A method and device for cross-operating system interconnection communication based on a power terminal

Abstract

The embodiment of the application discloses a kind of based on power terminal cross operating system interconnection communication method and device.The method comprises: obtaining the original data set of power terminal cross operating system channel under each modulation mode;Based on original data set and support vector machine regression algorithm training preset quantitative relationship model, determine the selected quantitative relationship model;Based on original data set, the performance of the selected quantitative relationship model is evaluated, and the target quantitative relationship model is determined;In the process of power terminal cross operating system interconnection communication, based on the current channel interval parameter under current communication environment and target quantitative relationship model, determine the current crosstalk intensity corresponding to current channel interval parameter;Based on current crosstalk intensity and preset crosstalk intensity threshold, the dynamic adjustment of current channel interval parameter is carried out, so as to accurately and conveniently realize the dynamic adjustment of channel interval parameter, and then avoid crosstalk between channels, and improve the communication quality of power terminal cross operating system interconnection communication.

Description

TECHNICAL FIELD

[0001] Embodiments of the present application relate to the technical field of computer, and particularly relate to a method and device for cross-operating system interconnection communication based on power terminal. BACKGROUND

[0002] With the development of information technology, power terminal can realize cross-operating system interconnection communication.

[0003] Currently, in the cross-operating system interconnection communication of power terminal, power line carrier communication faces the problem of repeated reception of messages caused by adjacent channel crosstalk. Due to the complex and changeable power line channel environment, the frequency interval and isolation degree between adjacent channels are insufficient, causing signal energy of one channel to leak to adjacent channels, causing crosstalk. When the crosstalk strength exceeds a certain threshold, the receiving end will repeatedly receive the message of the adjacent channel as the message of the target channel, causing communication errors.

[0004] It can be seen that there is an urgent need to accurately quantify the relationship between the frequency interval and the crosstalk strength between adjacent channels, and to find a way to ensure the isolation degree and suppress the crosstalk critical interval value. SUMMARY

[0005] Embodiments of the present application provide a method and device for cross-operating system interconnection communication based on power terminal, to accurately and conveniently realize dynamic adjustment of channel interval parameters, thereby avoiding crosstalk between channels and improving the communication quality of cross-operating system interconnection communication of power terminal.

[0006] In a first aspect, embodiments of the present application provide a method for cross-operating system interconnection communication based on power terminal, comprising:

[0007] Obtaining original data sets of channels of power terminal under cross-operating system in each modulation mode; the original data sets include channel interval parameters and crosstalk strength;

[0008] Training a preset quantitative relationship model based on the original data sets and a support vector machine regression algorithm to determine a to-be-selected quantitative relationship model; the preset quantitative relationship model is used to reflect the mapping relationship between the channel interval parameters and the crosstalk strength under each modulation mode;

[0009] Performing performance evaluation on the to-be-selected quantitative relationship model based on the original data sets to determine a target quantitative relationship model;

[0010] In the process of cross-operating system interconnection communication of power terminal, determining the current crosstalk strength corresponding to the current channel interval parameters based on the current channel interval parameters under the current communication environment and the target quantitative relationship model;

[0011] Performing dynamic adjustment of the current channel interval parameters based on the current crosstalk strength and a preset crosstalk strength threshold.

[0012] In a second aspect, an embodiment of the present application further provides an apparatus for cross-operating system interconnection communication based on a power terminal, the apparatus comprising:

[0013] an original data set obtaining module, configured to obtain original data sets of a channel of the power terminal in cross-operating system interconnection under various modulation modes, wherein the original data sets comprise channel spacing parameters and crosstalk strengths;

[0014] a to-be-selected quantitative relationship model determining module, configured to train a preset quantitative relationship model based on the original data sets and a support vector machine regression algorithm, and determine a to-be-selected quantitative relationship model, wherein the preset quantitative relationship model is used to reflect a mapping relationship between the channel spacing parameters and the crosstalk strengths under each modulation mode;

[0015] a target quantitative relationship model determining module, configured to perform performance evaluation on the to-be-selected quantitative relationship model based on the original data sets, and determine a target quantitative relationship model;

[0016] a current crosstalk strength determining module, configured to determine a current crosstalk strength corresponding to a current channel spacing parameter in a process of cross-operating system interconnection communication of the power terminal based on the current channel spacing parameter under a current communication environment and the target quantitative relationship model;

[0017] a dynamic adjustment module, configured to perform dynamic adjustment of the current channel spacing parameter based on the current crosstalk strength and a preset crosstalk strength threshold.

[0018] In a third aspect, an embodiment of the present application further provides an electronic device, the electronic device comprising:

[0019] one or more processors;

[0020] a memory, configured to store one or more programs;

[0021] when the one or more programs are executed by the one or more processors, the one or more processors implement the cross-operating system interconnection communication method based on the power terminal as provided in any embodiment of the present application.

[0022] In a fourth aspect, an embodiment of the present application provides a computer readable storage medium, which stores a computer program, and the program is executed by a processor to implement the cross-operating system interconnection communication method based on the power terminal as provided in any embodiment of the present application.

[0023] In a fifth aspect, an embodiment of the present application provides a computer program product, which comprises a computer program, and the computer program is executed by a processor to implement the cross-operating system interconnection communication method based on the power terminal as provided in any embodiment of the present application.

[0024] The technical scheme of the embodiment of the present application acquires original data sets of a channel of a power terminal across operating systems under various modulation modes; the original data sets include channel spacing parameters and crosstalk strength; a preset quantitative relationship model is trained based on the original data sets and a support vector machine regression algorithm to determine a to-be-selected quantitative relationship model; the preset quantitative relationship model is used to reflect the mapping relationship between the channel spacing parameters and the crosstalk strength under each modulation mode; the performance of the to-be-selected quantitative relationship model is evaluated based on the original data sets to determine a target quantitative relationship model; in the process of interconnection communication of the power terminal across operating systems, the current crosstalk strength corresponding to the current channel spacing parameters is determined based on the current channel spacing parameters under the current communication environment and the target quantitative relationship model; and the dynamic adjustment of the current channel spacing parameters is performed based on the current crosstalk strength and a preset crosstalk strength threshold, so that the dynamic adjustment of the channel spacing parameters is accurately and conveniently realized, and then the crosstalk between channels is avoided and the communication quality of the interconnection communication of the power terminal across operating systems is improved.

