Impedance matching method and apparatus, external network gateway device, storage medium, and computer program product

Abstract

The application relates to the technical field of impedance matching, and discloses an impedance matching method and device, an external line gateway device, a storage medium and a computer program product. The method is applied to an external line gateway device, the external line gateway device is used in connection with a telephone network, and the method comprises the following steps: sending a multi-frequency detection signal to the telephone network based on a preset period, and acquiring echo signal attributes corresponding to the collected echo signal; performing feature extraction according to the echo signal attributes to obtain a feature vector, inputting the normalized feature vector into a preset neural network prediction model to obtain a predicted impedance value, and training the preset neural network prediction model by using sample impedance values and sample feature vectors; obtaining a target impedance value based on a preset smooth transition algorithm and the predicted impedance value, and performing impedance matching according to the target impedance value. Since the corresponding target impedance value is generated in real time to perform impedance matching, the device can adapt to different telephone network line characteristics to perform impedance matching.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of impedance matching, in particular to an impedance matching method and device, an external gateway device, a storage medium and a computer program product. BACKGROUND

[0002] Impedance matching refers to the process of adjusting the input or output impedance of a circuit to match or conjugate the impedance of the connected transmission line or load, thereby achieving maximum power transmission of signals and reducing return loss. With the continuous development and complexity of communication networks, how to effectively perform impedance matching to ensure the smoothness of network sessions has become a key issue.

[0003] The prior art generally uses a fixed impedance value as the input impedance of the external gateway device when connecting to a traditional telephone network, i.e., relying on manual preset impedance values (such as the default 600Ω) to connect to a traditional telephone network, which cannot adapt to different line characteristics (such as line aging, environmental noise) in different regions. SUMMARY

[0004] The main purpose of the present application is to provide an impedance matching method to solve the technical problem that the prior art cannot adapt to different telephone network line characteristics.

[0005] To achieve the above purpose, the present application provides an impedance matching method, which is applied to an external gateway device connected to a telephone network, and includes the following steps:

[0006] Sending a multi-frequency probe signal to the telephone network based on a preset period, and obtaining the echo signal properties corresponding to the collected echo signal;

[0007] Extracting features according to the echo signal properties to obtain a feature vector, and inputting the normalized feature vector into a preset neural network prediction model to obtain a predicted impedance value, wherein the preset neural network prediction model is trained by sample impedance values and sample feature vectors;

[0008] Obtaining a target impedance value based on a preset smooth transition algorithm and the predicted impedance value, and performing impedance matching according to the target impedance value.

[0009] In an embodiment, the step of obtaining the echo signal properties corresponding to the collected echo signal includes:

[0010] Collecting the time-domain waveform corresponding to the echo signal, and performing fast Fourier transform on the time-domain waveform to obtain a frequency response curve;

[0011] obtaining an intensity index corresponding to the echo signal based on the frequency response curve and a preset characteristic frequency point, and obtaining a phase offset between the echo signal and the multi-frequency detection signal;

[0012] determining a noise power spectral density corresponding to the echo signal according to a peak frequency and a peak amplitude in the frequency response curve;

[0013] taking the frequency response curve, the intensity index, the phase offset and the noise power spectral density as echo signal attributes corresponding to the echo signal.

[0014] In an embodiment, the step of performing feature extraction according to the echo signal attributes to obtain a feature vector comprises:

[0015] determining an echo intensity corresponding to the echo signal based on the intensity index in the echo signal attributes, and determining a frequency response peak corresponding to the echo signal based on the frequency response curve in the echo signal attributes;

[0016] determining a noise standard deviation corresponding to the echo signal according to the noise power spectral density in the echo signal attributes, and obtaining a historical impedance configuration between the telephone network;

[0017] generating a feature vector based on the echo intensity, the frequency response peak, the noise standard deviation and the historical impedance configuration.

[0018] In an embodiment, the step of obtaining a target impedance value based on a preset smooth transition algorithm and the predicted impedance value comprises:

[0019] obtaining a historical impedance configuration and a preset configuration parameter;

[0020] obtaining a target impedance value based on the historical impedance configuration, the preset configuration parameter and the predicted impedance value through a preset smooth transition algorithm.

[0021] In an embodiment, the step of performing impedance matching according to the target impedance value comprises:

[0022] obtaining a current impedance level;

[0023] determining a target impedance level based on the target impedance value and the current impedance level;

[0024] updating the current impedance level to the target impedance level.

[0025] In an embodiment, after the step of performing impedance matching according to the target impedance value, the method further comprises:

[0026] acquire a communication quality between the telephone network, and determine whether the communication quality meets a preset communication condition;

[0027] under the condition that the communication quality does not meet the preset communication condition, perform secondary feature extraction based on the communication quality and the echo signal attribute to obtain a new feature vector;

[0028] update the preset neural network prediction model based on the new feature vector and the target impedance value.

[0029] In addition, to achieve the above-mentioned purpose, the present application further provides an impedance matching device, which comprises:

[0030] a signal acquisition module, configured to send a multi-frequency detection signal to a telephone network based on a preset period, and acquire echo signal attributes corresponding to the collected echo signal;

[0031] an impedance acquisition module, configured to perform feature extraction based on the echo signal attributes to obtain a feature vector, and input the normalized feature vector into a preset neural network prediction model to obtain a predicted impedance value, wherein the preset neural network prediction model is trained by a sample impedance value and a sample feature vector to obtain the predicted impedance value;

[0032] an impedance matching module, configured to obtain a target impedance value based on a preset smooth transition algorithm and the predicted impedance value, and perform impedance matching according to the target impedance value.

[0033] In addition, to achieve the above-mentioned purpose, the present application further provides an external line gateway device, which comprises a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the computer program is configured to implement the steps of the impedance matching method as described above.

