Keyboard and mouse wireless communication control method and system

Abstract

The present application discloses a method and system for controlling wireless keyboard and mouse communication, which relates to the field of mouse and keyboard technology. The method comprises: continuously monitoring the deployment electromagnetic environment of a target keyboard and mouse device using a spectrum analyzer and a signal strength detection device within a preset monitoring window to obtain a set of electromagnetic environment state feature sets; determining a set of interactive electromagnetic environment state feature sets; obtaining a set of interactive electromagnetic environment state feature concentration values; performing multi-dimensional feature fusion analysis to determine a set of wireless communication stability parameters; determining target wireless communication control parameters; and optimizing the deployment environment of the target keyboard and mouse device using the target wireless communication control parameters. The present application solves the technical problem in the prior art of poor wireless communication stability and low reliability of wireless communication control of keyboard and mouse devices in complex electromagnetic environments, thereby achieving the technical effect of improving the communication stability of keyboard and mouse devices in complex electromagnetic environments and enhancing the accuracy of wireless communication control.

Description

Technical Field

[0001] The present application relates to the technical field of mouse and keyboard, and in particular to a method and system for controlling wireless keyboard and mouse communication. Background Art

[0002] Currently, in complex electromagnetic environments, keyboard and mouse devices often experience poor wireless communication stability, impacting the user experience. For example, in the 2.4GHz frequency band, wireless keyboards and mice are often subject to interference from Wi-Fi routers, Bluetooth devices, and other sources, resulting in signal attenuation, delays, and even packet loss, which in turn affects device response speed. When multiple devices are coexisting in an office environment, frequency overlap exacerbates interference and leads to signal instability. Furthermore, existing wireless communication control methods often use fixed channels and fixed power, lacking dynamic adjustment capabilities and reducing control reliability.

[0003] The existing technology has the technical problems of poor wireless communication stability and low reliability of wireless communication control of keyboard and mouse devices in complex electromagnetic environments. Summary of the Invention

[0004] The present application provides a keyboard and mouse wireless communication control method and system, which are used to solve the technical problems in the prior art of poor wireless communication stability and low wireless communication control reliability of keyboard and mouse devices in complex electromagnetic environments.

[0005] In view of the above problems, the present application provides a keyboard and mouse wireless communication control method and system.

[0006] In a first aspect of the present application, a method for controlling wireless keyboard and mouse communication is provided, the method comprising:

[0007] Use spectrum analyzers and signal strength detection equipment to continuously monitor the deployment electromagnetic environment of the target keyboard and mouse device within the preset monitoring window to obtain a set sequence of electromagnetic environment status characteristics;

[0008] Performing adjacent matrix correlation interaction analysis on the electromagnetic environment state feature set sequence to determine an interactive electromagnetic environment state feature set sequence;

[0009] Traversing the interactive electromagnetic environment state feature set sequence to perform feature cross-time series centralized analysis to obtain an interactive electromagnetic environment state feature centralized value set, wherein the interactive electromagnetic environment state feature centralized value set includes a channel strength feature centralized value, an interference strength feature centralized value, and a channel occupancy feature centralized value;

[0010] Performing a multi-dimensional feature fusion analysis based on the channel strength feature concentration value, the interference strength feature concentration value, and the channel occupancy feature concentration value to determine a wireless communication stability parameter set, wherein the wireless communication stability parameter set includes a signal strength stability factor and a communication interference factor;

[0011] Optimizing the current wireless communication control parameters of the target keyboard and mouse device based on the signal strength stability factor and the communication interference factor to determine the target wireless communication control parameters;

[0012] The target wireless communication control parameters are used to control and optimize the deployment environment of the target keyboard and mouse device.

[0013] A second aspect of the present application provides a keyboard and mouse wireless communication control system, the system comprising:

[0014] The electromagnetic environment state feature set sequence acquisition module is used to continuously monitor the deployment electromagnetic environment of the target keyboard and mouse device using a spectrum analyzer and signal strength detection equipment within a preset monitoring window to obtain the electromagnetic environment state feature set sequence;

[0015] An interactive electromagnetic environment state feature set sequence determination module is used to perform adjacent matrix correlation interaction analysis on the electromagnetic environment state feature set sequence to determine the interactive electromagnetic environment state feature set sequence;

[0016] An interactive electromagnetic environment state feature concentrated value set obtaining module is used to traverse the interactive electromagnetic environment state feature set sequence to perform feature cross-time series concentrated analysis to obtain an interactive electromagnetic environment state feature concentrated value set, wherein the interactive electromagnetic environment state feature concentrated value set includes a channel strength feature concentrated value, an interference strength feature concentrated value, and a channel occupancy feature concentrated value;

[0017] a wireless communication stability parameter set determination module, configured to perform multi-dimensional feature fusion analysis based on the channel strength feature concentration value, the interference strength feature concentration value, and the channel occupancy feature concentration value to determine a wireless communication stability parameter set, wherein the wireless communication stability parameter set includes a signal strength stability factor and a communication interference factor;

[0018] a target wireless communication control parameter determination module, configured to optimize the current wireless communication control parameters of the target keyboard and mouse device based on the signal strength stability factor and the communication interference factor, and determine the target wireless communication control parameters;

[0019] A control optimization module is used to control and optimize the deployment environment of the target keyboard and mouse device using the target wireless communication control parameters.

