obtained, and a first enhanced transmission parameter is randomly generated.

[0148] An embedded enhanced transmission function is as follows:

[0149]

[0150] Wherein, HWF is the transmission fitness, the sum of w1 and w2 is 1, w1 and w2 are energy consumption weight and quality weight respectively, P y is the preset energy consumption of the wifi6 device, T is the stay time, P is the actual energy consumption of the wifi6 device under the enhanced transmission parameter, K is the transmission quality attention coefficient, and R is the transmission error rate of the data transmission between the wifi6 device and the terminal device under the enhanced transmission parameter.

[0151] According to the first enhanced transmission parameter, a first actual energy consumption and a first transmission error rate of the wifi6 device performing data transmission according to the first enhanced transmission parameter are analyzed and obtained.

[0152] According to the first actual energy consumption and the first transmission error rate, a first transmission fitness is calculated and obtained based on the enhanced transmission function.

[0153] The enhanced transmission parameter is iteratively optimized by continuously randomly generating the enhanced transmission parameter and calculating the transmission fitness. After optimization convergence, the enhanced transmission parameter with the maximum transmission fitness is obtained, and enhanced data transmission is performed on the terminal device within the stay time.

[0154] Embodiment three, as Figure 3 shown, based on the same inventive concept of the wifi6-based data transmission method provided in embodiment one, the present embodiment also provides a readable storage medium, wherein the storage medium stores a computer software program, and the computer software program is executed by a processor to realize the wifi6-based data transmission method as described in embodiment one.

[0155] The storage medium refers to a storage carrier capable of saving data, including but not limited to SSD (solid state disk), HDD (mechanical hard disk), and flash memory device (U disk, memory card), etc.

[0156] Embodiment four, as Figure 4 shown, based on the same inventive concept of the wifi6-based data transmission method provided in embodiment one, the present embodiment also provides an electronic device, which comprises:

[0157] a memory for storing a computer software program;

[0158] a processor for reading and executing the computer software program, thereby realizing the wifi6-based data transmission method as described in embodiment one.

[0159] The memory refers to a device in a computer for temporarily storing programs and data being run, including but not limited to random access memory (RAM), read-only memory (ROM), virtual memory, etc.

[0160] The processor is a component in a computer responsible for executing program instructions, processing data, and controlling the operation of the computer, including but not limited to general-purpose processors (General-Purpose Processor), graphics processing units (Graphics Processing Unit), embedded processors (Embedded Processor), etc.