[0034] In addition, to achieve the above-mentioned purpose, the present application further provides a storage medium, which is a computer readable storage medium, and the storage medium stores a computer program, wherein the computer program is executed by a processor to implement the steps of the impedance matching method as described above.

[0035] In addition, to achieve the above-mentioned purpose, the present application further provides a computer program product, which comprises a computer program, wherein the computer program is executed by a processor to implement the steps of the impedance matching method as described above.

[0036] The application provides an impedance matching method and device, an external line gateway device, a storage medium and a computer program product. The method is applied to the external line gateway device which is connected with a telephone network. The method comprises the following steps: sending a multi-frequency probe signal to the telephone network based on a preset period, and acquiring echo signal attributes corresponding to the collected echo signal; performing feature extraction based on the echo signal attributes to obtain a feature vector, inputting the normalized feature vector into a preset neural network prediction model to obtain a predicted impedance value, and training the preset neural network prediction model based on sample impedance values and sample feature vectors; obtaining a target impedance value based on a preset smooth transition algorithm and the predicted impedance value, and performing impedance matching according to the target impedance value. Compared with the existing impedance matching method which uses a fixed impedance value, the impedance matching method can adapt to different telephone network line characteristics to perform impedance matching, because the multi-frequency probe signal can be sent to the telephone network based on the preset period, and the echo signal attributes corresponding to the collected echo signal can be acquired, and the target impedance value can be generated based on the actual situation of the telephone network by using the preset neural network prediction model and the echo signal attributes. BRIEF DESCRIPTION OF DRAWINGS

[0037] The accompanying drawings, which are incorporated herein and form part of the specification, illustrate embodiments consistent with the present application and, together with the description, further serve to explain the principles of the application.

[0038] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the accompanying drawings needed to be used in the embodiments or prior art description will be briefly introduced. Obviously, for those skilled in the art, other drawings can also be obtained based on these drawings without creative labor.

[0039] Figure 1 The flow chart of the first embodiment of the impedance matching method provided by the embodiments of the present application;

[0040] Figure 2 The flow chart of the second embodiment of the impedance matching method provided by the embodiments of the present application;

[0041] Figure 3 The flow chart of the third embodiment of the impedance matching method provided by the embodiments of the present application;

[0042] Figure 4 The impedance matching device provided by the embodiments of the present application;

[0043] Figure 5 The structural schematic diagram of the external line gateway device suitable for realizing the embodiments of the present application.

[0044] The implementation, functional features and advantages of the present application will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION

[0045] It should be understood that the specific embodiments described herein are merely exemplary of the application and are not intended to limit the application.

[0046] The technical solutions in the embodiments of the present application will be clearly and completely described with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative labor are within the scope of protection of the present application.

[0047] It should be noted that all directional indications (such as up, down, left, right, front, back, etc.) in the embodiments of the present application are only used to explain the relative positional relationship, movement condition, etc. between components in a certain posture (as shown in the drawings), and if the certain posture changes, the directional indications will also change accordingly.

[0048] It can be understood that impedance matching refers to adjusting the input or output impedance of a circuit to be equal to or conjugate equal to the impedance of the connected transmission line or load, so as to realize maximum power transmission of signals and reduce return loss. In modern communication systems, the transmission quality of signals is directly related to the intelligibility of voice calls, the smoothness of video streams, and the stability of data transmission. Impedance mismatching can cause signal reflection, attenuation and distortion, and thus affect the smoothness of network sessions. For example, in voice communication, impedance mismatching can cause echo enhancement and voice distortion in the call, resulting in degraded call quality and poor user experience. In data transmission, impedance mismatching can cause data packet loss and reduced transmission rate, affecting the efficiency and accuracy of data transmission, so how to effectively perform impedance matching to ensure the smoothness of network sessions becomes a key issue.

[0049] The prior art generally uses a fixed impedance value as the input impedance of the external gateway device for communication with the traditional telephone network when connecting the traditional telephone network through the external gateway device, that is, relies on manual preset impedance value (such as default 600Ω) to connect the traditional telephone network, which cannot adapt to different regional line characteristics (such as line aging, environmental noise).

[0050] Therefore, in order to solve the technical problem that the prior art cannot adapt to different telephone network line characteristics, the embodiment proposes an impedance matching method. The method is applied to an off-line gateway device connected with a telephone network, and includes: sending a multi-frequency probe signal to the telephone network based on a preset period, and obtaining echo signal properties corresponding to the collected echo signal; performing feature extraction based on the echo signal properties to obtain a feature vector, inputting the normalized feature vector into a preset neural network prediction model to obtain a predicted impedance value, and training the preset neural network prediction model by using sample impedance values and sample feature vectors; obtaining a target impedance value based on a preset smooth transition algorithm and the predicted impedance value, and performing impedance matching according to the target impedance value. Since the embodiment can send a multi-frequency probe signal to the telephone network based on a preset period, and obtain echo signal properties corresponding to the collected echo signal, a target impedance value is generated based on the actual situation of the telephone network by using a preset neural network prediction model and the echo signal properties, and impedance matching is performed. Compared with the existing impedance matching method using a fixed impedance value, the impedance matching method can adapt to different telephone network line characteristics.

[0051] For the sake of convenience, the following describes the embodiments of the present application with reference to the accompanying drawings. Figures 1 to 5 The impedance matching method provided by the embodiments of the present application and the impedance matching method, device, off-line gateway device, storage medium and computer program product provided by the following embodiments are described in detail.

[0052] The embodiments of the present application provide an impedance matching method, which is described with reference to the accompanying drawings. Figure 1 , Figure 1 The flowchart of the first embodiment of the impedance matching method provided by the embodiments of the present application is shown in FIG. 1.