[0020] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0021] This application continuously monitors the electromagnetic environment in which a target keyboard and mouse device is deployed using a spectrum analyzer and a signal strength detection device within a preset monitoring window to obtain a sequence of electromagnetic environment state feature sets. The application then performs adjacent matrix correlation interaction analysis on the sequence of electromagnetic environment state feature sets to determine an interactive electromagnetic environment state feature set sequence. The interactive electromagnetic environment state feature set sequence is then traversed to perform feature cross-time series centralized analysis to obtain a set of interactive electromagnetic environment state feature concentration values, wherein the interactive electromagnetic environment state feature concentration value set includes a channel strength feature concentration value, an interference strength feature concentration value, and a channel occupancy feature concentration value. A multidimensional feature fusion analysis is then performed based on the channel strength feature concentration value, the interference strength feature concentration value, and the channel occupancy feature concentration value to determine a set of wireless communication stability parameters, wherein the wireless communication stability parameter set includes a signal strength stability factor and a communication interference factor. Based on the magnitude of the signal strength stability factor and the communication interference factor, the current wireless communication control parameters of the target keyboard and mouse device are optimized to determine the target wireless communication control parameters. The target wireless communication control parameters are then used to optimize the deployment environment of the target keyboard and mouse device. This achieves the technical effect of improving the communication stability of wireless keyboards and mice in complex electromagnetic environments and enhancing the accuracy of wireless communication control. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0023] Figure 1 A schematic flow chart of a keyboard and mouse wireless communication control method provided in an embodiment of the present application;

[0024] Figure 2 This is a schematic diagram of the keyboard and mouse wireless communication control system structure provided in an embodiment of the present application.

[0025] Explanation of the accompanying symbols: electromagnetic environment state feature set sequence acquisition module 11, interactive electromagnetic environment state feature set sequence determination module 12, interactive electromagnetic environment state feature concentrated value set acquisition module 13, wireless communication stability parameter set determination module 14, target wireless communication control parameter determination module 15, control optimization module 16. DETAILED DESCRIPTION

[0026] The present application provides a keyboard and mouse wireless communication control method and system to solve the technical problems in the prior art of poor wireless communication stability and low reliability of wireless communication control of keyboard and mouse devices in complex electromagnetic environments.

[0027] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0028] It should be noted that the terms "including" and "having" are intended to cover non-exclusive inclusions. For example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or modules that are not clearly listed or are inherent to these processes, methods, products or devices.

[0029] Example 1, as Figure 1 As shown, the present application provides a keyboard and mouse wireless communication control method, wherein the method includes:

[0030] S100: Continuously monitor the deployment electromagnetic environment of the target keyboard and mouse device using a spectrum analyzer and a signal strength detection device within a preset monitoring window to obtain a set sequence of electromagnetic environment state characteristics;

[0031] In one possible embodiment, the target keyboard and mouse device is any set of mouse and keyboard devices that require wireless communication control. The preset monitoring window is a time period pre-set by a person skilled in the art for monitoring the electromagnetic environment in which the target keyboard and mouse device is deployed. Optionally, the monitoring window duration can be set based on actual needs to ensure that sufficient electromagnetic environment status characteristic data is captured within the time period, such as 30 minutes, 45 minutes, etc.

[0032] Preferably, set the spectrum analyzer's operating frequency band (typically the 2.4 GHz band where the target keyboard and mouse device resides), as well as the bandwidth and resolution, to ensure accurate monitoring of electromagnetic signals in the target frequency band. Then, place a signal strength detection device near the keyboard and mouse device to collect real-time signal strength data around the device.

[0033] During the entire time period of the preset monitoring window, a spectrum analyzer is used to conduct a comprehensive scan of the target frequency band, and the signal strength peak, average strength, noise level and frequency band occupancy of each channel are recorded. The signal strength detection device monitors the signal strength and generates a time series of signal strength for subsequent stability and volatility analysis. The spectrum analyzer continuously captures the electromagnetic signal data of each channel, especially high-interference channels and channels with high usage frequency, and records the signal strength changes of the channels and the frequency distribution of interference sources. Furthermore, the collected characteristic data such as signal strength, interference strength and channel occupancy are organized into a sequence of electromagnetic environment state feature sets in a time series. The electromagnetic environment state feature set sequence reflects the changes in the deployment electromagnetic environment of the target keyboard and mouse device within the preset monitoring window.

[0034] By systematically and accurately collecting and organizing various characteristics of the electromagnetic environment, a set sequence of electromagnetic environment state characteristics is formed, achieving the technical effect of providing basic data for subsequent analysis and optimization of wireless communication control.

[0035] S200: performing adjacent matrix correlation interaction analysis on the electromagnetic environment state feature set sequence to determine an interactive electromagnetic environment state feature set sequence;

[0036] Furthermore, step S200 in the embodiment of the present application further includes:

[0037] Extracting a first electromagnetic environment state feature set from the electromagnetic environment state feature set sequence as a first interactive electromagnetic environment state feature set, wherein the first electromagnetic environment state feature set is the electromagnetic environment state feature set that is first in the electromagnetic environment state feature set sequence;

[0038] Performing an adjacent matrix correlation interaction analysis on the first electromagnetic environment state feature set and the second electromagnetic environment state feature set to obtain a second interactive electromagnetic environment state feature set;

[0039] Adjacent matrix correlation interaction analysis is performed on two adjacent electromagnetic environment state feature sets in the electromagnetic environment state feature set sequence in sequence to obtain the interactive electromagnetic environment state feature set sequence.

