High-efficiency wireless signal enhancement method and system
By acquiring and analyzing wireless signals, screening and training signal enhancement models, the problem of low wireless signal propagation efficiency is solved, and the stability and efficiency of signal transmission are improved.
Patent Information
- Application Number
- CN202411600286.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-11-11
- Publication Date
- 2025-02-28
AI Technical Summary
During the propagation process, wireless signals are affected by physical and environmental factors, resulting in weakening signal strength and limited coverage, reducing the transmission efficiency of wireless signals.
By acquiring the current and historical wireless signals, determining the signal transmission and reception locations, filtering the historical wireless signals to obtain reference signals matching the current environment, performing signal transmission fluctuation analysis, generating signal transmission curves and transmission tags, and training a signal enhancement model to enhance the current wireless signal.
It improves the accuracy and stability of signal transmission, enhances the transmission quality and efficiency of signal, and reduces transmission errors and data loss.
Smart Images
Figure CN119544108A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the technical field of signal enhancement, and in particular to a high-efficiency wireless signal enhancement method and system. Background Art
[0002] A wireless network is a network that can interconnect various communication devices without the need for wiring. Wireless network technology covers a wide range, including global voice and data networks that allow users to establish long-distance wireless connections, as well as infrared and radio frequency technologies that are optimized for short-distance wireless connections.
[0003] In modern society, wireless networks have become an indispensable part of people's daily life and work. However, the propagation of wireless signals is affected by a variety of physical and environmental factors, which leads to weakened wireless signal strength and limited coverage, thereby reducing the transmission efficiency of wireless signals. Summary of the invention
[0004] In order to solve at least one of the above technical problems, the present application provides a high-efficiency wireless signal enhancement method and system.
[0005] In a first aspect, the present application provides a high-efficiency wireless signal enhancement method, which adopts the following technical solution: Get current wireless signals and historical wireless signals; Determine a signal sending position and a signal receiving position according to the current wireless signal; Filter the historical wireless signals based on the transceiver range between the signal sending position and the signal receiving position to obtain a reference wireless signal corresponding to the transceiver range in the historical wireless signals; Performing a signal transmission fluctuation analysis on the reference wireless signal to obtain a signal transmission curve corresponding to the reference wireless signal and transmission labels corresponding to the signal transmission curve at different time nodes, wherein the transmission label is a labeling of a classification result of the signal transmission curve, and the classification result at least includes unenhanced, enhanced, a dynamic environment category when the signal transmission curve is obtained, and a dynamic environment category when the historical wireless signal is initially generated; Training the signal enhancement model according to the signal transmission curve, the time node, and the transmission label to obtain a trained signal enhancement model; Performing signal transmission fluctuation analysis on the current wireless signal to obtain a current signal curve corresponding to the current wireless signal, and inputting the current signal curve into the signal enhancement model for enhancement to obtain the enhanced current signal curve; A wireless signal to be transmitted is generated based on the current signal curve after the enhancement processing, and the wireless signal to be transmitted is sent to a target device.
[0006] By adopting the above technical solution, the current wireless signal and the historical wireless signal are obtained. By comparing the current and historical wireless signals, the stability and change law of signal transmission can be identified, which provides a reliable basis for subsequent signal processing and helps to improve the accuracy and stability of signal transmission. The signal sending and receiving positions are determined, and the spatial range of signal transmission is clarified. It provides a key spatial reference for the subsequent screening of historical wireless signals, making the screened reference wireless signals more representative, which helps to more accurately analyze the signal transmission characteristics and lay the foundation for the training of the signal enhancement model. The historical wireless signals are screened based on the transceiver range to obtain a reference wireless signal that matches the current transmission environment. This ensures the relevance of the reference signal, helps to reduce the interference of invalid data, improves the accuracy of signal transmission fluctuation analysis, and thus improves the training effect of the signal enhancement model. The reference wireless signal is subjected to transmission fluctuation analysis to obtain the signal transmission curve and transmission label. The label not only reflects the enhanced state of the signal, but also contains key information such as the dynamic environment category. The information provides a rich training sample and classification basis for the signal enhancement model, which helps the model to more accurately identify and handle signal transmission problems in different environments. The signal enhancement model is trained using the signal transmission curve, time node and transmission label. This enables the model to learn the transmission characteristics of the signal at different times and environments, thus having stronger generalization and adaptability. The trained signal enhancement model can more accurately predict and enhance the signal, improving the reliability and stability of signal transmission. The current wireless signal transmission fluctuation analysis is performed to obtain the current signal curve, which is then input into the signal enhancement model for enhancement. This ensures that the current signal can be enhanced in a targeted manner, which helps to solve the attenuation and interference problems during signal transmission and improve the signal transmission quality and efficiency. The wireless signal to be transmitted is generated based on the current signal curve after enhancement and sent to the target device. This ensures that the transmitted wireless signal has higher quality and stability, helps to reduce transmission errors and data loss, and improves the transmission efficiency of wireless signals.