[0053] As shown in FIG. 2, the method includes the following steps. Figure 1

[0054] Step S10: sending a multi-frequency probe signal to the telephone network based on a preset period, and obtaining echo signal properties corresponding to the collected echo signal.

[0055] It should be noted that the execution subject of the embodiment can be a multifunctional machine device with impedance matching, such as an off-line gateway device, or a device capable of realizing the above functions. The embodiment is described by using an off-line gateway device (hereinafter referred to as a device).

[0056] ​It should be noted that the above-mentioned external gateway device is used in connection with a telephone network, which can be an infrastructure for realizing telephone communication, including switches, transmission lines and other devices, connecting numerous user telephone terminals to ensure voice, fax and data signal transmission. The above-mentioned multi-frequency detection signal can be a detection signal composed of sine waves of different frequencies, used to detect the characteristics of the communication line. For example, the device can send a multi-frequency signal of 100-3400 Hz, covering the voice communication frequency band. The above-mentioned echo signal can be a signal reflected back to the sending end in the communication line due to impedance mismatch. When the line impedance and the device impedance are mismatched, part of the transmitted signal is reflected to form an echo signal. The above-mentioned echo signal attribute can be the characteristics of the echo signal, including intensity, phase, frequency response, etc.

[0057] In a specific implementation, the above-mentioned device has a built-in DSP echo detection module that generates and sends a multi-frequency detection signal to the traditional telephone network at a predetermined period (e.g. every 100 milliseconds). For example, a 100-3400 Hz multi-frequency signal covering the voice frequency band is sent. The signal is transmitted in the telephone network and generates an echo signal when it encounters a line impedance mismatch. The echo signal returns to the above-mentioned device along the line. The echo detection module of the above-mentioned device receives the echo signal and measures its intensity, phase, frequency response curve and other attributes.

[0058] Step S20: According to the echo signal attribute, a feature vector is obtained by feature extraction, and a normalized feature vector is input into a preset neural network prediction model to obtain a predicted impedance value, and the preset neural network prediction model is trained by sample impedance values and sample feature vectors.

[0059] It should be noted that the above-mentioned feature vector can be a vector composed of multiple feature values, used to represent the key characteristics of the echo signal, such as echo intensity, frequency response peak value, phase shift amount and noise power spectral density, etc. The above-mentioned preset neural network prediction model can be a pre-constructed and trained neural network model, used to predict the impedance value according to the input feature vector. The above-mentioned predicted impedance value can be the impedance value output by the neural network prediction model, used to guide impedance adjustment. The above-mentioned sample impedance value can be a known impedance value, used to train the neural network prediction model, and the above-mentioned sample feature vector can be a feature vector extracted from the sample echo signal attribute, corresponding to the sample impedance value one by one, used to train the neural network prediction model.

[0060] In a specific implementation, the device first extracts features from the collected echo signal attributes (including frequency response curve, intensity index, phase offset, and noise power spectral density), such as extracting peak frequency and peak amplitude from the frequency response curve, extracting echo intensity at a specific frequency point from the intensity index, extracting phase difference between the echo signal and the multi-frequency probe signal from the phase offset, and extracting background noise level from the noise power spectral density. Then, these feature values are combined into a feature vector, for example, the feature vector can be represented as [f1, a1, s1, p1, n1], where f1 and a1 represent peak frequency and peak amplitude respectively, s1 represents intensity index, p1 represents phase offset, and n1 represents noise power spectral density. Next, the feature vector is normalized so that its feature values fall within the range of 0, 1, for example, the normalized feature vector can be represented as [0.3, 0.5, 0.2, 0.4, 0.1]. The pre-set neural network prediction model is obtained by training a large number of sample impedance values and corresponding sample feature vectors, for example, using 1000 groups of sample impedance values and sample feature vectors under different line conditions for training. The normalized feature vector is input into the model to obtain the predicted impedance value.

[0061] Step S30: obtaining a target impedance value based on a pre-set smooth transition algorithm and the predicted impedance value, and performing impedance matching according to the target impedance value.

[0062] It should be noted that the pre-set smooth transition algorithm described above can be an algorithm pre-set for smoothly transitioning the predicted impedance value to the target impedance value, which is used to avoid the impact of impedance mutation on communication quality. The target impedance value described above can be an impedance value processed by the smooth transition algorithm, which is the value actually used for impedance matching.

[0063] In a specific implementation, the device first obtains a predicted impedance value through a pre-set neural network prediction model, and then processes the predicted impedance value using a pre-set smooth transition algorithm to obtain a target impedance value. For example, assuming that the predicted impedance value is 650Ω and the current impedance value is 600Ω, the smooth transition algorithm can use a step-by-step adjustment method to increase the impedance from 600Ω by 10Ω every certain time (such as 10 milliseconds) until it reaches the target impedance value of 650Ω. The device adjusts the impedance according to this target impedance value to achieve impedance matching with the telephone network and improve communication quality.

[0064] Further, the step of obtaining a target impedance value based on a pre-set smooth transition algorithm and the predicted impedance value includes:

[0065] Step S41: obtaining historical impedance configurations and pre-set configuration parameters;

[0066] Step S42: obtaining a target impedance value based on the historical impedance configuration, the preset configuration parameter and the predicted impedance value by a preset smooth transition algorithm.

[0067] It should be noted that the preset smooth transition algorithm is as follows:

[0068] ;

[0069] wherein, is the target impedance value, is the preset configuration parameter, is the historical impedance configuration. The historical impedance configuration can be a set of multiple impedance configuration parameters used by the device in the past running process, used to reflect the impedance adjustment in the past communication environment, and the preset configuration parameter can be a parameter preset by the device in the design for guiding impedance adjustment, including the step length, adjustment speed and impedance range of impedance adjustment.