[0025] It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of the present application, nor is it used to limit the scope of the present application. Other features of the present application will become apparent from the following description. BRIEF DESCRIPTION OF DRAWINGS

[0026] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor on the basis of these drawings.

[0027] Figure 1 is a flow chart of an interconnection communication method based on a power terminal across operating systems provided by the first embodiment of the present application;

[0028] Figure 2 is a structural schematic diagram of an interconnection communication device based on a power terminal across operating systems provided by the second embodiment of the present application;

[0029] Figure 3 is a structural schematic diagram of an electronic device for implementing the interconnection communication method based on a power terminal across operating systems of the present application. DETAILED DESCRIPTION

[0030] In the following, the technical solutions in the embodiments of the present application will be described clearly and completely with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all the other embodiments obtained by a person of ordinary skill in the art without creative effort should belong to the scope of protection of the present application.

[0031] It should be noted that the terms "first", "second", and the like in the specification and claims of the present application and the above-described drawings are used to distinguish similar objects, and do not necessarily indicate a specific order or a chronological sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product, or device including a series of steps or units does not necessarily have to include only those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to the process, method, product, or device.

[0032] Embodiment one

[0033] Figure 1 A flowchart of a method for cross-operating system interconnection communication based on a power terminal is provided for the first embodiment of the present application. The present embodiment can be applicable to the case of dynamically adjusting channel spacing parameters. The method can be performed by a cross-operating system interconnection communication device based on a power terminal. The cross-operating system interconnection communication device based on a power terminal can be realized in the form of hardware and / or software, and can be configured in an electronic device. As shown in the figure, the method comprises: Figure 1

[0034] S110, obtaining original data sets of a channel of a power terminal across operating systems under various modulation modes.

[0035] The original data sets include channel spacing parameters and crosstalk strength. The channel spacing parameters can include but are not limited to channel frequency spacing, i.e., channel spacing.

[0036] Specifically, channel characteristic parameters under different modulation modes are obtained. The channel characteristic parameters can include but are not limited to channel spacing, signal-to-noise ratio, and bit error rate index. The obtained channel characteristic parameters are preprocessed to remove abnormal values and noise data, and original data sets of channel spacing and crosstalk strength under different modulation modes are obtained.

[0037] ​On the basis of the above technical solutions, the "obtaining the original data set of the power terminal cross operating system channel under each modulation mode" can include: obtaining the phase parameter, amplitude parameter and spectrum feature parameter output by the modem under each modulation mode; performing data preprocessing based on the phase parameter, amplitude parameter and spectrum feature parameter to determine the preprocessing data set under each modulation mode; performing feature extraction and spectrum distribution based on the preprocessing data set to determine the channel characteristic parameter; establishing the mapping relationship between the channel interval parameter and the crosstalk strength based on the channel characteristic parameter to obtain the original data set of the power terminal cross operating system channel under each modulation mode.

[0038] Among them, the modem can run in binary phase shift keying, quadrature phase shift keying and quadrature amplitude modulation state. The modulation mode can include binary phase shift keying modulation mode, quadrature phase shift keying modulation mode and quadrature amplitude modulation mode.

[0039] Specifically, the phase parameter, amplitude parameter and spectrum feature parameter output by the modem are obtained by using the orthogonal frequency division multiplexing demodulator. According to the phase parameter, amplitude parameter and spectrum feature parameter, the sliding median filter processing is performed, and the data after the sliding median filter processing is smoothed by the cubic spline function to obtain the preprocessing data set. The correlation features of the channel interval and the crosstalk strength under each modulation mode are extracted for the preprocessing data set, and the spectrum distribution of the correlation features is analyzed by fast Fourier transform to obtain the channel characteristic parameter. According to the channel characteristic parameter data, the product of the bandwidth occupation ratio and the data throughput is calculated, and the product of the bandwidth occupation ratio and the data throughput is polynomial regression to establish the mapping relationship between the channel interval and the crosstalk strength, so as to form the original data set of the channel interval and the crosstalk strength under different modulation modes.

[0040] Exemplarily, a quadrature frequency division multiplex demodulator is used to obtain phase parameters, amplitude parameters and spectral feature parameters of the modem output from the channel. In binary phase shift keying, quadrature phase shift keying, quadrature amplitude modulation, multiple channel interval samples are taken in the three modulation states. The sampled data is forward error correction redundancy coded by a Reed Solomon encoder. According to the crosstalk strength, signal-to-noise ratio and bit error rate parameters under the channel characteristic parameters, a data set is constructed. A sliding median filter with a window length of 16 is run on the data set to remove outliers, and a cubic spline function is used to smooth the data trend. The ratio of channel capacity to actual data transmission rate is calculated. Abnormal data points exceeding the range of 0.8 to 1.2 are removed to generate a preprocessed data set. From the preprocessed data set, three-dimensional correlation features of modulation state, channel interval and crosstalk strength are extracted. Fast Fourier transform is used to analyze the spectral distribution of the channel characteristic waveform. The waveform distortion strength is calculated according to the normalized mean square error. The signal attenuation amount is calculated according to the ratio of signal power to noise power. According to the Rayleigh distribution, the phase noise and amplitude noise are counted, and the channel characteristic parameter data is constructed. Based on the channel characteristic parameter data, the product of bandwidth occupation ratio and data throughput is calculated to obtain the channel utilization index value. According to the channel interval size, the data is grouped, and the mapping relationship between the channel interval and the crosstalk strength is calculated for each group of data by applying polynomial regression. The accuracy of the mapping relationship is verified by five-fold cross-validation, and the original data set of channel interval and crosstalk strength under different modulation modes is generated.