[0040] Furthermore, the first electromagnetic environment state feature set and the second electromagnetic environment state feature set are subjected to an adjacent matrix correlation interaction analysis to obtain a second interactive electromagnetic environment state feature set. In this embodiment of the application, step S200 further includes:

[0041] Performing feature mapping calculation on the first electromagnetic environment state feature set and the second electromagnetic environment state feature set using an inner product mapping calculation formula to obtain a second interactive electromagnetic environment state feature similarity set;

[0042] Calling an adjacency matrix association analyzer to perform normalized value identification on the second interactive electromagnetic environment state feature similarity set to obtain an adjacency matrix;

[0043] A graph convolutional network is used to perform a convolution operation on the adjacency matrix and the second electromagnetic environment state feature set to obtain the second interactive electromagnetic environment state feature set.

[0044] Furthermore, step S200 in the embodiment of the present application further includes:

[0045] The inner product mapping calculation formula is:

[0046]

[0047] Among them, f(x i ,y i ) is the second interactive electromagnetic environment state feature similarity obtained after feature mapping calculation of the i-th electromagnetic environment state feature in the first electromagnetic environment state feature set and the second electromagnetic environment state feature set, x ij is the jth eigenvector of the ith electromagnetic environment state feature in the first electromagnetic environment state feature set, y ij is the jth eigenvector of the i-th electromagnetic environment state feature in the second electromagnetic environment state feature set, α ij The weight of the jth eigenvector of the i-th electromagnetic environment state characteristic when performing mapping calculation, n i is the total number of characteristic vectors contained in the i-th electromagnetic environment state characteristic, n i is an integer greater than or equal to 1.

[0048] Furthermore, step S200 in the embodiment of the present application further includes:

[0049] The adjacent matrix association analyzer includes an adjacent matrix association analysis formula, which is:

[0050]

[0051] Among them, Mix[f(x i ,y i )] is the normalized value of the i-th second interactive electromagnetic environment state feature similarity in the second interactive electromagnetic environment state feature similarity set, e is the base of the natural logarithm, and m is the total number of second interactive electromagnetic environment state feature similarities in the second interactive electromagnetic environment state feature similarity set;

[0052] The calling adjacent matrix association analyzer is used to perform normalized value identification on the second interactive electromagnetic environment state feature similarity set, and a matrix is ​​constructed on the identification result to obtain the adjacency matrix.

[0053] In one possible embodiment, due to the influence of various factors, the electromagnetic environment is in a fluctuating state, and there is an implicit correlation between the electromagnetic environment states of adjacent time series. Therefore, it is necessary to perform adjacent matrix correlation interaction analysis on the electromagnetic environment state feature set sequence, thereby forming an interactive electromagnetic environment state feature set sequence that contains deep correlation information and can more fully reflect the changes in the deployment electromagnetic environment within a preset monitoring window.

[0054] In one embodiment, a first electromagnetic environment state feature set in the sequence of electromagnetic environment state feature sets is extracted as the first interactive electromagnetic environment state feature set, wherein the first electromagnetic environment state feature set is the electromagnetic environment state feature set that is first in the sequence of electromagnetic environment state feature sets. Because the first electromagnetic environment state feature set is first, its data is not affected by electromagnetic environment state features at other monitoring time points, and therefore, it is used as the first interactive electromagnetic environment state feature set.

[0055] Optionally, the deep-seated impact of the electromagnetic environment state reflected by the first electromagnetic environment state feature set on the second electromagnetic environment state feature set located in the second position is analyzed, and the second interactive electromagnetic environment state feature set is obtained by performing adjacent matrix correlation interaction analysis on the first electromagnetic environment state feature set and the second electromagnetic environment state feature set.

[0056] Optionally, when analyzing the interaction of features at different time points between the first and second electromagnetic environment state feature sets, the similarity of the same features at different time points is first analyzed using an inner product mapping formula. Then, an adjacency matrix correlation analyzer is used to analyze the correlation strength of multiple features to determine an adjacency matrix. The adjacency matrix reflects the correlation strength between the first and second electromagnetic environment state feature sets. A convolution operation is then performed on the adjacency matrix and the second electromagnetic environment state feature set to incorporate the influence of the preceding adjacent electromagnetic environment state features, thereby obtaining a second interactive electromagnetic environment state feature set that incorporates the influence of deeper correlations.

[0057] In one embodiment, a feature mapping calculation is performed using an inner product mapping formula on features corresponding to the same feature type in the first electromagnetic environment state feature set and the second electromagnetic environment state feature set to obtain a second interactive electromagnetic environment state feature similarity set. The second interactive electromagnetic environment state feature similarity set reflects the degree of similarity between the same feature types in the first electromagnetic environment state feature set and the second electromagnetic environment state feature set. Furthermore, an adjacency matrix association analyzer is invoked to perform normalized value identification on the second interactive electromagnetic environment state feature similarity set to obtain an adjacency matrix.