[0070] In a specific implementation, the device first collects multiple impedance configuration parameters used in the previous running process as the historical impedance configuration, and these historical data reflect the characteristics of the past line and the adapted impedance. At the same time, the device obtains the preset configuration parameter, which provides a general framework and direction for the calculation and adjustment of the impedance. When the predicted impedance value is generated, the device utilizes the preset smooth transition algorithm to comprehensively process the historical impedance configuration, the preset configuration parameter and the predicted impedance value.

[0071] In the impedance matching of the embodiment, the multi-frequency probe signal is sent to the telephone network based on a preset period, and the echo signal attribute corresponding to the collected echo signal is obtained; the feature vector is obtained by feature extraction according to the echo signal attribute, and the normalized feature vector is input into the preset neural network prediction model to obtain the predicted impedance value, and the preset neural network prediction model is trained by the sample impedance value and the sample feature vector; the target impedance value is obtained based on the preset smooth transition algorithm and the predicted impedance value, and the impedance matching is performed according to the target impedance value. Since the embodiment can send the multi-frequency probe signal to the telephone network based on the preset period, and obtain the echo signal attribute corresponding to the collected echo signal, the target impedance value is generated by the preset neural network prediction model and the echo signal attribute according to the actual situation of the telephone network to perform impedance matching, compared with the existing impedance matching using fixed impedance value, which can adapt to different telephone network line characteristics for impedance matching.

[0072] Based on the first embodiment, in the second embodiment, the same or similar contents as the above embodiment one can refer to the above introduction, and the following will not be repeated. On this basis, please refer to Figure 2 , Figure 2The flowchart of the second embodiment of the impedance matching method proposed in the embodiments of the present application, further, in order to realize more accurate impedance matching, further, in order to realize accurate impedance matching, the step of acquiring the echo signal attribute corresponding to the collected echo signal comprises:

[0073] Step S11: Collecting the time domain waveform corresponding to the echo signal, and performing fast Fourier transform on the time domain waveform to obtain a frequency response curve.

[0074] It should be noted that the above fast Fourier transform can be an efficient algorithm for calculating discrete Fourier transform (DFT) and its inverse transform. The frequency response curve can be a curve describing the response characteristics of a system or signal at different frequencies. For example, for an echo signal processed by fast Fourier transform, the frequency response curve can be expressed as the amplitude value (in decibels) corresponding to each frequency point in the frequency range (such as 100Hz to 3400Hz).

[0075] In a specific implementation, the above device samples the echo signal through an analog-to-digital converter to obtain a series of discrete time domain data points, such as 1024 data points collected within 1 millisecond. Subsequently, the fast Fourier transform algorithm is used to convert these time domain data into frequency domain data. For example, after fast Fourier transform, the amplitude value distribution from 100Hz to 3400Hz frequency range is obtained. These amplitude values constitute the frequency response curve, reflecting the intensity change of the echo signal at different frequencies.

[0076] Step S12: Obtaining the intensity index corresponding to the echo signal based on the frequency response curve and the preset characteristic frequency point, and obtaining the phase offset between the echo signal and the multi-frequency detection signal.

[0077] It should be noted that the above preset characteristic frequency point can be a plurality of specific frequency points selected in advance for analyzing the frequency characteristics of the echo signal. For example, in the voice communication frequency band, frequency points such as 300Hz, 1kHz and 3.4kHz can be selected as the preset characteristic frequency point. The above intensity index can be the amplitude value of the echo signal at a specific frequency point, which is used to measure the strength of the echo signal. For example, at the preset characteristic frequency point, the intensity index of the echo signal can be the amplitude value corresponding to the frequency point, in decibels (dB). The above phase offset can be the difference between the echo signal and the multi-frequency detection signal in phase, which is used to reflect the phase change of the signal in the transmission process. For example, the phase offset can be expressed as the difference between the phase of the echo signal at a specific frequency point and the phase of the multi-frequency detection signal at the same frequency point, in degrees.

[0078] In a specific implementation, the device first extracts the amplitude values corresponding to the preset characteristic frequency points on the frequency response curve as the intensity indicators of the echo signal. For example, the preset characteristic frequency points are 300 Hz, 1 kHz and 3.4 kHz, and the device reads the amplitude values of these three frequency points on the frequency response curve to obtain the intensity indicators of the echo signal as -20 dB, -30 dB and -25 dB respectively. Meanwhile, the device calculates the phase difference between the echo signal and the multi-frequency probe signal at these characteristic frequency points by the phase detection algorithm to obtain the phase offset. For example, the phase offset between the echo signal and the multi-frequency probe signal at the 1 kHz characteristic frequency point is 45 degrees.

[0079] Step S13: determining the noise power spectral density corresponding to the echo signal according to the peak frequency and the peak amplitude in the frequency response curve.

[0080] It should be noted that the peak frequency can be the frequency point at which the amplitude of the echo signal reaches the maximum value in the frequency response curve, indicating that the signal intensity at this frequency is relatively high. For example, the amplitude of the echo signal reaches the peak value at 1 kHz in the frequency response curve, so 1 kHz is the peak frequency. The peak amplitude can be the amplitude value of the echo signal at the peak frequency, which is used to measure the intensity of the echo signal at this frequency. The noise power spectral density can be a physical quantity describing the power distribution of the noise signal within a unit frequency bandwidth, which is used to evaluate the frequency characteristics of the noise.