[0041] Further, when the orthogonal frequency division multiplexing demodulator collects the phase parameter, amplitude parameter and spectrum feature parameter output by the modem, in the binary phase shift keying modulation state, the phase of each carrier is offset by 180 degrees, and two modulation symbols are constructed; in the quadrature phase shift keying modulation state, the phase of each carrier is offset by 90 degrees, and four modulation symbols are constructed; in the quadrature amplitude modulation state, 16 modulation symbols are distributed on a constellation diagram, and the intervals between adjacent modulation symbols are equal. In the three modulation states, 10 different channel intervals are sampled, the signal-to-noise ratio is from 5 dB to 30 dB, and the bit error rate is from 0.00001 to 0.1. A Reed-Solomon encoder is used to add a 4-byte check code to the sampled data to generate a data set. A sliding median filter filters abnormal data points in a window of 16 sampling points, and smooths the sampling points of the mutated signal. A cubic spline function fitting curve passes through all normal sampling points, and a cubic polynomial is used for smooth interpolation between two adjacent sampling points. When the ratio of channel capacity to actual data transmission rate exceeds the threshold interval, it is determined that the point is abnormal and is removed, and a preprocessed data set is generated to retain normal sampling points. Fast Fourier transform converts the channel waveform from the time domain to the frequency domain, calculates the spectrum distribution characteristics of the waveform, and normalizes the mean square error to represent the deviation degree of the actual waveform from the ideal waveform. The waveform distortion degree is between 0.1 and 0.5, the signal power attenuation amount is between 3 dB and 15 dB, the Rayleigh distribution describes the phase noise and amplitude noise, the phase jitter standard deviation is between 5 degrees and 20 degrees, and the amplitude fluctuation standard deviation is between 0.1 and 0.4, which constitute the channel characteristic parameters. When calculating the bandwidth occupation ratio, the actual occupied bandwidth is between 100 MHz and 500 MHz, the data throughput is between 100 Mbps and 1 Gbps, and the product gives the channel utilization rate index. According to the channel interval from 10 MHz to 100 MHz, the data is divided into 10 groups, and the crosstalk intensity in each group of data is from -60 dB to -20 dB. A third-order polynomial regression is used to fit the corresponding relationship between the channel interval and the crosstalk intensity. When using 5-fold cross-validation, each fold of data accounts for 20%, and the accuracy index of the mapping function is obtained by training and verification. The modulation mode label is added to form the original data set of channel interval and crosstalk intensity under different modulation modes.

[0042] S120, training a preset quantitative relationship model based on the original data set and a support vector machine regression algorithm, and determining a to-be-selected quantitative relationship model.

[0043] The preset quantitative relationship model is used to reflect the mapping relationship between the channel interval parameter and the crosstalk intensity under each modulation mode. The to-be-selected quantitative relationship model can be a preset quantitative relationship model with a prediction accuracy that meets a standard.

[0044] Specifically, the original data set is standardized by using a max-min normalizer, and features with a high correlation with the modulation mode higher than a preset threshold are screened by using a Pearson correlation coefficient to obtain a standardized feature matrix. Training data is divided according to the standardized feature matrix, and the training data is mapped by using a radial basis kernel function, and training parameters are obtained by using a support vector machine regression algorithm. The training parameters are used to predict the verification data, the prediction deviation is calculated by using a mean square error loss function, and the regression prediction parameters are obtained by using a gradient descent method. If the prediction accuracy corresponding to the regression prediction parameters is higher than a prediction accuracy threshold, the prediction result is output. If the prediction accuracy is lower than the prediction accuracy threshold and the training round does not reach a preset training round, the verification data is stacked to retrain.

[0045] On the basis of the above technical solutions, the "training a preset quantitative relationship model based on an original data set and a support vector machine regression algorithm, and determining a to-be-selected quantitative relationship model" can include: performing standardization processing and feature screening based on the original data set to determine a standardized feature matrix; determining training parameters based on the support vector machine regression algorithm and the mapping features corresponding to the training data in the standardized feature matrix; the training parameters are model parameters corresponding to the preset quantitative relationship model; determining regression prediction parameters based on the training parameters and the verification data in the standardized feature matrix; the regression prediction parameters are model parameters corresponding to the to-be-selected quantitative relationship model.

[0046] Specifically, the channel interval values in the original data set are mapped to the interval of 0 to 1, and the crosstalk intensity values are mapped to the interval of -1 to 1 by using a max-min normalizer, and the Pearson correlation coefficients are screened according to a fixed threshold of 0.7 to retain features with a correlation with the modulation mode greater than the threshold. The feature vectors with a cumulative contribution rate of 95% are retained by using singular value decomposition truncation, and the standardized feature matrix is generated according to the modulation mode marking. The training data is divided according to an 80% proportion based on the standardized feature matrix. The channel interval and crosstalk intensity under each modulation mode in the training data are mapped based on a radial basis kernel function, the support vector machine penalty factor initial value is set to 0.1, the support vector machine regression algorithm is applied to the training data for fitting, and the training parameters are stored according to the modulation mode marking. The verification data is predicted according to the stored training parameters according to the modulation mode marking, the deviation of the prediction result from the actual value is calculated by using a mean square error loss function, and the training parameters are iteratively updated by using a batch gradient descent method, the learning rate is set to 0.01, and the iteration number is set to 1000 to obtain the regression prediction parameters corresponding to the modulation mode. The regression prediction parameters are used to predict the crosstalk intensity of the input modulation mode and channel interval data, and the accuracy of the prediction result is calculated by using a root mean square error. If the accuracy is greater than 0.9, the prediction result is output. If the accuracy is less than 0.9, the verification data is stacked to retrain, and the prediction result corresponding to the prediction parameter with the highest accuracy is output when the training round reaches a preset value of 5.