[0007] In a possible implementation, the signal enhancement model is trained according to the signal transmission curve, the time node, and the transmission label to obtain the trained signal enhancement model, including: Inputting the signal transmission curve whose transmission label is not enhanced and meets the preset conditions and the time node into the signal enhancement model to generate an enhanced signal transmission curve; Acquire a signal sample set, wherein the signal samples in the signal sample set include the enhanced signal transmission curve and the signal transmission curve with a transmission label of enhanced; Inputting the signal sample into a signal determination model to obtain a determination result of the signal sample, wherein the determination result includes unenhanced, enhanced, and a dynamic environment category of the signal sample; A loss function of the signal network is obtained according to the determination result, and parameters of the signal enhancement model and the signal determination model are adjusted respectively according to the loss function until the signal network converges to obtain a trained signal enhancement model.
[0008] In a possible implementation, the inputting the signal transmission curve whose transmission label is not enhanced and satisfies a preset condition and the time node into the signal enhancement model to generate an enhanced signal transmission curve further includes: Inputting any of the signal transmission curves with the transmission label of not being enhanced into the identification model to determine whether any of the signal transmission curves with the transmission label of not being enhanced is an abnormal signal, wherein the curve time ratio of the abnormal signal is lower than a preset curve time ratio; If the identification model determines that it is not an abnormal signal, then it is identified that any of the signal transmission curves with the transmission label as not enhanced meets the preset condition.
[0009] In a possible implementation, the identification model includes a linear layer and a residual network, and the determining whether any signal transmission curve whose transmission label is not enhanced is an abnormal signal includes: Performing dimension upgrading on any of the signal transmission curves whose transmission label is not enhanced by the linear layer, and adjusting the dimension upgraded signal transmission curve to two-dimensional data by a reshape function; The two-dimensional data is input into the residual network for feature extraction to output a judgment result of whether it is an abnormal signal.
[0010] In a possible implementation, the signal enhancement model adopts a U-shaped network, the U-shaped network includes an encoder and a decoder, and the signal transmission curve whose transmission label is not enhanced and satisfies a preset condition and the time node are input into the signal enhancement model to generate an enhanced signal transmission curve, including: Based on the encoder, feature extraction is performed on the signal transmission curve whose transmission label is not enhanced and meets a preset condition to obtain a first feature; Based on the decoder, the first feature is dimensionally upgraded and information is restored to obtain fused data with the same dimension as the signal transmission curve whose transmission label is not enhanced and meets a preset condition; The fused data is added to the signal transmission curve whose transmission label is not enhanced and meets the preset condition to generate an enhanced signal transmission curve.
[0011] In a possible implementation, the signal determination model includes a feature extraction layer, a maximum pooling layer, an average pooling layer, a first multi-layer perceptron, and a second multi-layer perceptron, and the signal sample is input into the signal determination model to obtain a determination result of the signal sample, including: Performing feature extraction on the signal sample through the feature extraction layer to obtain a second feature; performing dimension reduction on the second feature through the maximum pooling layer and the average pooling layer respectively to obtain a third feature and a fourth feature respectively; The third feature and the fourth feature are input into a first multi-layer perceptron to obtain a determination result of the dynamic environment category of the signal sample, and the third feature and the fourth feature are input into a second multi-layer perceptron to obtain a determination result of the signal sample.
[0012] In a second aspect, the present application provides a high-efficiency wireless signal enhancement system, which adopts the following technical solution: A high-efficiency wireless signal enhancement system, comprising: A signal acquisition module is used to acquire current wireless signals and historical wireless signals; A location determination module, used to determine a signal sending location and a signal receiving location according to the current wireless signal; A signal screening module, configured to screen the historical wireless signals based on a transceiver range between the signal sending position and the signal receiving position, and obtain a reference wireless signal corresponding to the transceiver range in the historical wireless signals; A first analysis module is used to perform a signal transmission fluctuation analysis on the reference wireless signal to obtain a signal transmission curve corresponding to the reference wireless signal and a transmission label corresponding to the signal transmission curve at different time nodes, wherein the transmission label is a labeling of a classification result of the signal transmission curve, and the classification result at least includes unenhanced, enhanced, a dynamic environment category when the signal transmission curve is obtained, and a dynamic environment category when the historical wireless signal is initially generated; A model training module, used to train the signal enhancement model according to the signal transmission curve, the time node and the transmission label to obtain a trained signal enhancement model; A second analysis module is used to perform signal transmission fluctuation analysis on the current wireless signal to obtain a current signal curve corresponding to the current wireless signal, and input the current signal curve into the signal enhancement model for enhancement to obtain the enhanced current signal curve; The signal generating module is used to generate a wireless signal to be transmitted based on the current signal curve after the enhancement processing, and send the wireless signal to be transmitted to a target device.
[0013] In a possible implementation, when the model training module trains the signal enhancement model according to the signal transmission curve, the time node, and the transmission label to obtain the trained signal enhancement model, it is specifically used to: Inputting the signal transmission curve whose transmission label is not enhanced and meets the preset conditions and the time node into the signal enhancement model to generate an enhanced signal transmission curve; Acquire a signal sample set, wherein the signal samples in the signal sample set include the enhanced signal transmission curve and the signal transmission curve with a transmission label of enhanced; Inputting the signal sample into a signal determination model to obtain a determination result of the signal sample, wherein the determination result includes unenhanced, enhanced, and a dynamic environment category of the signal sample; A loss function of the signal network is obtained according to the determination result, and parameters of the signal enhancement model and the signal determination model are adjusted respectively according to the loss function until the signal network converges to obtain a trained signal enhancement model.