[0058] The inner product mapping calculation formula is:

[0059]

[0060] Among them, f(x i ,y i ) is the second interactive electromagnetic environment state feature similarity obtained after feature mapping calculation of the i-th electromagnetic environment state feature in the first electromagnetic environment state feature set and the second electromagnetic environment state feature set, x ij is the jth eigenvector of the ith electromagnetic environment state feature in the first electromagnetic environment state feature set, y ij is the jth eigenvector of the i-th electromagnetic environment state feature in the second electromagnetic environment state feature set, α ij The weight of the jth eigenvector of the i-th electromagnetic environment state characteristic when performing mapping calculation, n i is the total number of characteristic vectors contained in the i-th electromagnetic environment state characteristic, n i is an integer greater than or equal to 1.

[0061] The inner product mapping calculation formula is used to quantitatively analyze the feature similarity between two adjacent electromagnetic environment state feature sets, thereby achieving a technical effect of improving the accuracy of analyzing electromagnetic environment state features.

[0062] Optionally, the adjacent matrix association analyzer includes an adjacent matrix association analysis formula, and the adjacent matrix association analysis formula is:

[0063]

[0064] Among them, Mix[f(x i ,y i )] is the normalized value of the i-th second interactive electromagnetic environment state feature similarity in the second interactive electromagnetic environment state feature similarity set, e is the base of the natural logarithm, and m is the total number of second interactive electromagnetic environment state feature similarities in the second interactive electromagnetic environment state feature similarity set.

[0065] The similarities of different features in the second interactive electromagnetic environment state feature similarity set are normalized using the adjacent matrix association analysis formula, so that the processed normalized values ​​are within the range of 0 to 1. Furthermore, an adjacent matrix association analyzer including the adjacent matrix association formula is used to perform normalized value identification on the second interactive electromagnetic environment state feature similarity set, obtaining a normalized value for each feature similarity in the second interactive electromagnetic environment state feature similarity set, and then populating the normalized value into a matrix to obtain the adjacency matrix.

[0066] Optionally, multiple sample adjacency matrices, multiple sample electromagnetic environment state feature sets, and multiple sample interactive electromagnetic environment state feature sets are obtained as training data to train a graph convolutional network. The mapping relationship between the adjacency matrix, the sample electromagnetic environment state feature set, and the interactive electromagnetic environment state feature set is learned until the training reaches convergence, thereby obtaining a trained graph convolutional network. Furthermore, the trained graph convolutional network is used to perform a convolution operation on the adjacency matrix and the second electromagnetic environment state feature set to obtain the second interactive electromagnetic environment state feature set.

[0067] Based on the same principles used to obtain the second interactive electromagnetic environment state feature set, adjacent matrix correlation analysis is performed on two adjacent electromagnetic environment state feature sets in the electromagnetic environment state feature set sequence to obtain the interactive electromagnetic environment state feature set sequence. By systematically extracting the correlation relationships between adjacent feature sets, a interactive electromagnetic environment state feature set sequence containing deep correlation information is formed, providing a solid data foundation for further feature-focused analysis and wireless communication optimization.

[0068] S300: traversing the interactive electromagnetic environment state feature set sequence to perform feature cross-time series centralized analysis to obtain an interactive electromagnetic environment state feature centralized value set, wherein the interactive electromagnetic environment state feature centralized value set includes a channel strength feature centralized value, an interference strength feature centralized value, and a channel occupancy feature centralized value;

[0069] Furthermore, the interactive electromagnetic environment state feature set sequence is traversed to perform feature cross-time series centralized analysis to obtain an interactive electromagnetic environment state feature centralized value set. In this embodiment of the application, step S300 further includes:

[0070] Extracting the interactive electromagnetic environment state feature set sequence using the environment state feature type as an index to obtain a channel strength feature split sequence, an interference strength feature split sequence, and a channel occupancy feature split sequence, wherein the environment state feature type includes channel strength, interference strength, and channel occupancy;

[0071] Performing feature cross-time series mean processing on the channel strength feature split sequence, the interference strength feature split sequence, and the channel occupancy feature split sequence respectively to obtain multiple channel strength feature means, multiple interference strength feature means, and multiple channel occupancy feature means;

[0072] The multiple channel strength feature means, the multiple interference strength feature means and the multiple channel occupancy feature means are respectively used as the channel strength feature concentrated value, the interference strength feature concentrated value and the channel occupancy feature concentrated value.

[0073] In one possible embodiment, starting with the first set of interactive electromagnetic environment state feature sets, the entire interactive electromagnetic environment state feature set sequence is traversed one by one to ensure that all time series data is included to obtain comprehensive time series feature information. Each set of interactive electromagnetic environment state feature sets is split using the environment state feature type as an index, and feature sequences of channel strength, interference strength, and channel occupancy are extracted respectively. The channel strength feature split sequence contains the channel strength data at each time point. The interference strength feature split sequence contains the interference strength data at each time point. The channel occupancy feature split sequence contains the channel occupancy data at each time point.

[0074] Then, mean values ​​are calculated for the channel strength feature split sequence, the interference strength feature split sequence, and the channel occupancy feature split sequence, respectively, to obtain multiple channel strength feature means, multiple interference strength feature means, and multiple channel occupancy feature means. Optionally, a time series mean processing is performed on the channel strength feature split sequence, i.e., the channel strengths at all time points are averaged to obtain a centralized value of the channel strength feature.