Claims

1. A data transmission method based on Wi-Fi 6, characterized in that, The method includes: Within the target environment where data transmission is conducted via a Wi-Fi 6 device, the signal strength parameters of the terminal device are monitored, wherein the terminal device transmits data with the Wi-Fi 6 device. When the signal strength parameter is less than the signal strength threshold, the signal interference scenario is predicted based on the signal strength parameter to obtain the signal interference scenario, wherein the signal interference scenario is a living area scenario. Based on the aforementioned signal interference scenarios, the dwell time and transmission quality concern coefficient are classified to obtain the dwell time and transmission quality concern coefficient, including: Based on the usage records of terminal devices in the target environment over a historical period, a set of sample signal interference scenarios is collected; Collect the average dwell time of terminal devices in different sample signal interference scenarios, as well as the average number of operations of terminal devices, to obtain the sample dwell time set and the sample operation number set; Calculate the ratio of the number of operations for each sample to the mean of the set of number of operations for the samples, and use it as the sample transmission quality attention coefficient to obtain the set of sample transmission quality attention coefficients. Construct a mapping relationship between the sample signal interference scenario set, the sample dwell time set, and the sample transmission quality attention coefficient set to obtain an interference scenario classifier; The signal interference scenario is input into the interference scenario classifier, and the dwell time and transmission quality attention coefficient are obtained by mapping and classification. Based on the dwell time and transmission quality concern coefficient, signal enhancement transmission optimization is performed to obtain enhanced transmission parameters. Enhanced data transmission is then performed on the terminal device during the dwell time, including: Based on the dwell time and transmission quality concern coefficient, signal enhancement transmission optimization is performed to obtain enhanced transmission parameters, and enhanced data transmission is then performed, including: Obtain the enhanced transmission parameter space for enhanced data transmission of Wi-Fi 6 devices, and randomly generate the first enhanced transmission parameter; Construct the enhanced transfer function as follows: ； Where HWF is the transmission fitness, w1 and w2 are summed to 1, representing the energy consumption weight and quality weight respectively, Py is the preset energy consumption of the Wi-Fi 6 device, T is the dwell time, P is the actual energy consumption of the Wi-Fi 6 device under enhanced transmission parameters, K is the transmission quality concern coefficient, and R is the transmission error rate of data transmission between the Wi-Fi 6 device and the terminal device under enhanced transmission parameters. Based on the first enhanced transmission parameters, analyze and obtain the first actual energy consumption and the first transmission error rate of the Wi-Fi 6 device when transmitting data according to the first enhanced transmission parameters. Based on the first actual energy consumption and the first transmission error rate, and based on the enhanced transmission function, the first transmission fitness is calculated. Continue to randomly generate enhanced transmission parameters and calculate transmission fitness, perform iterative optimization of enhanced transmission parameters, and obtain the enhanced transmission parameters with the largest transmission fitness after optimization convergence, and perform enhanced data transmission on the terminal device during the dwell time.

2. The data transmission method based on Wi-Fi 6 according to claim 1, characterized in that, Within the target environment where data transmission is conducted via Wi-Fi 6 devices, monitor the signal strength parameters of the terminal devices, including: Within the target environment where data transmission is performed via Wi-Fi 6 devices, identify the terminal devices connected to the Wi-Fi 6 devices. Monitor the signal strength parameters of the terminal device transmitting data via Wi-Fi 6.

3. The data transmission method based on Wi-Fi 6 according to claim 1, characterized in that, When the signal strength parameter is less than the signal strength threshold, signal interference scenario prediction is performed based on the signal strength parameter to obtain the signal interference scenario, including: Determine whether the signal strength parameter is less than the signal strength threshold; if not, continue monitoring and determination. If so, the signal strength parameters are input into the pre-trained interference scenario predictor to predict the signal interference scenario.

4. The data transmission method based on Wi-Fi 6 according to claim 3, characterized in that, The pre-training steps of the interference scene predictor include: Based on the data transmission records in the target environment over a historical period, a set of sample signal strength parameters is collected, and the location of the terminal device under different sample signal strength parameters is collected and labeled as sample signal interference scenarios to obtain a set of sample signal interference scenarios. Construct a prediction tool for interference scenarios using neural networks; Using the set of sample signal strength parameters and the set of sample signal interference scenarios, the interference scenario predictor is subjected to supervised training and accuracy testing. Pre-training is completed after the requirements are met.

5. The data transmission method based on Wi-Fi 6 according to claim 1, characterized in that, Based on the first enhanced transmission parameters, analyze and obtain the first actual energy consumption and first transmission error rate of the Wi-Fi 6 device transmitting data according to the first enhanced transmission parameters, including: Based on the historical operating data of Wi-Fi 6 devices, we collect a set of sample enhanced transmission parameters and a set of sample signal strength parameters. We also collect the average operating energy consumption of Wi-Fi 6 devices under different sample enhanced transmission parameters, as well as the bit error rate of terminal device data transmission under different sample enhanced transmission parameters and sample signal strength parameters. We then label and obtain the set of sample actual energy consumption and the set of sample bit error rate. Construct a mapping relationship between the set of sample enhanced transmission parameters and the set of sample actual energy consumption to obtain the energy consumption classification branch; Based on deep learning, a transmission performance prediction branch is constructed. The sample enhanced transmission parameter set and the sample signal strength parameter set are used as input features, and the sample bit error rate set is used as output features. Supervised training and testing are carried out until the requirements are met. The first enhanced transmission parameter is input into the energy consumption classification branch to obtain the first actual energy consumption. The first enhanced transmission parameter and signal strength parameter are input into the transmission performance prediction branch, and the first bit error rate is obtained by outputting the result.