[0014] In another possible implementation, the system further includes: an abnormality identification module and a condition identification module, wherein: The abnormal identification module is used to input any of the signal transmission curves with the transmission label as unenhanced into the identification model to determine whether any of the signal transmission curves with the transmission label as unenhanced is an abnormal signal, and the curve time ratio of the abnormal signal is lower than the preset curve time ratio; The condition identification module is used to identify that any signal transmission curve with the transmission label as not enhanced satisfies the preset condition if the identification model determines that it does not belong to an abnormal signal.
[0015] In another possible implementation, the identification model includes a linear layer and a residual network, and the abnormal identification module is specifically used to: Performing dimension upgrading on any of the signal transmission curves whose transmission label is not enhanced by the linear layer, and adjusting the dimension upgraded signal transmission curve to two-dimensional data by a reshape function; The two-dimensional data is input into the residual network for feature extraction to output a judgment result of whether it is an abnormal signal.
[0016] In another possible implementation, the signal enhancement model adopts a U-shaped network, the U-shaped network includes an encoder and a decoder, and the second analysis module, when inputting the signal transmission curve whose transmission label is not enhanced and satisfies the preset conditions and the time node into the signal enhancement model to generate the enhanced signal transmission curve, is specifically used to: Based on the encoder, feature extraction is performed on the signal transmission curve whose transmission label is not enhanced and meets a preset condition to obtain a first feature; Based on the decoder, the first feature is dimensionally upgraded and information is restored to obtain fused data with the same dimension as the signal transmission curve whose transmission label is not enhanced and meets a preset condition; The fused data is added to the signal transmission curve whose transmission label is not enhanced and meets the preset condition to generate an enhanced signal transmission curve.
[0017] In another possible implementation, the signal determination model includes a feature extraction layer, a maximum pooling layer, an average pooling layer, a first multi-layer perceptron, and a second multi-layer perceptron. When the model training module inputs the signal sample into the signal determination model to obtain a determination result for the signal sample, the model training module is specifically used to: Performing feature extraction on the signal sample through the feature extraction layer to obtain a second feature; performing dimension reduction on the second feature through the maximum pooling layer and the average pooling layer respectively to obtain a third feature and a fourth feature respectively; The third feature and the fourth feature are input into a first multi-layer perceptron to obtain a determination result of the dynamic environment category of the signal sample, and the third feature and the fourth feature are input into a second multi-layer perceptron to obtain a determination result of the signal sample.
[0018] In a third aspect, the present application provides an electronic device, which adopts the following technical solution: at least one processor; Memory; At least one application, wherein the at least one application is stored in a memory and configured to be executed by at least one processor, and the at least one application is configured to: execute a high-efficiency wireless signal enhancement method as described in any one of the first aspects.
[0019] In a fourth aspect, the present application provides a computer-readable storage medium, which adopts the following technical solution: A computer-readable storage medium stores a computer program, which, when executed in a computer, causes the computer to execute any high-efficiency wireless signal enhancement method of the first aspect.
[0020] In summary, the present application includes at least one of the following beneficial technical effects: By adopting the above technical solution, the current wireless signal and the historical wireless signal are obtained. By comparing the current and historical wireless signals, the stability and change law of signal transmission can be identified, which provides a reliable basis for subsequent signal processing and helps to improve the accuracy and stability of signal transmission. The signal sending and receiving positions are determined, and the spatial range of signal transmission is clarified. It provides a key spatial reference for the subsequent screening of historical wireless signals, making the screened reference wireless signals more representative, which helps to more accurately analyze the signal transmission characteristics and lay the foundation for the training of the signal enhancement model. The historical wireless signals are screened based on the transceiver range to obtain a reference wireless signal that matches the current transmission environment. This ensures the relevance of the reference signal, helps to reduce the interference of invalid data, improves the accuracy of signal transmission fluctuation analysis, and thus improves the training effect of the signal enhancement model. The reference wireless signal is subjected to transmission fluctuation analysis to obtain the signal transmission curve and transmission label. The label not only reflects the enhanced state of the signal, but also contains key information such as the dynamic environment category. The information provides a rich training sample and classification basis for the signal enhancement model, which helps the model to more accurately identify and handle signal transmission problems in different environments. The signal enhancement model is trained using the signal transmission curve, time node and transmission label. This enables the model to learn the transmission characteristics of the signal at different times and environments, thus having stronger generalization and adaptability. The trained signal enhancement model can more accurately predict and enhance the signal, improving the reliability and stability of signal transmission. The current wireless signal transmission fluctuation analysis is performed to obtain the current signal curve, which is then input into the signal enhancement model for enhancement. This ensures that the current signal can be enhanced in a targeted manner, which helps to solve the attenuation and interference problems during signal transmission and improve the signal transmission quality and efficiency. The wireless signal to be transmitted is generated based on the current signal curve after enhancement and sent to the target device. This ensures that the transmitted wireless signal has higher quality and stability, helps to reduce transmission errors and data loss, and improves the transmission efficiency of wireless signals. BRIEF DESCRIPTION OF THE DRAWINGS
[0021] Figure 1 A schematic diagram of a flow chart of a high-efficiency wireless signal enhancement method provided in an embodiment of the present application.