[0047] Further, in the maximum-minimum normalization process, the channel spacing original value in the range of 10MHz to 100MHz is mapped to the interval of 0 to 1 for subsequent processing; the crosstalk strength original value in the range of -60dB to -20dB is mapped to the interval of -1 to 1 to maintain the data distribution characteristics. In the binary phase shift keying modulation mode, the Pearson correlation coefficient of the channel spacing and the crosstalk strength is 0.85, 0.78 in the quadrature phase shift keying, and 0.72 in the quadrature amplitude modulation, all of which exceed the threshold of 0.7, indicating strong correlation. The singular value decomposition retains the eigenvectors corresponding to the characteristic value contribution rate of 95%, and the dimensionality reduction reduces the number of characteristics from the original 12 to 4. In the support vector machine regression algorithm, the kernel parameter of the radial basis function is 0.5, the similarity between the training data points is calculated, and the penalty factor is initially 0.1 to balance the model complexity and fitting degree. In the binary phase shift keying modulation mode, the predicted crosstalk strength is -45dB when the channel spacing is 20MHz, and the actual value is -42dB; the predicted value is -32dB when the channel spacing is 50MHz, and the actual value is -35dB. The prediction results under quadrature phase shift keying and quadrature amplitude modulation have similar error distribution. The mean square error loss value gradually decreases from the initial 0.25 to 0.05 during the training process, reflecting the improvement of prediction accuracy. The batch gradient descent uses 256 samples to calculate the gradient for each iteration, and the learning rate is 0.01 to ensure the stability of convergence, and the loss value tends to be stable after 1000 iterations. In the three modulation modes, the root mean square error of the predicted parameters is 0.08, 0.12, and 0.15, respectively, showing the difference in prediction performance under different modulation modes. In the prediction stage, the input channel spacing is 35MHz, and the predicted crosstalk strength is -38dB under binary phase shift keying modulation, with an accuracy of 0.93, which meets the threshold requirement of 0.9 and is directly output. In the quadrature phase shift keying modulation, the prediction accuracy is 0.87, which improves to 0.91 after 3 rounds of training iteration. In the quadrature amplitude modulation, after 5 rounds of training iteration, the accuracy is as high as 0.88, and the corresponding prediction result -35dB is output. The prediction result reflects the influence of the modulation mode on the relationship between the channel spacing and the crosstalk strength.

[0048] S130, based on the original data set, the performance of the to-be-selected quantitative relationship model is evaluated, and a target quantitative relationship model is determined.

[0049] The target quantitative relationship model can be the to-be-selected quantitative relationship model with the highest performance.

[0050] Specifically, the performance of the to-be-selected quantitative relationship model is evaluated by using the cross-validation method, and the fitting degree and generalization ability of the model are determined by using the mean square error and the determination coefficient index. The to-be-selected quantitative relationship model with the best performance is selected as the final target quantitative relationship model.

[0051] On the basis of the above technical solutions, the "performing performance evaluation on the to-be-selected quantitative relationship model based on the original data set, and determining the target quantitative relationship model" can include: determining a training set and a validation set in the original data set; determining candidate model parameters corresponding to qualified training results based on the training set, the to-be-selected quantitative relationship model, and a residual probability density curve generated by a preset bandwidth parameter Gaussian kernel density estimator; determining the candidate model parameters with the highest weighted prediction accuracy as the target model parameters based on the validation set and the candidate model parameters; and the target model parameters being model parameters corresponding to the target quantitative relationship model.

[0052] Specifically, the training set and the validation set in the original data set are determined. The training set and the validation set are obtained. The determination coefficient and the goodness of fit are calculated according to the training set, and a residual probability density curve is obtained by a Gaussian kernel density estimator with a bandwidth parameter threshold. Qualified training results are screened out according to the condition that the residual mean is less than a residual mean threshold and the standard deviation is less than a standard deviation threshold. The candidate model parameters are run on the validation set for the qualified training results. If the normalized error is less than a normalized error threshold, the validation result and the candidate model parameters are stored in a performance qualified set. If the normalized error is greater than the normalized error threshold, the validation result and the candidate model parameters are stored in a to-be-processed set. The validation data is predicted by the candidate model parameters in the performance qualified set. The validation set accuracy is used as a weight coefficient to calculate a weighted average score. The optimal candidate model parameters, i.e., the target model parameters, are selected according to the weighted average score, and are used to generate the target quantitative relationship model corresponding to the modulation mode.

[0053] Exemplarily, the data set of the quantitative relationship model is divided into a training set and a validation set in a proportion of 20% per fold by five-fold cross-validation. Five groups of training sets and validation sets are constructed respectively under three modulation modes of binary phase-shift keying, quadrature phase-shift keying and quadrature amplitude modulation. The mean square error value of the predicted value and the measured value is calculated by running the candidate quantitative relationship model. The prediction accuracy index under each modulation mode is calculated according to the signal-to-noise ratio weighting. The coefficient of determination is calculated for the prediction results of the training set. The goodness of fit is obtained by dividing the sum of squares of the deviation between the measured value and the predicted value by the sum of squares of the deviation between the measured value and the mean value. The residual probability density curve is generated by using a Gaussian kernel density estimator with a bandwidth parameter of 0.5. The qualified training results are screened according to the standard that the residual mean is less than 0.1 and the standard deviation is less than 0.3. The corresponding candidate model parameters are stored according to the modulation mode. The stored candidate model parameters are run on the validation set for prediction. The prediction error is normalized by using the ratio between the predicted value and the measured value. If the normalized error is less than 0.15, the validation result and the corresponding candidate model parameters are stored in the performance qualified set. If the normalized error is greater than 0.15, the validation result and the corresponding candidate model parameters are stored in the to-be-processed set, and the candidate model parameters in the to-be-processed set are fine-tuned and re-validated. The validation data is predicted according to the candidate model parameters in the performance qualified set, and the coefficient of determination and the mean square error of each group of prediction results are calculated. The prediction index under each modulation mode is weighted and averaged by using the validation set accuracy as the weight coefficient. The candidate model parameter with the highest weighted average score is selected as the optimal model parameter of the corresponding modulation mode, i.e. the target model parameter, so as to generate the optimal quantitative relationship model under different modulation modes, i.e. the target quantitative relationship model.