[0081] In a specific implementation, the device first analyzes the frequency response curve to find the peak frequency point therein. For example, the frequency response curve shows that the echo signal has a significant peak at 1 kHz. Then, the device obtains the peak amplitude value at the peak frequency. For example, the peak amplitude at 1 kHz is 50 dB. Next, the device calculates the noise power spectral density corresponding to the echo signal by using a preset noise power spectral density calculation model in combination with the peak frequency and the peak amplitude. For example, assuming that the calculation model is that the noise power spectral density is equal to the difference between the peak amplitude and the background noise reference value divided by the frequency bandwidth factor, the background noise reference value at 1 kHz is 30 dB, and the frequency bandwidth factor is 2 Hz / dB, then the calculated noise power spectral density is (50-30) / 2=10 W / Hz.

[0082] Step S14: taking the frequency response curve, the intensity indicators, the phase offset and the noise power spectral density as the echo signal attributes corresponding to the echo signal.

[0083] In a specific implementation, the above device obtains a frequency response curve by collecting the time-domain waveform of the echo signal and performing a fast Fourier transform. The amplitude value of a specific frequency point in the frequency response curve is extracted as the intensity indicator, for example, the amplitude value at 1 kHz is -30 dB. At the same time, the phase difference between the echo signal and the multi-frequency detection signal is calculated to obtain the phase shift, for example, the phase shift at 1 kHz is 45 degrees. In addition, the above device analyzes the noise component in the frequency response curve to determine the noise power spectral density, for example, the noise power spectral density at 1 kHz is W / Hz. These attributes together constitute a comprehensive feature description of the echo signal, all of which are echo signal attributes corresponding to the echo signal.

[0084] Further, the step of performing feature extraction according to the echo signal attributes to obtain a feature vector includes:

[0085] Step S21: determining the echo intensity corresponding to the echo signal based on the intensity indicator in the echo signal attributes, and determining the frequency response peak value corresponding to the echo signal based on the frequency response curve in the echo signal attributes.

[0086] It should be noted that the above echo intensity can be the strength of the echo signal determined based on the intensity indicator, which is used to evaluate the overall energy level of the echo signal. The above frequency response peak value can be the frequency point and its corresponding amplitude value in the frequency response curve where the amplitude reaches the maximum value, which is used to identify the strongest frequency component and its intensity in the echo signal.

[0087] In a specific implementation, the above device first extracts the intensity indicator from the echo signal attributes, which reflects the amplitude value of the echo signal at a specific frequency point. For example, assuming that the intensity indicator at 1 kHz is -30 dB, which indicates the relative amplitude size of this frequency component. According to this intensity indicator, the above device calculates the overall echo intensity of the echo signal, which is used to evaluate the energy level of the echo signal. At the same time, the above device analyzes the frequency response curve to find the frequency point and its corresponding amplitude value where the amplitude reaches the maximum value, to determine the frequency response peak value. For example, the frequency response curve shows that the amplitude of the echo signal reaches the peak value at 1 kHz, and the peak amplitude is 50 dB.

[0088] Step S22: determining the noise standard deviation corresponding to the echo signal according to the noise power spectral density in the echo signal attributes, and obtaining the historical impedance configuration between the telephone network.

[0089] It should be noted that the noise power spectral density described above can be a parameter describing the power distribution of the noise signal within a unit frequency bandwidth, which is used to evaluate the frequency characteristics of the noise. The noise standard deviation described above can be a parameter for measuring the dispersion degree of the intensity of the noise signal, which is used to evaluate the stability of the noise. The historical impedance configuration can be a set of multiple impedance configuration parameters used by the device in the past running process, which is used to reflect the impedance adjustment in the past communication environment.

[0090] In a specific implementation, the device first extracts the noise power spectral density from the echo signal attributes, for example, assuming that the noise power spectral density at a certain frequency point is W / Hz. According to the relationship between the noise power spectral density and the noise standard deviation, the device calculates the noise standard deviation. Then, the device obtains the historical impedance configuration between the device and the telephone network from the storage unit, which records multiple impedance configuration parameters used by the device and the telephone network in the past communication process. For example, the historical impedance configuration can include impedance values used at different time points and in different communication states before, such as 600Ω, 900Ω, etc. These data will be used for subsequent feature extraction and impedance matching calculation to achieve more accurate impedance adjustment.

[0091] Step S23: generating a feature vector based on the echo intensity, the frequency response peak, the noise standard deviation, and the historical impedance configuration.

[0092] It should be noted that the feature vector described above can be a vector composed of multiple feature values arranged in a certain order, which is used to represent the key characteristics of the echo signal. In a specific implementation, the device first extracts the echo intensity from the echo signal attributes, for example, assuming that the echo intensity is -30dB. Then, the frequency response peak is determined from the frequency response curve, for example, the amplitude reaches 50dB at 1kHz. Then, the noise standard deviation is calculated, which is assumed to be 0.5. At the same time, the key parameters in the historical impedance configuration are obtained, for example, the previously used impedance values 600Ω and 900Ω. The device combines these parameters into a feature vector, for example, [-30, 50, 1000, 0.5, 600, 900]. This feature vector comprehensively reflects the characteristics of the echo signal and the historical impedance configuration information, providing input data for the subsequent neural network prediction model

[0093] Based on the first embodiment and the second embodiment, in the third embodiment, the same or similar contents as the above embodiments one and two can be referred to the above description, and will not be described hereinafter. On this basis, please refer to Figure 3 , Figure 3 the flowchart of the third embodiment of the impedance matching method proposed in the embodiments of the present application, further, the step of performing impedance matching according to the target impedance value comprises:​

[0094] Step S33: Obtain a current impedance level.

[0095] Step S34: Determine a target impedance level based on the target impedance value and the current impedance level.

[0096] Step S35: Update the current impedance level to the target impedance level.