[0075] The calculated mean values ​​of channel strength, interference intensity, and channel occupancy are stored as corresponding concentrated values, reflecting the average state of these features over the entire time period. The concentrated values ​​of channel strength, interference intensity, and channel occupancy are integrated to form a concentrated set of interactive electromagnetic environment state features. Through cross-time series feature concentration analysis, a concentrated set of values ​​reflecting overall trends can be extracted from a large amount of time series data, facilitating subsequent wireless communication stability assessment and parameter optimization.

[0076] S400: Perform multi-dimensional feature fusion analysis based on the channel strength feature concentration value, the interference strength feature concentration value, and the channel occupancy feature concentration value to determine a wireless communication stability parameter set, wherein the wireless communication stability parameter set includes a signal strength stability factor and a communication interference factor;

[0077] In one possible embodiment, a preset channel strength characteristic threshold (a range of channel strengths for wireless communication between a target keyboard and mouse device, predefined by those skilled in the art) is obtained, and a ratio of the channel strength characteristic concentration value to the preset channel strength characteristic threshold is calculated. The calculated result is used as the signal strength stability factor. The signal strength stability factor reflects the signal stability of the target keyboard and mouse device.

[0078] Optionally, the interference intensity feature concentration value and the channel occupancy feature concentration value are weighted according to a weight ratio preset by a person skilled in the art to obtain the communication interference factor. The communication interference factor reflects the degree of interference to the wireless communication of the target keyboard and mouse device. The signal strength stability factor and the communication interference factor are used as a wireless communication stability parameter set.

[0079] By conducting multi-dimensional feature fusion analysis, the wireless communication environment of the target keyboard and mouse device is evaluated, achieving the technical effect of providing data support for subsequent communication control optimization.

[0080] S500: Optimizing current wireless communication control parameters of the target keyboard and mouse device based on the signal strength stability factor and the communication interference factor to determine target wireless communication control parameters;

[0081] S600: Utilizing the target wireless communication control parameters to control and optimize the deployment environment of the target keyboard and mouse device.

[0082] Furthermore, step S500 in the embodiment of the present application further includes:

[0083] acquiring a plurality of sample signal strength stability factors, a plurality of sample communication interference factors, a plurality of current wireless communication control parameters, and a plurality of sample target wireless communication control parameters as sample data;

[0084] Dividing the sample data according to a preset ratio to obtain a training set and a validation set;

[0085] Training a framework based on a convolutional neural network according to the training set, and supervising the training process using the validation set until the training reaches convergence, thereby obtaining a trained parameter optimizer;

[0086] The parameter optimizer is used to optimize the signal strength stability factor, the communication interference factor, and the current wireless communication control parameter to obtain the target wireless communication control parameter.

[0087] In one possible embodiment, the current wireless communication control parameters of the target keyboard and mouse device are optimized according to the signal strength stability factor and the size of the communication interference factor to obtain the target wireless communication control layer parameters, thereby ensuring stable communication quality even in an environment with large signal fluctuations, reducing delays and packet loss, and achieving the technical effect of improving the accuracy of wireless communication control of the target keyboard and mouse device.

[0088] The target wireless communication control parameters obtained in step S500 are applied to the wireless communication module of the keyboard and mouse device. For example, the transmit power can be adjusted to an appropriate level, a low-interference channel can be selected, or the frequency hopping strategy can be adjusted. After applying these parameters, the device's wireless communication quality is significantly improved in complex electromagnetic environments. By using control parameters more appropriate for the current environment, the device can reduce packet loss, lower latency, and better resist interference.

[0089] In one embodiment, a large amount of sample data is collected from a large database, including multiple sample signal strength stability factors, multiple sample communication interference factors, and current and target wireless communication control parameters under these conditions. These data samples are used to establish an optimization model. The collected sample data is divided into a training set and a validation set according to a preset ratio. The training set is used to train the model, and the validation set is used to monitor the model's performance during training to prevent overfitting and ensure model generalization. Optionally, the preset ratio is 3:2.

[0090] Alternatively, an optimization framework can be constructed based on a convolutional neural network (CNN). CNNs can effectively process complex data patterns and associated features, especially when multi-dimensional feature inputs are used. The CNN model is trained using the training set, and the model learns control parameter optimization patterns under different environments. The validation set monitors the training results in real time to ensure that the model accurately predicts the optimal control parameters under different electromagnetic environments. After training reaches convergence, a final parameter optimizer is generated. This optimizer outputs the corresponding target wireless communication control parameters based on the input signal strength stability factor and communication interference factor.

[0091] The signal strength stability factor and communication interference factor are then fed into the trained parameter optimizer, which then outputs the corresponding target wireless communication control parameters. Optionally, the resulting target wireless communication control parameters include transmit power, channel frequency, and frequency hopping rate, which are optimized for the current environment to achieve optimal communication stability and anti-interference capabilities.

[0092] In summary, the embodiments of the present application have at least the following technical effects:

[0093] 1. By real-time monitoring and analysis of the electromagnetic environment, extracting signal strength and interference factors, and optimizing wireless communication control parameters, the system achieves the technical effect of maintaining higher communication stability for keyboard and mouse devices in interference environments, reducing delays and packet loss, and improving wireless communication control reliability.