[0054] Further, five-fold cross-validation is implemented by data set rotation to achieve comprehensive evaluation. The validation set accounts for 20% of each round of division, corresponding to 1000 sample points. In the binary phase shift keying modulation mode, the channel interval is from 10MHz to 100MHz, the crosstalk degree is from-60dB to-20dB, and the signal-to-noise ratio is from 5dB to 30dB. When the signal-to-noise ratio is 20dB, the weight coefficient is 0.8, and when the signal-to-noise ratio is 10dB, the weight coefficient is 0.5. The weighted calculation obtains the prediction accuracy index 0.92. Quadrature phase shift keying and quadrature amplitude modulation adopt the same data division mode, and show different prediction accuracy distribution. The determination coefficient reflects the explanatory degree of the predicted value to the measured value. In the binary phase shift keying modulation, the measured crosstalk value-45dB corresponds to the predicted value-43dB, and the measured value-35dB corresponds to the predicted value-36dB, and the determination coefficient is calculated as 0.95. The bandwidth parameter 0.5 of the Gaussian kernel density estimator ensures the smoothness of the curve and reflects the residual distribution characteristics. The residual mean 0.08 is less than the threshold 0.1, and the standard deviation 0.25 is less than the threshold 0.3, and the corresponding model parameters meet the training requirements. The residual distribution under quadrature phase shift keying and quadrature amplitude modulation shows different concentration trends. Normalized prediction error eliminates the dimensional difference of crosstalk degree under different modulation modes. In the binary phase shift keying verification set, the ratio of the predicted crosstalk value to the measured value corresponding to the channel interval of 30MHz is 0.92, which is less than the threshold 0.15 and is classified into the performance qualified set. The prediction error in the quadrature phase shift keying verification set is 0.18, and after fine-tuning by reducing the kernel function parameter to 0.3, the error is 0.14, which enters the performance qualified set. The prediction error of the quadrature amplitude modulation verification set is reduced to 0.13 after multiple rounds of parameter fine-tuning. Comprehensive evaluation of model parameters under different modulation modes in the performance qualified set, the accuracy of the binary phase shift keying verification set is 0.94, corresponding to the weight 0.4, the accuracy of the quadrature phase shift keying is 0.91, corresponding to the weight 0.35, and the accuracy of the quadrature amplitude modulation is 0.89, corresponding to the weight 0.25. After weighted averaging, the binary phase shift keying model score 0.93 is the highest, and is determined as the optimal model under this modulation mode. The quadrature phase shift keying and quadrature amplitude modulation respectively select the model parameters corresponding to the scores 0.90 and 0.87 to form the optimal model set of the three modulation modes.

[0055] In the process of cross-operating system interconnection communication of the power terminal, the current crosstalk strength corresponding to the current channel interval parameter is determined based on the current channel interval parameter in the current communication environment and the target quantitative relationship model.

[0056] Specifically, the software-defined radio receiver is used to obtain the power terminal communication environment sampling data. The sampling data is subjected to fast Fourier transform to obtain the current channel interval parameter. The current modulation mode identification bit is extracted according to the operation identification field in the power terminal communication message header. The corresponding modulation parameter is obtained by the current modulation mode identification bit through the modulation parameter database index. The target quantitative relationship model is input for the current channel interval parameter, and the target quantitative relationship model is calculated by the Gaussian kernel function to obtain the crosstalk degree prediction value, i.e. the current crosstalk strength. The Kalman filter is used for smoothing processing for the crosstalk degree prediction value, and the smoothed crosstalk strength is divided according to the preset threshold to obtain the channel crosstalk grade marking data.

[0057] On the basis of the above technical solutions, the current crosstalk strength corresponding to the current channel interval parameter can be determined based on the current channel interval parameter and the target quantitative relationship model under the current communication environment, which includes: obtaining the communication environment data of the power terminal under the current communication environment, and determining the current channel interval parameter by Fourier transform; extracting the current modulation mode identification bit based on the operation identification field in the current communication message header of the power terminal; determining the current crosstalk strength corresponding to the current channel interval parameter based on the target quantitative relationship model corresponding to the current channel interval parameter and the current modulation mode identification bit.

[0058] Specifically, the power terminal communication environment is monitored by a software-defined radio receiver, the receiver sampling frequency is set to 100 MHz, the intermediate frequency sampler sampling bit number is 16 bits, and the current channel interval parameters are extracted from the spectrum data by fast Fourier transform. The sampling data is segmented according to the fixed frame length of 512 bytes. If the signal-to-noise ratio of the sampling data is greater than 10 dB, the input channel identifier is generated, and the current channel parameter set is generated. According to the operation identification field in the power terminal communication message header, the current operation state is judged, and the operation identification contains two types based on embedded real-time operation and open operation. The current modulation method identification bit is extracted by message analysis. Based on the index of the identification bit, the corresponding modulation parameter is obtained from the modulation parameter database, and matching is performed according to the binary phase shift keying, quadrature phase shift keying and quadrature amplitude modulation. For the identified modulation method, the prediction parameters (equivalent to target model parameters) in the quantitative relationship model library are read, the current channel interval parameters are input into the target quantitative relationship model composed of the prediction parameters, and the crosstalk degree prediction value is calculated by the Gaussian kernel function. The kernel function bandwidth parameter is set to 0.5. The prediction result is verified for effectiveness by the signal-to-noise ratio and the bit error rate threshold. If the verification is passed, the prediction value is output. If the verification is not passed, the resampling is returned. The corresponding data between the channel interval and the crosstalk strength is extracted from the prediction value, and the prediction data is smoothed by the Kalman filter. The observation noise variance is set to 0.1, and the process noise variance is set to 0.01. According to the crosstalk strength threshold -20 dB, -40 dB, -60 dB, the processing result is divided into three levels of high, medium and low, and the labeled channel crosstalk prediction data is generated.