[0097] It should be noted that the current impedance level can be an impedance adjustment level currently set by the device, which is used to indicate the impedance setting of the device. The target impedance level can be an impedance adjustment level determined according to the target impedance value, which is used to guide the device to adjust the impedance.

[0098] In a specific implementation, the device first obtains the current impedance level, for example, the current impedance level is 600Ω. Then, according to the target impedance value calculated before, for example, the target impedance value is 650Ω, and in combination with the step length and level setting of impedance adjustment, the target impedance level is determined. Assuming that the impedance adjustment step length is 50Ω, the device determines that the target impedance level is 650Ω. Finally, the device updates the current impedance level from 600Ω to 650Ω, and completes the impedance adjustment to achieve impedance matching with the external interface and optimize the communication quality.

[0099] Further, after the step of performing impedance matching according to the target impedance value, the method further includes:

[0100] Step S40: Obtain the communication quality between the device and the telephone network, and determine whether the communication quality meets a preset communication condition.

[0101] It should be noted that the communication quality can be a comprehensive index for measuring the communication effect, including packet loss rate, delay, MOS score, etc. For example, if the MOS score is greater than or equal to 4, it indicates that the communication quality is good. The preset communication condition can be a pre-set communication quality requirement, which is used to determine whether the current communication is normal. For example, the preset packet loss rate is not more than 1%, and the delay is not more than 100 milliseconds.

[0102] In a specific implementation, the device collects communication data in real time through the communication quality monitoring module, and calculates the current packet loss rate, delay, and MOS score and other indicators. For example, the current packet loss rate is 0.5%, the delay is 80 milliseconds, and the MOS score is 4.2 points. The device compares these indicators with the preset communication conditions, and the preset conditions are that the packet loss rate does not exceed 1%, the delay does not exceed 100 milliseconds, and the MOS score is above 3.5 points. Since the current communication quality indicators are all better than the preset conditions, the device determines that the current communication quality meets the preset communication conditions, and there is no need to perform impedance adjustment. If the communication quality does not meet the preset conditions, the device triggers the impedance adjustment process and re-performs impedance matching to optimize the communication quality.

[0103] Step S50: under the condition that the communication quality does not meet the preset communication conditions, performing secondary feature extraction based on the communication quality and the echo signal attribute to obtain a new feature vector.

[0104] Step S60: updating the preset neural network prediction model based on the new feature vector and the target impedance value.

[0105] It should be noted that the above secondary feature extraction can be a process of further analyzing and processing the communication quality and the echo signal attribute to extract more valuable features. The above new feature vector can be a vector composed of the secondary extracted feature values, which is used to update the neural network model input.

[0106] In a specific implementation, under the condition that the communication quality does not meet the preset communication conditions, the device performs secondary feature extraction based on the communication quality and the echo signal attribute to obtain a new feature vector, and updates the preset neural network prediction model based on the new feature vector and the target impedance value. When the communication quality monitoring module detects that the packet loss rate is higher than the preset 1%, the delay exceeds the preset 100 milliseconds, and the MOS score is lower than the preset 3.5 points, the device determines that the communication quality does not meet the preset communication conditions. The device extracts the current communication quality data, such as the packet loss rate 1.5%, the delay 120 milliseconds, and the MOS score 3.0 points, and combines the echo signal attribute, such as the echo intensity -30 dB, the frequency response peak value 50 dB@1 kHz, and the noise standard deviation 0.5, to perform secondary feature extraction. Through a specific algorithm, a new feature vector [-30, 50, 1000, 0.5, 1.5%, 120 ms, 3.0] is obtained. At the same time, the device obtains the current target impedance value 650Ω. The new feature vector and the target impedance value are input into the preset neural network prediction model, the model parameters are adjusted, such as the neuron weight and bias are optimized, and the model updating is completed. The updated model can more accurately predict the impedance value and improve the communication quality.

[0107] The first embodiment of the impedance matching device is also provided in the embodiment, which is described as follows.Figure 4 , Figure 4 The impedance matching device provided by the embodiment of the present application comprises:

[0108] The signal acquisition module is configured to send a multi-frequency probe signal to a telephone network based on a preset period and acquire echo signal attributes corresponding to the collected echo signal.

[0109] The impedance acquisition module is configured to perform feature extraction based on the echo signal attributes to obtain a feature vector and input the normalized feature vector into a preset neural network prediction model to obtain a predicted impedance value, wherein the preset neural network prediction model is trained based on sample impedance values and sample feature vectors.

[0110] The impedance matching module is configured to obtain a target impedance value based on a preset smooth transition algorithm and the predicted impedance value and perform impedance matching according to the target impedance value.

[0111] The impedance matching module is further configured to acquire a historical impedance configuration and preset configuration parameters and obtain a target impedance value based on the historical impedance configuration, the preset configuration parameters and the predicted impedance value through the preset smooth transition algorithm.

[0112] With reference to the first embodiment of the impedance matching device, the second embodiment of the impedance matching device is also provided, and the same or similar content as the first embodiment of the impedance matching device can be referred to the foregoing description and will not be described hereinafter.

[0113] The signal acquisition module is further configured to collect a time-domain waveform corresponding to the echo signal, perform fast Fourier transform on the time-domain waveform to obtain a frequency response curve, obtain an intensity index corresponding to the echo signal based on the frequency response curve and a preset characteristic frequency point, obtain a phase offset between the echo signal and the multi-frequency probe signal, determine a noise power spectral density corresponding to the echo signal according to a peak frequency and a peak amplitude in the frequency response curve, and take the frequency response curve, the intensity index, the phase offset and the noise power spectral density as the echo signal attributes corresponding to the echo signal.