[0022] Figure 2 A schematic diagram of the structure of a high-efficiency wireless signal enhancement system provided in an embodiment of the present application.
[0023] Figure 3 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION
[0024] The following is combined with Figure 1-3 This application is described in further detail.
[0025] This specific embodiment is merely an explanation of the present application and is not a limitation of the present application. After reading this specification, those skilled in the art may make non-creative modifications to the present embodiment as needed, but as long as they are within the scope of the present application, they are protected by the patent law.
[0026] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.
[0027] In addition, the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article, unless otherwise specified, generally means that the associated objects before and after are in an "or" relationship.
[0028] The embodiments of the present application are further described in detail below in conjunction with the drawings in the specification.
[0029] The embodiment of the present application provides a method for high-efficiency wireless signal enhancement, which is performed by an electronic device, wherein the electronic device can be an independent physical electronic device, or an electronic device cluster or distributed system composed of multiple physical electronic devices, or a cloud electronic device that provides cloud computing services. The embodiment of the present application is not limited here, such as Figure 1 As shown, the method includes: Step S10: Acquire current wireless signals and historical wireless signals.
[0030] For the embodiments of the present application, the current wireless signal represents the radio electromagnetic wave signal transmitted or received by the wireless device (such as a router, mobile phone, base station, etc.) at the current time point or time period. These signals contain various information, such as signal strength, frequency, phase, transmission and reception position, etc., and are used for various applications such as communication, positioning, and environmental monitoring. Historical wireless signals refer to radio electromagnetic wave signals that were also transmitted or received by wireless devices at past time points or time periods. These signals are recorded and can be used to analyze changes in the wireless environment, the movement trajectory of the device, signal interference, etc.
[0031] Specifically, the current wireless signal data is collected in real time through the sensors or interfaces of the wireless device and stored in a local or remote database. At the same time, the wireless signal data recorded in the past is retrieved from the database as the historical wireless signal.
[0032] Step S11: determine a signal sending position and a signal receiving position according to the current wireless signal.
[0033] For the embodiments of the present application, the signal transmission location refers to the geographical location of the device that transmits the wireless signal, which can be determined by a variety of methods, including but not limited to GPS positioning, base station positioning, wireless signal strength analysis, etc. The signal reception location refers to the geographical location of the device that receives the wireless signal, which can also be determined by a similar method.
[0034] In an embodiment of the present application, the longitude and latitude coordinates of the signal sending device and the receiving device are directly acquired through a GPS module.
[0035] Step S12: Filter the historical wireless signals based on the transceiver range between the signal sending position and the signal receiving position to obtain a reference wireless signal corresponding to the transceiver range in the historical wireless signals.
[0036] Specifically, the transceiver range indicates the maximum distance or area between the signal sending position and the signal receiving position within which the wireless signal can be effectively transmitted, and is used to indicate the geographical range or spatial range covered by the wireless signal.
[0037] For the embodiment of the present application, a signal propagation model is established, which can predict the propagation range and strength of the signal based on the signal sending position, receiving position and environmental factors (such as obstacles, buildings, etc.). Then, the historical wireless signal is compared with the range predicted by the model to screen out the reference wireless signal that meets the conditions.
[0038] Step S13: Perform signal transmission fluctuation analysis on the reference wireless signal to obtain a signal transmission curve corresponding to the reference wireless signal and transmission labels corresponding to the signal transmission curve at different time nodes.
[0039] For the embodiments of the present application, signal transmission fluctuation analysis refers to analyzing the changes in the strength, frequency, phase and other parameters of the wireless signal during the transmission process to understand the stability and quality of the signal transmission. The signal transmission curve is obtained based on the signal transmission fluctuation analysis, and is used to visually display the changes in the parameters (such as signal strength) of the wireless signal during the transmission process over time or distance. The transmission label is the signal characteristic information at a specific time node or position node marked on the signal transmission curve, such as signal strength, frequency, phase, etc. Specifically, the reference wireless signal is preprocessed, including denoising and filtering, to improve the signal quality. Then, the signal is analyzed in the frequency domain or time domain using signal processing techniques (such as Fourier transform, wavelet analysis, etc.) to extract the changes in signal strength, frequency, phase and other parameters over time or distance. Then, a signal transmission curve is drawn based on these parameters to intuitively display the transmission characteristics of the signal. Finally, the signal characteristic information at a specific time node or position node is marked on the signal transmission curve, i.e., the transmission label, for subsequent analysis and processing.
[0040] Step S14: training the signal enhancement model according to the signal transmission curve, time node and transmission label to obtain a trained signal enhancement model.