[0059] Exemplarily, the software-defined radio receiver performs channel monitoring through a 100MHz sampling frequency, adapts to a 10MHz to 50MHz frequency band commonly used in power terminal communication, and 16-bit sampling precision meets a dynamic range requirement of -90dB to 0dB. Fast Fourier transform processes 512-byte fixed frame length data, and a frequency spectrum resolution reaches 0.2MHz. In a 20MHz channel spacing case, a frequency spectrum leakage of adjacent channels is less than -55dB. When the power terminal works in a 35MHz frequency band and a signal-to-noise ratio is 15dB, a channel identification accuracy rate reaches 96%. An operation identification field is located in the 4th byte to the 8th byte of a communication message header, an embedded real-time operation corresponds to an identification value 0x01, and an open operation corresponds to an identification value 0x02. A modulation mode identification bit is located in the 12th byte of the message header, binary phase shift keying corresponds to 0x11, quadrature phase shift keying corresponds to 0x12, and quadrature amplitude modulation corresponds to 0x13. In the 35MHz frequency band, the embedded real-time operation terminal adopts binary phase shift keying, and a data transmission rate is 10Mbps; the open operation terminal adopts quadrature phase shift keying, and a data transmission rate is 20Mbps. A Gaussian kernel function bandwidth parameter 0.5 provides a good smoothing characteristic when calculating a crosstalk degree. For the 35MHz frequency band binary phase shift keying modulation, a predicted crosstalk degree is -42dB when a channel spacing is 25MHz, and a measured value is -40dB; a predicted value is -58dB when the channel spacing is 40MHz, and a measured value is -55dB. When a signal-to-noise ratio is lower than 8dB or a bit error rate is higher than 0.001, a prediction result is determined as invalid and re-sampling is triggered. Deviation distribution of predicted values and measured values under quadrature phase shift keying is similar. Settings of an observation noise variance 0.1 and a process noise variance 0.01 of the Kalman filter reflect stability and dynamic characteristics of the prediction result. In the 35MHz frequency band under binary phase shift keying, original prediction data shows that the crosstalk is -38dB when the channel spacing is 20MHz, and is -35dB after being smoothed, and is divided into a medium level according to a preset threshold. When the channel spacing increases to 45MHz, the crosstalk degree decreases to -62dB, and is divided into a low level. The crosstalk degree under quadrature phase shift keying is generally 5dB to 8dB higher in the same frequency band, and the corresponding level division result is also adjusted, which embodies crosstalk characteristics of different modulation modes.

[0060] S150, dynamically adjusting the current channel spacing parameter based on the current crosstalk strength and a preset crosstalk strength threshold.

[0061] Specifically, based on the current crosstalk strength and the preset crosstalk strength threshold, it is judged whether the current channel spacing parameter meets the communication quality requirement of the message transmission. If the current crosstalk strength exceeds the preset crosstalk strength threshold, the current channel spacing parameter is dynamically adjusted until the communication quality requirement is met.

[0062] On the basis of the above technical solutions, the dynamic adjustment of the current channel spacing parameter based on the current crosstalk intensity and the preset crosstalk intensity threshold value can include: if the current crosstalk intensity is greater than the preset crosstalk intensity threshold value, determining a channel spacing correction value based on a step value corresponding to the current modulation mode; and dynamically adjusting the current channel spacing parameter based on the channel spacing correction value to obtain a target channel spacing parameter.

[0063] Specifically, the current crosstalk intensity is obtained, and it is determined whether the current crosstalk intensity exceeds the preset crosstalk intensity threshold value according to the preset crosstalk intensity threshold value. If the current crosstalk intensity exceeds the preset crosstalk intensity threshold value, a channel spacing correction value is calculated according to a step value corresponding to the current modulation mode. Gradient descent is used for iterative optimization of the channel spacing correction value, and communication quality data is collected at the optimized channel spacing. The Kalman filter is used to smooth the communication quality data, and the smoothed crosstalk value is obtained according to the signal amplitude state variable and the received signal strength observation variable; the channel characteristic curve is fitted using a cubic spline function, and the optimal channel spacing is determined according to the fluctuation range limit corresponding to the modulation mode. The optimal channel spacing is written into the communication control register.

[0064] Exemplarily, the current crosstalk intensity is obtained using a communication quality monitor, and the communication state is determined according to the preset quality threshold value. The crosstalk threshold value is -40 dB under binary phase shift keying modulation, -45 dB under quadrature phase shift keying modulation, and -50 dB under quadrature amplitude modulation. The channel parameter change curve is collected through cruise testing, and the corresponding relationship between the channel spacing and the crosstalk intensity is recorded. For the channel crosstalk exceeding the threshold value, the channel spacing correction value is calculated through adaptive adjustment, and the step size is determined according to the amplitude of the current crosstalk exceeding the threshold value. The step size is set to 5 MHz under binary phase shift keying, 8 MHz under quadrature phase shift keying, and 10 MHz under quadrature amplitude modulation. Gradient descent is used for iterative optimization of the channel spacing parameter, and the number of iterations is limited to 50 times, and the learning rate is set to 0.1. The communication quality data is collected at the updated channel spacing, and the data is smoothed by the Kalman filter. The signal amplitude is used as the state variable, the received signal strength is used as the observation variable, the observation noise variance is set to 0.1, and the process noise variance is set to 0.01. If the smoothed crosstalk value still exceeds the threshold value, the parameter adjustment link is repeatedly executed until the preset threshold requirement is met or the maximum number of iterations is reached. The channel characteristic curve is fitted using a cubic spline function, and the optimal channel spacing range is determined based on the minimum residual principle. The fluctuation range is limited to within 2 MHz under binary phase shift keying, within 3 MHz under quadrature phase shift keying, and within 4 MHz under quadrature amplitude modulation. The verified channel spacing parameter is written into the communication control register with an address offset of 0x1000.

[0065] Further, the communication quality monitor acquires channel crosstalk data in real time, and uses differentiated quality thresholds to make judgments under different modulation modes. When binary phase shift keying modulation is used, the crosstalk prediction value is -38 dB when the channel interval is 35 MHz, which exceeds the threshold of -40 dB, triggering channel adjustment. When quadrature phase shift keying is used, the crosstalk value is -42 dB when the channel interval is 40 MHz, which exceeds the threshold of -45 dB. When quadrature amplitude modulation is used, the crosstalk value is -46 dB when the channel interval is 45 MHz, which exceeds the threshold of -50 dB. The cruise test cycle is 1 second, and 100 data points are collected in each cycle to record the channel characteristics. In the adaptive adjustment process, the amplitude of the crosstalk exceeding the threshold determines the correction step. When the crosstalk exceeds 2 dB under binary phase shift keying, a step of 5 MHz is used, and when it exceeds 5 dB, the step is increased to 8 MHz. Under the initial state of a channel interval of 35 MHz, after 3 iterations, the channel interval is increased to 50 MHz, and the crosstalk value is reduced to -43 dB. Quadrature phase shift keying and quadrature amplitude modulation use similar adaptive mechanisms, but with larger steps to adapt to more stringent crosstalk requirements. The learning rate of 0.1 in gradient descent optimization ensures convergence stability, and the 50 iteration limit prevents over-adjustment. The Kalman filter estimates the state of the signal amplitude, and the observation noise variance of 0.1 corresponds to the measurement error, and the process noise variance of 0.01 reflects the channel state change characteristics. Under binary phase shift keying with a channel interval of 50 MHz, the original crosstalk measurement value fluctuates between -45 dB and -41 dB, and after filtering and smoothing, it stabilizes at -43 dB. Under quadrature phase shift keying with a channel interval of 60 MHz, the filtered crosstalk value stabilizes at -47 dB; under quadrature amplitude modulation with a channel interval of 70 MHz, the filtered crosstalk value stabilizes at -52 dB. The channel characteristic curve fitted by the cubic spline function reflects the correspondence between the channel interval and the crosstalk. In the binary phase shift keying mode, the crosstalk value fluctuates by less than 2 MHz in the range of 45 MHz to 55 MHz, which is determined as the optimal interval. The optimal interval of quadrature phase shift keying is 55 MHz to 65 MHz, and the optimal interval of quadrature amplitude modulation is 65 MHz to 75 MHz. The communication control register stores parameters in byte alignment, with the high 4 bits storing the modulation mode identifier and the low 12 bits storing the channel interval value. The parameter update delay during operation is less than 1 millisecond, ensuring real-time adjustment of communication quality.