[0094] 2. Utilizing multi-dimensional feature fusion analysis, a set of communication stability parameters is generated to achieve the goal of rapid response to environmental changes. This achieves the technical effect of ensuring that keyboard and mouse devices optimize control parameters in a changing electromagnetic environment and maintain communication quality.

[0095] Embodiment 2 is based on the same inventive concept as the keyboard and mouse wireless communication control method in the above embodiment. Figure 2 As shown, the present application provides a keyboard and mouse wireless communication control system, and the system and method embodiments in the present application are based on the same inventive concept. The system includes:

[0096] The electromagnetic environment state feature set sequence acquisition module 11 is used to continuously monitor the deployment electromagnetic environment of the target keyboard and mouse device using a spectrum analyzer and a signal strength detection device within a preset monitoring window to obtain an electromagnetic environment state feature set sequence;

[0097] An interactive electromagnetic environment state feature set sequence determination module 12 is configured to perform adjacent matrix correlation interaction analysis on the electromagnetic environment state feature set sequence to determine the interactive electromagnetic environment state feature set sequence;

[0098] An interactive electromagnetic environment state feature concentrated value set obtaining module 13 is configured to traverse the interactive electromagnetic environment state feature set sequence to perform feature cross-time series concentrated analysis to obtain an interactive electromagnetic environment state feature concentrated value set, wherein the interactive electromagnetic environment state feature concentrated value set includes a channel strength feature concentrated value, an interference strength feature concentrated value, and a channel occupancy feature concentrated value;

[0099] a wireless communication stability parameter set determination module 14, configured to perform a multi-dimensional feature fusion analysis based on the channel strength feature concentration value, the interference strength feature concentration value, and the channel occupancy feature concentration value to determine a wireless communication stability parameter set, wherein the wireless communication stability parameter set includes a signal strength stability factor and a communication interference factor;

[0100] a target wireless communication control parameter determination module 15, configured to optimize the current wireless communication control parameters of the target keyboard and mouse device based on the signal strength stability factor and the communication interference factor, and determine the target wireless communication control parameters;

[0101] The control optimization module 16 is configured to optimize the deployment environment of the target keyboard and mouse device by using the target wireless communication control parameters.

[0102] Furthermore, the interactive electromagnetic environment state feature set sequence determination module 12 is configured to perform the following steps:

[0103] Extracting a first electromagnetic environment state feature set from the electromagnetic environment state feature set sequence as a first interactive electromagnetic environment state feature set, wherein the first electromagnetic environment state feature set is the electromagnetic environment state feature set that is first in the electromagnetic environment state feature set sequence;

[0104] Performing an adjacent matrix correlation interaction analysis on the first electromagnetic environment state feature set and the second electromagnetic environment state feature set to obtain a second interactive electromagnetic environment state feature set;

[0105] Adjacent matrix correlation interaction analysis is performed on two adjacent electromagnetic environment state feature sets in the electromagnetic environment state feature set sequence in sequence to obtain the interactive electromagnetic environment state feature set sequence.

[0106] Furthermore, the interactive electromagnetic environment state feature set sequence determination module 12 is configured to perform the following steps:

[0107] Performing feature mapping calculation on the first electromagnetic environment state feature set and the second electromagnetic environment state feature set using an inner product mapping calculation formula to obtain a second interactive electromagnetic environment state feature similarity set;

[0108] Calling an adjacency matrix association analyzer to perform normalized value identification on the second interactive electromagnetic environment state feature similarity set to obtain an adjacency matrix;

[0109] A graph convolutional network is used to perform a convolution operation on the adjacency matrix and the second electromagnetic environment state feature set to obtain the second interactive electromagnetic environment state feature set.

[0110] Furthermore, the interactive electromagnetic environment state feature set sequence determination module 12 is configured to perform the following steps:

[0111] The inner product mapping calculation formula is:

[0112]

[0113] Among them, f(x i ,y i ) is the second interactive electromagnetic environment state feature similarity obtained after feature mapping calculation of the i-th electromagnetic environment state feature in the first electromagnetic environment state feature set and the second electromagnetic environment state feature set, x ij is the jth eigenvector of the ith electromagnetic environment state feature in the first electromagnetic environment state feature set, y ij is the jth eigenvector of the i-th electromagnetic environment state feature in the second electromagnetic environment state feature set, α ij The weight of the jth eigenvector of the i-th electromagnetic environment state characteristic when performing mapping calculation, n i is the total number of characteristic vectors contained in the i-th electromagnetic environment state characteristic, n i is an integer greater than or equal to 1.

[0114] Furthermore, the interactive electromagnetic environment state feature set sequence determination module 12 is configured to perform the following steps:

[0115] The adjacent matrix association analyzer includes an adjacent matrix association analysis formula, which is:

[0116]

[0117] Among them, Mix[f(x i ,y i )] is the normalized value of the i-th second interactive electromagnetic environment state feature similarity in the second interactive electromagnetic environment state feature similarity set, e is the base of the natural logarithm, and m is the total number of second interactive electromagnetic environment state feature similarities in the second interactive electromagnetic environment state feature similarity set;

[0118] The calling adjacent matrix association analyzer is used to perform normalized value identification on the second interactive electromagnetic environment state feature similarity set, and a matrix is ​​constructed on the identification result to obtain the adjacency matrix.