[0041] For the embodiment of the present application, the signal transmission curve and time node with the transmission label of unenhanced and satisfying the preset conditions are input into the signal enhancement model to generate the enhanced signal transmission curve, and then a signal sample set is obtained, wherein the signal samples in the signal sample set include the enhanced signal transmission curve and the signal transmission curve with the transmission label of enhanced, and then the signal samples are input into the signal judgment model to obtain the judgment result of the signal sample, and the judgment result includes unenhanced, enhanced and dynamic environment categories of the signal samples, and then the loss function of the signal network is obtained according to the judgment result, and the parameters of the signal enhancement model and the signal judgment model are adjusted according to the loss function respectively until the signal network converges to obtain the trained signal enhancement model.
[0042] Specifically, any signal transmission curve with a transmission label of not enhanced is input into the identification model to determine whether any signal transmission curve with a transmission label of not enhanced is an abnormal signal. The curve time ratio of the abnormal signal is lower than the preset curve time ratio. If the identification model determines that it does not belong to an abnormal signal, then the identification model determines that any signal transmission curve with a transmission label of not enhanced meets the preset conditions.
[0043] In an embodiment of the present application, the identification model includes a linear layer and a residual network, and determines whether any signal transmission curve with a transmission label of not enhanced is an abnormal signal, including: upgrading the dimension of any signal transmission curve with a transmission label of not enhanced through a linear layer, and adjusting the upgraded signal transmission curve to two-dimensional data through a reshape function, and then inputting the two-dimensional data into the residual network for feature extraction to output a judgment result of whether it is an abnormal signal.
[0044] In an embodiment of the present application, the signal enhancement model adopts a U-type network, which includes an encoder and a decoder. The signal transmission curve with a transmission label of not enhanced and meeting the preset conditions and the time node are input into the signal enhancement model to generate an enhanced signal transmission curve, including: based on the encoder, feature extraction is performed on the signal transmission curve with a transmission label of not enhanced and meeting the preset conditions to obtain a first feature, and then based on the decoder, dimension upgrading and information recovery are performed on the first feature to obtain fused data with the same dimension as the signal transmission curve with a transmission label of not enhanced and meeting the preset conditions, and then the fused data is added to the signal transmission curve with a transmission label of not enhanced and meeting the preset conditions to generate an enhanced signal transmission curve.
[0045] In an embodiment of the present application, a signal determination model includes a feature extraction layer, a maximum pooling layer, an average pooling layer, a first multi-layer perceptron, and a second multi-layer perceptron. A signal sample is input into the signal determination model to obtain a determination result of the signal sample, including: extracting features of the signal sample through the feature extraction layer and obtaining a second feature, and then reducing the dimension of the second feature through the maximum pooling layer and the average pooling layer respectively to obtain a third feature and a fourth feature respectively, and then inputting the third feature and the fourth feature into the first multi-layer perceptron to obtain a determination result of the dynamic environment category of the signal sample, and inputting the third feature and the fourth feature into the second multi-layer perceptron to obtain a determination result of the signal sample.
[0046] Step S15: perform signal transmission fluctuation analysis on the current wireless signal to obtain a current signal curve corresponding to the current wireless signal, and input the current signal curve into a signal enhancement model for enhancement to obtain the enhanced current signal curve.
[0047] Specifically, signal transmission fluctuation analysis is the process of analyzing the changes in the strength, frequency, phase and other parameters of the wireless signal during transmission. The current signal curve is a graphical representation obtained based on the transmission fluctuation analysis of the current wireless signal, which shows the changes in signal parameters (such as signal strength) over time or frequency.
[0048] Specifically, the current wireless signal is collected and preprocessed, including denoising, filtering and other steps to reduce unnecessary interference and noise. Then, the signal transmission fluctuation analysis technology is used to conduct an in-depth analysis of the current signal, extract key parameters such as signal strength, frequency, phase, and draw the current signal curve. Next, the current signal curve is used as input and sent to the signal enhancement model. The model automatically adjusts its internal parameters according to the input signal characteristics to achieve signal enhancement processing. Finally, the current signal curve after enhancement processing is output, which reflects the improvement of the signal after being processed by the model.
[0049] Step S16: Generate a wireless signal to be transmitted based on the current signal curve after enhancement processing, and send the wireless signal to be transmitted to the target device.
[0050] In the embodiment of the present application, the current wireless signal and the historical wireless signal are obtained. By comparing the current and historical wireless signals, the stability and change law of signal transmission can be identified, providing a reliable basis for subsequent signal processing, and helping to improve the accuracy and stability of signal transmission. The signal sending and receiving positions are determined, and the spatial range of signal transmission is clarified. It provides a key spatial reference for the subsequent screening of historical wireless signals, making the screened reference wireless signals more representative, helping to more accurately analyze the signal transmission characteristics, and laying the foundation for the training of the signal enhancement model. The historical wireless signals are screened based on the transceiver range to obtain a reference wireless signal that matches the current transmission environment. This ensures the relevance of the reference signal, helps to reduce the interference of invalid data, improves the accuracy of signal transmission fluctuation analysis, and thus improves the training effect of the signal enhancement model. The reference wireless signal is subjected to transmission fluctuation analysis to obtain a signal transmission curve and a transmission label. The label not only reflects the enhanced state of the signal, but also contains key information such as the dynamic environment category. The information provides a rich training sample and classification basis for the signal enhancement model, which helps the model to more accurately identify and handle signal transmission problems in different environments. The signal enhancement model is trained using the signal transmission curve, time node and transmission label. This enables the model to learn the transmission characteristics of the signal at different times and environments, thus having stronger generalization and adaptability. The trained signal enhancement model can more accurately predict and enhance the signal, improving the reliability and stability of signal transmission. The current wireless signal transmission fluctuation analysis is performed to obtain the current signal curve, which is then input into the signal enhancement model for enhancement. This ensures that the current signal can be enhanced in a targeted manner, which helps to solve the attenuation and interference problems during signal transmission and improve the signal transmission quality and efficiency. The wireless signal to be transmitted is generated based on the current signal curve after enhancement and sent to the target device. This ensures that the transmitted wireless signal has higher quality and stability, helps to reduce transmission errors and data loss, and improves the transmission efficiency of wireless signals.