[0066] The technical scheme of the embodiment of the present application comprises the following steps: obtaining original data sets of a channel of a power terminal across operating systems under various modulation modes; the original data sets comprise channel spacing parameters and crosstalk intensity; training a preset quantitative relationship model based on the original data sets and a support vector machine regression algorithm to determine a to-be-selected quantitative relationship model; the preset quantitative relationship model is used to reflect the mapping relationship between the channel spacing parameters and the crosstalk intensity under each modulation mode; performing performance evaluation on the to-be-selected quantitative relationship model based on the original data sets to determine a target quantitative relationship model; in the process of cross-operating system interconnection communication of the power terminal, determining the current crosstalk intensity corresponding to the current channel spacing parameters based on the current channel spacing parameters under the current communication environment and the target quantitative relationship model; and performing dynamic adjustment of the current channel spacing parameters based on the current crosstalk intensity and a preset crosstalk intensity threshold, so as to accurately and conveniently realize dynamic adjustment of the channel spacing parameters, thereby avoiding crosstalk between channels and improving the communication quality of the cross-operating system interconnection communication of the power terminal.

[0067] On the basis of the above technical scheme, the method further comprises the following steps: obtaining the optimal channel spacing meeting the communication quality requirement by using the prediction result of the quantitative relationship model of the channel spacing and the crosstalk intensity under different modulation modes, and applying the optimal channel spacing to the parameter configuration of the power line carrier communication module in the cross-operating system interconnection communication scenario of the power terminal, so as to adaptively adjust the receiving threshold of the communication module. If the crosstalk degree prediction value (equivalent to the current crosstalk intensity) of the current channel spacing exceeds the crosstalk degree prediction value threshold (equivalent to the preset crosstalk intensity threshold), the receiving threshold is increased to strengthen the message checking; the content, the time stamp, the source node ID and the unique identifier of the received new message are obtained, and the content, the time stamp and the unique identifier of the message in the recently received message cache are compared for matching; if all conditions are met, the new message is regarded as a repeated message and discarded without further processing; if no matching cache message is found, the new message is added to the cache.

[0068] Exemplarily, the prediction result of the quantitative relationship model of the channel spacing and the crosstalk intensity under different modulation modes is used to obtain the optimal channel spacing meeting the communication quality requirement, and is applied to the parameter configuration of the power line carrier communication module in the power terminal cross-operating system interconnection communication scene, so as to adaptively adjust the receiving threshold of the communication module. If the crosstalk degree prediction value of the current channel spacing exceeds the crosstalk degree prediction value threshold, the receiving threshold is increased to strengthen the message checking. It can include: obtaining the crosstalk degree prediction value of the current channel spacing, and reading the running parameters from the power line carrier communication module memory according to the crosstalk prediction value. The binary phase shift keying, quadrature phase shift keying and quadrature amplitude modulation corresponding carrier communication parameter combinations are generated for the running parameters. If the crosstalk degree prediction value exceeds the crosstalk degree prediction value threshold, the check bit length is generated according to the carrier communication parameter combination corresponding to the modulation mode, and the enhanced check communication message is obtained by using the cyclic redundancy code generation polynomial. The Kalman filter is constructed by using the enhanced check communication message, and if the continuous test period bit error rate exceeds the period bit error rate threshold, the receiving threshold is increased, and the updated carrier communication module running parameters are obtained.

[0069] Specifically, the crosstalk degree of the current channel spacing is evaluated by using the crosstalk predictor. The crosstalk prediction value under each modulation mode is calculated by support vector regression of radial basis kernel function. The minimum channel spacing is extracted according to the channel quality threshold of-20dB, and the deviation between the measured crosstalk value and the prediction value in the cross-operating system communication scene is corrected online. The optimal channel spacing parameters are generated by verifying the channel quality indicators of each frequency band through cruise testing. The current running parameters are read from the power line carrier communication module memory, and the corresponding parameter configuration reference values are extracted according to the terminal attributes based on embedded real-time operation and open operation. The receiving threshold is quantized by the bit error rate threshold of 0.001, and the quantization interval is from-85dBm to-35dBm. The carrier communication parameter combinations are generated for binary phase shift keying, quadrature phase shift keying and quadrature amplitude modulation respectively. The signal quality is judged according to the crosstalk prediction value, and when the crosstalk degree exceeds the preset threshold of-40dB, the check bit length is adaptively adjusted according to the modulation mode. The binary phase shift keying check bit is set to 16 bits, the quadrature phase shift keying check bit is set to 24 bits, and the quadrature amplitude modulation check bit is set to 32 bits. The communication message is encoded by using the cyclic redundancy code generation polynomial to generate the enhanced check communication message. The Kalman filter is constructed by using the enhanced check communication message for channel transmission test, taking the signal amplitude as the state variable and the received signal strength as the observation variable. The observation noise variance is set to 0.1, and the process noise variance is set to 0.01. When the bit error rate of 5 consecutive test periods exceeds 0.0001, the receiving threshold is increased by 2dB, and when the bit error rate of 10 consecutive test periods is less than 0.00001, the receiving threshold is reduced by 1dB, and the carrier communication module running parameters are updated.