6. A data transmission system based on Wi-Fi 6, characterized in that, The system is used to perform the method according to any one of claims 1-5, the system comprising: A terminal signal strength monitoring module is used to monitor the signal strength parameters of a terminal device in a target environment where data is transmitted via a Wi-Fi 6 device, wherein the terminal device transmits data with the Wi-Fi 6 device. The signal interference scene prediction module is used to predict the signal interference scene based on the signal strength parameter when the signal strength parameter is less than the signal strength threshold, and obtain the signal interference scene, wherein the signal interference scene is a living area scene. The signal interference scenario classification module is used to classify dwell time and transmission quality concern coefficient according to the signal interference scenario, and obtain the dwell time and transmission quality concern coefficient, including: Based on the usage records of terminal devices in the target environment over a historical period, a set of sample signal interference scenarios is collected; Collect the average dwell time of terminal devices in different sample signal interference scenarios, as well as the average number of operations of terminal devices, to obtain the sample dwell time set and the sample operation number set; Calculate the ratio of the number of operations for each sample to the mean of the set of number of operations for the samples, and use it as the sample transmission quality attention coefficient to obtain the set of sample transmission quality attention coefficients. Construct a mapping relationship between the sample signal interference scenario set, the sample dwell time set, and the sample transmission quality attention coefficient set to obtain an interference scenario classifier; The signal interference scenario is input into the interference scenario classifier, and the dwell time and transmission quality attention coefficient are obtained by mapping and classification. The signal enhancement transmission optimization module is used to perform signal enhancement transmission optimization based on the dwell time and transmission quality concern coefficient, obtain enhanced transmission parameters, and perform enhanced data transmission to the terminal device during the dwell time, including: Based on the dwell time and transmission quality concern coefficient, signal enhancement transmission optimization is performed to obtain enhanced transmission parameters, and enhanced data transmission is then performed, including: Obtain the enhanced transmission parameter space for enhanced data transmission of Wi-Fi 6 devices, and randomly generate the first enhanced transmission parameter; Construct the enhanced transfer function as follows: ； Where HWF is the transmission fitness, w1 and w2 are summed to 1, representing the energy consumption weight and quality weight respectively, Py is the preset energy consumption of the Wi-Fi 6 device, T is the dwell time, P is the actual energy consumption of the Wi-Fi 6 device under enhanced transmission parameters, K is the transmission quality concern coefficient, and R is the transmission error rate of data transmission between the Wi-Fi 6 device and the terminal device under enhanced transmission parameters. Based on the first enhanced transmission parameters, analyze and obtain the first actual energy consumption and the first transmission error rate of the Wi-Fi 6 device when transmitting data according to the first enhanced transmission parameters. Based on the first actual energy consumption and the first transmission error rate, and based on the enhanced transmission function, the first transmission fitness is calculated. Continue to randomly generate enhanced transmission parameters and calculate transmission fitness, perform iterative optimization of enhanced transmission parameters, and obtain the enhanced transmission parameters with the largest transmission fitness after optimization convergence, and perform enhanced data transmission on the terminal device during the dwell time.

7. A computer-readable storage medium, characterized in that, The storage medium stores a computer software program, which, when executed by a processor, implements the data transmission method based on Wi-Fi 6 as described in any one of claims 1-5.

8. An electronic device, characterized in that, include: Memory, used to store computer software programs; A processor is configured to read and execute the computer software program, thereby implementing the data transmission method based on Wi-Fi 6 as described in any one of claims 1-5.

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