[0051] The following is an introduction to a high-performance wireless signal enhancement system provided in an embodiment of the present application. The high-performance wireless signal enhancement system described below and the high-performance wireless signal enhancement method described above can be referred to in correspondence with each other. Please refer to Figure 2 , Figure 2 : is a schematic diagram of the structure of a high-efficiency wireless signal enhancement system 20 provided in an embodiment of the present application, including: A signal acquisition module 21 is used to acquire current wireless signals and historical wireless signals; A location determination module 22, used to determine a signal sending location and a signal receiving location according to a current wireless signal; A signal screening module 23, configured to screen historical wireless signals based on a transceiver range between a signal transmission position and a signal reception position, and obtain a reference wireless signal corresponding to the transceiver range in the historical wireless signals; A first analysis module 24 is used to perform signal transmission fluctuation analysis on the reference wireless signal to obtain a signal transmission curve corresponding to the reference wireless signal and transmission labels corresponding to the signal transmission curve at different time nodes, where the transmission label is a label for a classification result of the signal transmission curve, and the classification result at least includes unenhanced, enhanced, a dynamic environment category when the signal transmission curve is obtained, and a dynamic environment category when the historical wireless signal is initially generated; A model training module 25 is used to train the signal enhancement model according to the signal transmission curve, time node and transmission label to obtain a trained signal enhancement model; The second analysis module 26 is used to perform signal transmission fluctuation analysis on the current wireless signal to obtain a current signal curve corresponding to the current wireless signal, and input the current signal curve into the signal enhancement model for enhancement to obtain the enhanced current signal curve; The signal generating module 27 is used to generate a wireless signal to be transmitted based on the current signal curve after enhancement processing, and send the wireless signal to be transmitted to the target device.
[0052] In a possible implementation of the embodiment of the present application, when the model training module 25 trains the signal enhancement model according to the signal transmission curve, the time node and the transmission label to obtain the trained signal enhancement model, it is specifically used to: Inputting the signal transmission curve and time node whose transmission label is not enhanced and meets the preset conditions into the signal enhancement model to generate the enhanced signal transmission curve; Acquire a signal sample set, where the signal samples in the signal sample set include an enhanced signal transmission curve and a signal transmission curve with a transmission label of having been enhanced; Inputting the signal sample into the signal determination model to obtain a determination result of the signal sample, wherein the determination result includes unenhanced, enhanced and dynamic environment categories of the signal sample; The loss function of the signal network is obtained according to the judgment result, and the parameters of the signal enhancement model and the signal judgment model are adjusted according to the loss function until the signal network converges to obtain the trained signal enhancement model.
[0053] In another possible implementation of the embodiment of the present application, the system 20 further includes: an abnormality identification module and a condition identification module, wherein: An abnormal identification module, used for inputting any signal transmission curve with a transmission label of not enhanced into the identification model to determine whether any signal transmission curve with a transmission label of not enhanced is an abnormal signal, and the curve time ratio of the abnormal signal is lower than a preset curve time ratio; The conditional identification module is used to identify that any signal transmission curve with a transmission label of not being enhanced meets a preset condition if the identification model determines that it does not belong to an abnormal signal.
[0054] In another possible implementation of the embodiment of the present application, the identification model includes a linear layer and a residual network, and the abnormal identification module is specifically used to determine whether any signal transmission curve with a transmission label of not enhanced is an abnormal signal: The dimension of any signal transmission curve with the transmission label as unenhanced is increased through the linear layer, and the signal transmission curve after the dimension increase is adjusted to two-dimensional data through the reshape function; The two-dimensional data is input into the residual network for feature extraction to output the judgment result of whether it is an abnormal signal.
[0055] In another possible implementation of the embodiment of the present application, the signal enhancement model adopts a U-shaped network, the U-shaped network includes an encoder and a decoder, and the second analysis module 26 inputs the signal transmission curve and the time node with the transmission label as unenhanced and meeting the preset conditions into the signal enhancement model to generate the enhanced signal transmission curve, specifically for: Based on the encoder, feature extraction is performed on a signal transmission curve whose transmission label is not enhanced and meets a preset condition, to obtain a first feature; Based on the decoder, the first feature is dimensionally upgraded and information is restored to obtain fused data with the same dimension as the signal transmission curve whose transmission label is not enhanced and meets the preset conditions; The fused data is added to the signal transmission curve whose transmission label is not enhanced and meets the preset conditions to generate the enhanced signal transmission curve.