[0114] The signal acquisition module is further configured to determine an echo intensity corresponding to the echo signal based on the intensity index in the echo signal attributes and determine a frequency response peak value corresponding to the echo signal based on the frequency response curve in the echo signal attributes, determine a noise standard deviation corresponding to the echo signal based on the noise power spectral density in the echo signal attributes, and acquire a historical impedance configuration between the telephone network; and generate a feature vector based on the echo intensity, the frequency response peak value, the noise standard deviation and the historical impedance configuration.

[0115] With reference to the first embodiment of the impedance matching device and the second embodiment of the impedance matching device, the third embodiment of the impedance matching device is also provided. The same or similar contents of the third embodiment of the impedance matching device as the first embodiment of the impedance matching device and the second embodiment of the impedance matching device can be referred to the above description, and will not be described hereinafter.

[0116] The impedance matching module is further configured to obtain a current impedance level, determine a target impedance level based on the target impedance value and the current impedance level, and update the current impedance level to the target impedance level.

[0117] The impedance obtaining module is further configured to obtain a communication quality between the telephone network and determine whether the communication quality meets a preset communication condition, perform secondary feature extraction based on the communication quality and the echo signal attribute to obtain a new feature vector, and update the preset neural network prediction model based on the new feature vector and the target impedance value.

[0118] The impedance matching device provided in the embodiment can solve the technical problem that the prior art cannot adapt to different telephone network line characteristics by using the impedance matching method in the above embodiments. Compared with the prior art, the impedance matching device provided in the embodiment has the same beneficial effects as the impedance matching method provided in the above embodiments, and other technical features in the impedance matching device are the same as the features disclosed in the above embodiments, which will not be described here.

[0119] The embodiment provides an external line gateway device, which comprises at least one processor and a memory in communication connection with the at least one processor, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the impedance matching method in the above embodiment one.

[0120] Reference will be made to the following Figure 5 , Figure 5 A structural schematic diagram of an external line gateway device suitable for implementing the embodiment of the present application is shown in FIG. 1. The external line gateway device in the embodiment of the present application can include, but is not limited to, mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (Personal Digital Assistant), PADs (Portable Application Description), PMPs (Portable Media Player), and vehicle-mounted terminals (e.g., vehicle-mounted navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 5The illustrated off-line gateway device is merely an example and should not impose any limitation on the function and use range of the embodiments of the present application.

[0121] As shown in Figure 5 The off-line gateway device can include a processing device 1001 (e.g., a central processing unit, a graphics processing unit, etc.) that can perform various appropriate actions and processes according to programs stored in a read-only memory (ROM) 1002 or programs loaded from a storage device 1003 into a random access memory (RAM) 1004. Various programs and data required for operation of the off-line gateway device are also stored in the RAM 1004. The processing device 1001, the ROM 1002, and the RAM 1004 are connected to each other through a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Generally, the following systems can be connected to the I / O interface 1006: input devices 1007 including, for example, a touch screen, a touch pad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; the storage device 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 can allow the off-line gateway device to communicate wirelessly or wired with other devices to exchange data. Although the off-line gateway device with various systems is shown in the figure, it should be understood that it is not required to implement or have all the illustrated systems. More or fewer systems can be alternatively implemented or provided.

[0122] In particular, according to the present embodiments, the processes described above with reference to the flowcharts can be implemented as a computer software program. For example, the present embodiments include a computer program product comprising a computer program carried on a computer readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network through the communication device, or installed from the storage device 1003, or installed from the ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the methods disclosed in the present embodiments are performed.

[0123] The off-line gateway device provided by the present embodiments adopts the impedance matching method in the above embodiments, and can solve the technical problem that the prior art cannot adapt to different telephone network line characteristics. Compared with the prior art, the off-line gateway device provided by the present embodiments has the same beneficial effects as the impedance matching method provided by the above embodiments, and other technical features in the off-line gateway device are the same as the features disclosed in the above embodiments, which will not be repeated here.

[0124] It should be understood that various parts of the embodiments disclosed herein can be implemented in hardware, software, firmware or a combination thereof. In the description of the above-described embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0125] The above description is merely that of specific embodiments of the present embodiments, but the scope of protection of the present embodiments is not limited thereto. Any person skilled in the art can easily conceive of changes or replacements within the technical scope disclosed by the present embodiments, and all such changes or replacements should be encompassed within the scope of protection of the present embodiments. Therefore, the scope of protection of the present embodiments should be subject to the scope of protection of the claims.

[0126] The present embodiments provide a computer-readable storage medium having stored thereon computer-readable program instructions (i.e., a computer program) for performing the impedance matching method in the above-described embodiments.

[0127] The computer-readable storage medium provided by the present embodiments may, for example, be a U disk, but is not limited to an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, system, or device, or any combination of the above. More specific examples of the computer-readable storage medium can include, but are not limited to, an electrical connection having one or more conductive wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present embodiments, the computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, system, or device. The program code contained on the computer-readable storage medium can be transmitted using any suitable medium, including but not limited to electrical wires, optical cables, RF (Radio Frequency), etc., or any suitable combination of the above.

[0128] The above-described computer-readable storage medium can be included in an external gateway device; or can exist separately without being assembled into an external gateway device.

[0129] The above-described computer-readable storage medium carries one or more programs, which, when executed by the external gateway device, cause the external gateway device to perform impedance matching.

[0130] Computer program code for carrying out operations of the embodiments can be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code can execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection can be made to an external computer (for example, through the Internet using an Internet Service Provider).