[0119] Furthermore, the interactive electromagnetic environment state feature concentrated value set obtaining module 13 is used to perform the following steps:

[0120] Extracting the interactive electromagnetic environment state feature set sequence using the environment state feature type as an index to obtain a channel strength feature split sequence, an interference strength feature split sequence, and a channel occupancy feature split sequence, wherein the environment state feature type includes channel strength, interference strength, and channel occupancy;

[0121] Performing feature cross-time series mean processing on the channel strength feature split sequence, the interference strength feature split sequence, and the channel occupancy feature split sequence respectively to obtain multiple channel strength feature means, multiple interference strength feature means, and multiple channel occupancy feature means;

[0122] The multiple channel strength feature means, the multiple interference strength feature means and the multiple channel occupancy feature means are respectively used as the channel strength feature concentrated value, the interference strength feature concentrated value and the channel occupancy feature concentrated value.

[0123] Furthermore, the target wireless communication control parameter determination module 15 is configured to perform the following steps:

[0124] acquiring a plurality of sample signal strength stability factors, a plurality of sample communication interference factors, a plurality of current wireless communication control parameters, and a plurality of sample target wireless communication control parameters as sample data;

[0125] Dividing the sample data according to a preset ratio to obtain a training set and a validation set;

[0126] Training a framework based on a convolutional neural network according to the training set, and supervising the training process using the validation set until the training reaches convergence, thereby obtaining a trained parameter optimizer;

[0127] The parameter optimizer is used to optimize the signal strength stability factor, the communication interference factor, and the current wireless communication control parameter to obtain the target wireless communication control parameter.

[0128] It should be noted that the order in which the embodiments of the present application are presented is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. Furthermore, the foregoing descriptions of specific embodiments of this specification are provided. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order or sequential sequence shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0129] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should be included in the scope of protection of the present application.

[0130] This specification and drawings are merely illustrative of the present application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Obviously, those skilled in the art may make various modifications and variations to this application without departing from the scope of this application. Thus, this application is intended to include such modifications and variations as fall within the scope of this application and its equivalents.

Claims

1. A keyboard and mouse wireless communication control method, characterized in that: The method comprises: Use spectrum analyzers and signal strength detection equipment to continuously monitor the deployment electromagnetic environment of the target keyboard and mouse device within the preset monitoring window to obtain a set sequence of electromagnetic environment status characteristics; Performing adjacent matrix correlation interaction analysis on the electromagnetic environment state feature set sequence to determine an interactive electromagnetic environment state feature set sequence; Traversing the interactive electromagnetic environment state feature set sequence to perform feature cross-time series centralized analysis to obtain an interactive electromagnetic environment state feature centralized value set, wherein the interactive electromagnetic environment state feature centralized value set includes a channel strength feature centralized value, an interference strength feature centralized value, and a channel occupancy feature centralized value; Performing a multi-dimensional feature fusion analysis based on the channel strength feature concentration value, the interference strength feature concentration value, and the channel occupancy feature concentration value to determine a wireless communication stability parameter set, wherein the wireless communication stability parameter set includes a signal strength stability factor and a communication interference factor; Optimizing the current wireless communication control parameters of the target keyboard and mouse device based on the signal strength stability factor and the communication interference factor to determine the target wireless communication control parameters; Using the target wireless communication control parameters to control and optimize the deployment environment of the target keyboard and mouse device; The method of traversing the interactive electromagnetic environment state feature set sequence to perform cross-time series feature centralized analysis to obtain a centralized value set of interactive electromagnetic environment state features includes: Extracting the interactive electromagnetic environment state feature set sequence using the environment state feature type as an index to obtain a channel strength feature split sequence, an interference strength feature split sequence, and a channel occupancy feature split sequence, wherein the environment state feature type includes channel strength, interference strength, and channel occupancy; Performing feature cross-time series mean processing on the channel strength feature split sequence, the interference strength feature split sequence, and the channel occupancy feature split sequence respectively to obtain multiple channel strength feature means, multiple interference strength feature means, and multiple channel occupancy feature means; The multiple channel strength feature means, the multiple interference strength feature means and the multiple channel occupancy feature means are respectively used as the channel strength feature concentrated value, the interference strength feature concentrated value and the channel occupancy feature concentrated value.

2. The keyboard and mouse wireless communication control method according to claim 1, wherein: include: Extracting a first electromagnetic environment state feature set from the electromagnetic environment state feature set sequence as a first interactive electromagnetic environment state feature set, wherein the first electromagnetic environment state feature set is the electromagnetic environment state feature set that is first in the electromagnetic environment state feature set sequence; Performing an adjacent matrix correlation interaction analysis on the first electromagnetic environment state feature set and the second electromagnetic environment state feature set to obtain a second interactive electromagnetic environment state feature set; Adjacent matrix correlation interaction analysis is performed on two adjacent electromagnetic environment state feature sets in the electromagnetic environment state feature set sequence in sequence to obtain the interactive electromagnetic environment state feature set sequence.