[0056] In another possible implementation of the embodiment of the present application, the signal determination model includes a feature extraction layer, a maximum pooling layer, an average pooling layer, a first multi-layer perceptron, and a second multi-layer perceptron. When the model training module 25 inputs the signal sample into the signal determination model to obtain a determination result of the signal sample, it is specifically used to: The feature extraction layer extracts features from the signal sample and obtains a second feature; the second feature is reduced in dimension by a maximum pooling layer and an average pooling layer, respectively, to obtain a third feature and a fourth feature; The third feature and the fourth feature are input into the first multi-layer perceptron to obtain a determination result of the dynamic environment category of the signal sample, and the third feature and the fourth feature are input into the second multi-layer perceptron to obtain a determination result of the signal sample.
[0057] The present application embodiment provides an electronic device, such as Figure 3 As shown, Figure 3 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application, Figure 3 The electronic device 300 shown includes: a processor 301 and a memory 303. The processor 301 and the memory 303 are connected, such as through a bus 302. Optionally, the electronic device 300 may also include a transceiver 304. It should be noted that in actual applications, the transceiver 304 is not limited to one, and the structure of the electronic device 300 does not constitute a limitation on the embodiments of the present application.
[0058] The processor 301 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It may implement or execute various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of the embodiments of the present application. The processor 301 may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, and the like.
[0059] The bus 302 may include a path to transmit information between the above components. The bus 302 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. The bus 302 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 3 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.
[0060] The memory 303 may be a ROM (Read Only Memory) or other types of static storage devices that can store static information and instructions, a RAM (Random Access Memory) or other types of dynamic storage devices that can store information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), a CD-ROM (Compact Disc Read Only Memory) or other optical disk storage, optical disk storage (including compressed optical disk, laser disk, optical disk, digital versatile disk, Blu-ray disk, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto.
[0061] The memory 303 is used to store application code for executing the solution of the embodiment of the present application, and the execution is controlled by the processor 301. The processor 301 is used to execute the application code stored in the memory 303 to implement the content shown in the above method embodiment.
[0062] Among them, electronic devices include but are not limited to: mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 3 The electronic device shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.
[0063] A computer-readable storage medium provided in an embodiment of the present application is introduced below. The computer-readable storage medium described below and the method described above can be referenced to each other.
[0064] An embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above high-efficiency wireless signal enhancement method are implemented.
[0065] Since the embodiments of the computer-readable storage medium part and the embodiments of the method part correspond to each other, the embodiments of the computer-readable storage medium part refer to the description of the embodiments of the method part.
[0066] It should be understood that, although the steps in the flowchart of the accompanying drawings are displayed in sequence as indicated by the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least a part of the steps in the flowchart of the accompanying drawings may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed in turn or alternately with other steps or at least a part of the sub-steps or stages of other steps.
[0067] The above are only some implementation methods of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.
Claims
1. A high-efficiency wireless signal enhancement method, characterized in that: include: Get current wireless signals and historical wireless signals; Determine a signal sending position and a signal receiving position according to the current wireless signal; Filter the historical wireless signals based on the transceiver range between the signal sending position and the signal receiving position to obtain a reference wireless signal corresponding to the transceiver range in the historical wireless signals; Performing a signal transmission fluctuation analysis on the reference wireless signal to obtain a signal transmission curve corresponding to the reference wireless signal and transmission labels corresponding to the signal transmission curve at different time nodes, wherein the transmission label is a labeling of a classification result of the signal transmission curve, and the classification result at least includes unenhanced, enhanced, a dynamic environment category when the signal transmission curve is obtained, and a dynamic environment category when the historical wireless signal is initially generated; Training the signal enhancement model according to the signal transmission curve, the time node, and the transmission label to obtain a trained signal enhancement model; Performing signal transmission fluctuation analysis on the current wireless signal to obtain a current signal curve corresponding to the current wireless signal, and inputting the current signal curve into the signal enhancement model for enhancement to obtain the enhanced current signal curve; A wireless signal to be transmitted is generated based on the current signal curve after the enhancement processing, and the wireless signal to be transmitted is sent to a target device.
2. A high-efficiency wireless signal enhancement method according to claim 1, characterized in that: The step of training the signal enhancement model according to the signal transmission curve, the time node, and the transmission label to obtain the trained signal enhancement model includes: Inputting the signal transmission curve whose transmission label is not enhanced and meets the preset conditions and the time node into the signal enhancement model to generate an enhanced signal transmission curve; Acquire a signal sample set, wherein the signal samples in the signal sample set include the enhanced signal transmission curve and the signal transmission curve with a transmission label of enhanced; Inputting the signal sample into a signal determination model to obtain a determination result of the signal sample, wherein the determination result includes unenhanced, enhanced, and a dynamic environment category of the signal sample; A loss function of the signal network is obtained according to the determination result, and parameters of the signal enhancement model and the signal determination model are adjusted respectively according to the loss function until the signal network converges to obtain a trained signal enhancement model.