[0131] The flow diagrams and the block diagrams in the drawings are meant as methodological and functional description of implementations of possible implementations of systems, methods, and computer program products according to various embodiments of the present embodiments. In this regard, each block in the flow diagrams or block diagrams can represent a module, a segment, or a portion of code, which comprises one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions noted in the blocks can occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently or the blocks may

[0132] The modules described in the present embodiments can be implemented by software or by hardware. In some cases, the name of the module does not constitute a limitation on the module itself.

[0133] The readable storage medium provided by the present embodiments is a computer readable storage medium, which stores computer readable program instructions (i.e., computer programs) for executing the impedance matching method described above, and can solve the technical problem that the prior art cannot adapt to different telephone network line characteristics. Compared with the prior art, the computer readable storage medium provided by the present embodiments has the same beneficial effects as the impedance matching method provided by the above embodiments, and will not be described here.

[0134] The above merely describes some embodiments and does not limit the patent scope of the embodiments. Any equivalent structural transformation, direct / indirect application in other related technical fields based on the technical concept of the present application and the content of the specification and drawings are included in the patent protection scope of the present application.

Claims

1. An impedance matching method, characterized in that, The method is applied to an external gateway device, which is connected to a telephone network, and the method includes: Based on a preset period, multi-frequency detection signals are sent to the telephone network, and the echo signal attributes corresponding to the collected echo signals are obtained. Feature vectors are obtained by extracting features based on the properties of the echo signal, and the normalized feature vectors are input into a preset neural network prediction model to obtain the predicted impedance value. The preset neural network prediction model is obtained by training the sample impedance value and the sample feature vector. The target impedance value is obtained based on the preset smooth transition algorithm and the predicted impedance value, and impedance matching is performed according to the target impedance value; The step of obtaining the echo signal attributes corresponding to the acquired echo signal includes: Acquire the time-domain waveform corresponding to the echo signal, and perform a fast Fourier transform on the time-domain waveform to obtain the frequency response curve; Based on the frequency response curve and the preset characteristic frequency point, the intensity index corresponding to the echo signal is obtained, and the phase offset between the echo signal and the multi-frequency detection signal is obtained. The noise power spectral density corresponding to the echo signal is determined based on the peak frequency and peak amplitude in the frequency response curve. The frequency response curve, the intensity index, the phase shift, and the noise power spectral density are used as the echo signal attributes corresponding to the echo signal. The step of extracting features based on the echo signal attributes to obtain a feature vector includes: The echo intensity of the echo signal is determined based on the intensity index in the echo signal attributes, and the peak frequency response of the echo signal is determined based on the frequency response curve in the echo signal attributes. The noise standard deviation corresponding to the echo signal is determined based on the noise power spectral density in the echo signal attributes, and the historical impedance configuration with the telephone network is obtained. A feature vector is generated based on the echo intensity, the peak frequency response, the noise standard deviation, and the historical impedance configuration.

2. The method as described in claim 1, characterized in that, The step of obtaining the target impedance value based on the preset smooth transition algorithm and the predicted impedance value includes: Obtain historical impedance configurations and preset configuration parameters; The target impedance value is obtained by using a preset smooth transition algorithm based on the historical impedance configuration, the preset configuration parameters, and the predicted impedance value.

3. The method as described in claim 1, characterized in that, The step of performing impedance matching according to the target impedance value includes: Get the current impedance level; Determine the target impedance level based on the target impedance value and the current impedance level; Update the current impedance level to the target impedance level.

4. The method as described in claim 1, characterized in that, After the step of impedance matching according to the target impedance value, the method further includes: The communication quality with the telephone network is obtained, and it is determined whether the communication quality meets the preset communication conditions. If the communication quality does not meet the preset communication conditions, a second feature extraction is performed based on the communication quality and the echo signal attributes to obtain a new feature vector. The preset neural network prediction model is updated based on the new feature vector and the target impedance value.

5. An impedance matching device, characterized in that, The device includes: The signal acquisition module is used to send multi-frequency detection signals to the telephone network based on a preset period and to acquire the echo signal attributes corresponding to the acquired echo signals. The impedance acquisition module is used to extract features based on the echo signal attributes to obtain a feature vector, and input the normalized feature vector into a preset neural network prediction model to obtain a predicted impedance value. The preset neural network prediction model is obtained by training through sample impedance values ​​and sample feature vectors. An impedance matching module is used to obtain a target impedance value based on a preset smooth transition algorithm and the predicted impedance value, and to perform impedance matching according to the target impedance value. The signal acquisition module is further configured to acquire the time-domain waveform corresponding to the echo signal, and perform a fast Fourier transform on the time-domain waveform to obtain a frequency response curve; obtain the intensity index corresponding to the echo signal based on the frequency response curve and preset characteristic frequency points, and obtain the phase offset between the echo signal and the multi-frequency detection signal; determine the noise power spectral density corresponding to the echo signal according to the peak frequency and peak amplitude in the frequency response curve; and use the frequency response curve, the intensity index, the phase offset, and the noise power spectral density as the echo signal attributes corresponding to the echo signal. The impedance acquisition module is further configured to determine the echo intensity corresponding to the echo signal based on the intensity index in the echo signal attributes, and determine the frequency response peak value corresponding to the echo signal based on the frequency response curve in the echo signal attributes; determine the noise standard deviation corresponding to the echo signal according to the noise power spectral density in the echo signal attributes, and obtain the historical impedance configuration with the telephone network; and generate a feature vector based on the echo intensity, the frequency response peak value, the noise standard deviation, and the historical impedance configuration.

6. An external gateway device, characterized in that, The device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the impedance matching method as described in any one of claims 1 to 4.

7. A storage medium, characterized in that, The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, it implements the steps of the impedance matching method as described in any one of claims 1 to 4.

8. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the steps of the impedance matching method as described in any one of claims 1 to 4.

Images