3. The keyboard and mouse wireless communication control method according to claim 2, wherein: Performing an adjacent matrix correlation interaction analysis on the first electromagnetic environment state feature set and the second electromagnetic environment state feature set to obtain a second interactive electromagnetic environment state feature set includes: Performing feature mapping calculation on the first electromagnetic environment state feature set and the second electromagnetic environment state feature set using an inner product mapping calculation formula to obtain a second interactive electromagnetic environment state feature similarity set; Calling an adjacency matrix association analyzer to perform normalized value identification on the second interactive electromagnetic environment state feature similarity set to obtain an adjacency matrix; A graph convolutional network is used to perform a convolution operation on the adjacency matrix and the second electromagnetic environment state feature set to obtain the second interactive electromagnetic environment state feature set.

4. The keyboard and mouse wireless communication control method according to claim 3, wherein: include: The inner product mapping calculation formula is: Among them, f(x i ,y i ) is the second interactive electromagnetic environment state feature similarity obtained after feature mapping calculation of the i-th electromagnetic environment state feature in the first electromagnetic environment state feature set and the second electromagnetic environment state feature set, x ij is the jth eigenvector of the ith electromagnetic environment state feature in the first electromagnetic environment state feature set, y ij is the jth eigenvector of the i-th electromagnetic environment state feature in the second electromagnetic environment state feature set, α ij The weight of the jth eigenvector of the i-th electromagnetic environment state characteristic when performing mapping calculation, n i is the total number of characteristic vectors contained in the i-th electromagnetic environment state characteristic, n i is an integer greater than or equal to 1.

5. The keyboard and mouse wireless communication control method according to claim 4, wherein: include: The adjacent matrix association analyzer includes an adjacent matrix association analysis formula, which is: Among them, Mix[f(x i ,y i )] is the normalized value of the i-th second interactive electromagnetic environment state feature similarity in the second interactive electromagnetic environment state feature similarity set, e is the base of the natural logarithm, and m is the total number of second interactive electromagnetic environment state feature similarities in the second interactive electromagnetic environment state feature similarity set; The calling adjacent matrix association analyzer is used to perform normalized value identification on the second interactive electromagnetic environment state feature similarity set, and a matrix is ​​constructed on the identification result to obtain the adjacency matrix.

6. The keyboard and mouse wireless communication control method according to claim 1, wherein: include: acquiring a plurality of sample signal strength stability factors, a plurality of sample communication interference factors, a plurality of current wireless communication control parameters, and a plurality of sample target wireless communication control parameters as sample data; Dividing the sample data according to a preset ratio to obtain a training set and a validation set; Training a framework based on a convolutional neural network according to the training set, and supervising the training process using the validation set until the training reaches convergence, thereby obtaining a trained parameter optimizer; The parameter optimizer is used to optimize the signal strength stability factor, the communication interference factor, and the current wireless communication control parameter to obtain the target wireless communication control parameter.

7. A keyboard and mouse wireless communication control system, characterized in that: The system is used to implement the keyboard and mouse wireless communication control method according to any one of claims 1 to 6, and the system includes: The electromagnetic environment state feature set sequence acquisition module is used to continuously monitor the deployment electromagnetic environment of the target keyboard and mouse device using a spectrum analyzer and signal strength detection equipment within a preset monitoring window to obtain the electromagnetic environment state feature set sequence; An interactive electromagnetic environment state feature set sequence determination module is used to perform adjacent matrix correlation interaction analysis on the electromagnetic environment state feature set sequence to determine the interactive electromagnetic environment state feature set sequence; An interactive electromagnetic environment state feature concentrated value set obtaining module is used to traverse the interactive electromagnetic environment state feature set sequence to perform feature cross-time series concentrated analysis to obtain an interactive electromagnetic environment state feature concentrated value set, wherein the interactive electromagnetic environment state feature concentrated value set includes a channel strength feature concentrated value, an interference strength feature concentrated value, and a channel occupancy feature concentrated value; a wireless communication stability parameter set determination module, configured to perform multi-dimensional feature fusion analysis based on the channel strength feature concentration value, the interference strength feature concentration value, and the channel occupancy feature concentration value to determine a wireless communication stability parameter set, wherein the wireless communication stability parameter set includes a signal strength stability factor and a communication interference factor; a target wireless communication control parameter determination module, configured to optimize the current wireless communication control parameters of the target keyboard and mouse device based on the signal strength stability factor and the communication interference factor, and determine the target wireless communication control parameters; A control optimization module, configured to optimize the deployment environment of the target keyboard and mouse device by using the target wireless communication control parameters; The method of traversing the interactive electromagnetic environment state feature set sequence to perform cross-time series feature centralized analysis to obtain a centralized value set of interactive electromagnetic environment state features includes: Extracting the interactive electromagnetic environment state feature set sequence using the environment state feature type as an index to obtain a channel strength feature split sequence, an interference strength feature split sequence, and a channel occupancy feature split sequence, wherein the environment state feature type includes channel strength, interference strength, and channel occupancy; Performing feature cross-time series mean processing on the channel strength feature split sequence, the interference strength feature split sequence, and the channel occupancy feature split sequence respectively to obtain multiple channel strength feature means, multiple interference strength feature means, and multiple channel occupancy feature means; The multiple channel strength feature means, the multiple interference strength feature means and the multiple channel occupancy feature means are respectively used as the channel strength feature concentrated value, the interference strength feature concentrated value and the channel occupancy feature concentrated value.

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