3. A high-efficiency wireless signal enhancement method according to claim 2, characterized in that: The step of inputting the signal transmission curve whose transmission label is not enhanced and satisfies a preset condition and the time node into the signal enhancement model to generate an enhanced signal transmission curve further includes: Inputting any of the signal transmission curves with the transmission label of not being enhanced into the identification model to determine whether any of the signal transmission curves with the transmission label of not being enhanced is an abnormal signal, wherein the curve time ratio of the abnormal signal is lower than a preset curve time ratio; If the identification model determines that it is not an abnormal signal, then it is identified that any of the signal transmission curves with the transmission label as not enhanced meets the preset condition.
4. The high-efficiency wireless signal enhancement method according to claim 3, characterized in that: The identification model includes a linear layer and a residual network, and the determining whether any signal transmission curve whose transmission label is not enhanced is an abnormal signal includes: Performing dimension upgrading on any of the signal transmission curves whose transmission label is not enhanced by the linear layer, and adjusting the dimension upgraded signal transmission curve to two-dimensional data by a reshape function; The two-dimensional data is input into the residual network for feature extraction to output a judgment result of whether it is an abnormal signal.
5. The high-efficiency wireless signal enhancement method according to claim 2, characterized in that: The signal enhancement model adopts a U-shaped network, and the U-shaped network includes an encoder and a decoder. The signal transmission curve whose transmission label is not enhanced and meets the preset conditions and the time node are input into the signal enhancement model to generate an enhanced signal transmission curve, including: Based on the encoder, feature extraction is performed on the signal transmission curve whose transmission label is not enhanced and meets a preset condition to obtain a first feature; Based on the decoder, the first feature is dimensionally upgraded and information is restored to obtain fused data with the same dimension as the signal transmission curve whose transmission label is not enhanced and meets a preset condition; The fused data is added to the signal transmission curve whose transmission label is not enhanced and meets the preset condition to generate an enhanced signal transmission curve.
6. A high-efficiency wireless signal enhancement method according to claim 2, characterized in that: The signal determination model includes a feature extraction layer, a maximum pooling layer, an average pooling layer, a first multi-layer perceptron and a second multi-layer perceptron, and the signal sample is input into the signal determination model to obtain a determination result of the signal sample, including: Performing feature extraction on the signal sample through the feature extraction layer to obtain a second feature; performing dimension reduction on the second feature through the maximum pooling layer and the average pooling layer respectively to obtain a third feature and a fourth feature respectively; The third feature and the fourth feature are input into a first multi-layer perceptron to obtain a determination result of the dynamic environment category of the signal sample, and the third feature and the fourth feature are input into a second multi-layer perceptron to obtain a determination result of the signal sample.
7. A high-performance wireless signal enhancement system, characterized in that: include: A signal acquisition module is used to acquire current wireless signals and historical wireless signals; A location determination module, used to determine a signal sending location and a signal receiving location according to the current wireless signal; A signal screening module, configured to screen the historical wireless signals based on a transceiver range between the signal sending position and the signal receiving position, and obtain a reference wireless signal corresponding to the transceiver range in the historical wireless signals; A first analysis module is used to perform a signal transmission fluctuation analysis on the reference wireless signal to obtain a signal transmission curve corresponding to the reference wireless signal and a transmission label corresponding to the signal transmission curve at different time nodes, wherein the transmission label is a labeling of a classification result of the signal transmission curve, and the classification result at least includes unenhanced, enhanced, a dynamic environment category when the signal transmission curve is obtained, and a dynamic environment category when the historical wireless signal is initially generated; A model training module, used to train the signal enhancement model according to the signal transmission curve, the time node and the transmission label to obtain a trained signal enhancement model; A second analysis module is used to perform signal transmission fluctuation analysis on the current wireless signal to obtain a current signal curve corresponding to the current wireless signal, and input the current signal curve into the signal enhancement model for enhancement to obtain the enhanced current signal curve; The signal generating module is used to generate a wireless signal to be transmitted based on the current signal curve after the enhancement processing, and send the wireless signal to be transmitted to a target device.
8. The high-performance wireless signal enhancement system according to claim 7, characterized in that: When the model training module trains the signal enhancement model according to the signal transmission curve, the time node and the transmission label to obtain the trained signal enhancement model, it is specifically used to: Inputting the signal transmission curve whose transmission label is not enhanced and meets the preset conditions and the time node into the signal enhancement model to generate an enhanced signal transmission curve; Acquire a signal sample set, wherein the signal samples in the signal sample set include the enhanced signal transmission curve and the signal transmission curve with a transmission label of enhanced; Inputting the signal sample into a signal determination model to obtain a determination result of the signal sample, wherein the determination result includes unenhanced, enhanced, and a dynamic environment category of the signal sample; A loss function of the signal network is obtained according to the determination result, and parameters of the signal enhancement model and the signal determination model are adjusted respectively according to the loss function until the signal network converges to obtain a trained signal enhancement model.
9. An electronic device, characterized in that: The electronic device includes: at least one processor; Memory; At least one application, wherein the at least one application is stored in a memory and configured to be executed by at least one processor, and the at least one application is configured to: execute a high-efficiency wireless signal enhancement method according to any one of claims 1-6.
10. A computer-readable storage medium, characterized in that: include: A computer program is stored which can be loaded by a processor and executes a high-efficiency wireless signal enhancement method as described in any one of claims 1 to 6.