[0070] Further, the radial basis function support vector regression shows good nonlinear mapping ability in processing the power line carrier communication channel characteristics. When the channel interval is 35 MHz, the predicted crosstalk value is -38 dB under binary phase shift keying modulation, and the measured value is -35 dB; the predicted value is -32 dB under quadrature phase shift keying, and the measured value is -30 dB. In the online correction process, the terminal cruise test based on embedded real-time operation shows that the average crosstalk in the frequency band of 35 MHz to 45 MHz is -42 dB, which meets the quality threshold requirement of -20 dB. The average crosstalk in the same frequency band test of the open operation terminal is -38 dB. The power line carrier communication module operating parameter memory saves the last 100 groups of historical parameter records. Based on the embedded real-time operation terminal, when the bit error rate threshold is 0.001, the typical value of the receiving threshold is -75 dBm; the typical value of the open operation terminal is -70 dBm. In the binary phase shift keying modulation mode, when the channel interval is 40 MHz, the signal receiving level is -65 dBm; the receiving level is -60 dBm under quadrature phase shift keying; and the receiving level is reduced to -55 dBm under quadrature amplitude modulation, which reflects the requirements of different modulation modes on receiving sensitivity. The adaptive adjustment of the cyclic redundancy check bit reflects the difference in anti-interference ability of different modulation modes. When the crosstalk exceeds the threshold of -40 dB, the binary phase shift keying adopts 16-bit check to correct 2-bit random error; the quadrature phase shift keying adopts 24-bit check to correct 4-bit random error; and the quadrature amplitude modulation adopts 32-bit check to correct 6-bit random error. The generating polynomial adopts the form of x16+x12+x5+1 recommended in the standard specification, and the bit error rate is reduced by 3 orders of magnitude in the 35 MHz frequency band test, and x is a position marker of a binary number. The Kalman filter realizes dynamic adjustment of the receiving threshold through recursive estimation, the observation noise variance 0.1 corresponds to the uncertainty of signal amplitude measurement, and the process noise variance 0.01 reflects the slow change characteristics of the channel state. In the binary phase shift keying 35 MHz frequency band transmission test, when the bit error rate reaches 0.0002 for 5 consecutive periods, the receiving threshold is increased from -75 dBm to -73 dBm, and the communication quality is improved; when the bit error rate is reduced to 0.000008 for 10 consecutive periods, the receiving threshold is reduced to -74 dBm accordingly, and the channel utilization is optimized. The threshold adjustment of quadrature phase shift keying and quadrature amplitude modulation follows similar rules, which reflects the adaptability of the adaptive mechanism to different modulation modes.

[0071] Exemplarily, "obtaining the content, timestamp, source node ID and unique identifier of the received new packet, and comparing the content, timestamp and unique identifier with the packets in the recent received packet cache; if all conditions are met, it is considered as a duplicate packet and discarded, and no further processing is performed; if no matching cache packet is found, the new packet is added to the cache" can include: obtaining the packet content of the content field, timestamp field, source node identifier field and unique identifier field of the new packet. The packet feature value is generated by the secure hash algorithm on the packet content. According to the packet feature value, the Bloom filter is used to match and find the packet feature vector from the cache area, and the time interval is calculated by the timestamp. If the matching result is found by the Bloom filter and the time interval is less than the time interval threshold, the packet content is checked for features. The new packet with a non-duplicate packet feature check result is stored in the cache area packet sequence.

[0072] Specifically, the data extractor is used to parse the new packet according to the field start position and length, obtain the packet content of bytes 0 to 511, the timestamp of bytes 512 to 519, the source node identifier of bytes 520 to 527, and the unique identifier of bytes 528 to 535. A 256-bit feature value is generated by the secure hash algorithm SHA-256 on the packet content, an index key value is constructed according to the identifier, the integrity of the feature value is verified by the one-way check function, and a new packet feature vector is generated. The latest received packet feature vector is extracted from the cache packet area. Three hash functions and a Bloom filter with a bit array size of 8 times the number of packets are used for matching and finding. Whether the interval is less than 100 milliseconds is determined by the timestamp difference calculator, the source node identifier and the unique identifier are compared based on the bitmap index, the sliding time window is used to sequentially process multiple duplicate packets that arrive at the same time, and the comparison result is obtained. According to the comparison result, the duplicate state is determined, and if the new packet feature vector exists in the Bloom filter and the time interval is less than the threshold. The message digest algorithm MD5 is used for secondary verification of the packet content, and the 16-byte digest value in the comparison verification result is compared. The packets marked as duplicates are added to the discard record table, and the discard statistical information is updated according to the time sequence. The non-duplicate packets are cached, the cache area packet sequence is maintained by the circular queue manager, and the current cache packet number is recorded by the counter. If the count value exceeds the preset maximum value 1024, the queue head packet is deleted, the new packet is inserted into the queue tail according to the arrival timestamp, and the cache area is automatically updated.

[0073] Further, the data extractor parses the message field by fixed length, the message content 512 bytes stores complete service data, the time stamp 8 bytes records the generation time accurate to millisecond, the source node identification 8 bytes records the unique number of the sending device, and the unique identifier 8 bytes uses the incremental serial number. In actual application, the message content is the power distribution network monitoring data, the feature value length generated by SHA-256 is 256 bits, and the one-way check function guarantees the integrity of the feature value through the cyclic redundancy check. When a plurality of messages are continuously received, the feature vectors are stored in sequence according to the arrival order. The Bloom filter uses three hash functions to map the feature vectors. If the cache area stores 128 messages, the size of the bit array is set to 1024 bits. In the power distribution network monitoring scenario, the time stamp difference of adjacent messages is set to a threshold of 100 milliseconds, the source node identification uses the device factory number, and the bitmap index accelerates the matching process. When a plurality of repeated messages arrive simultaneously within 80 milliseconds, the sliding time window processes in sequence at an interval of 5 milliseconds, avoiding missed judgment and misjudgment. The message digest algorithm MD5 performs secondary verification on the repeated messages, generates a 16-byte digest value, and the verification result completely matches the repeated messages. In actual operation, the power distribution network monitoring device sends 2 messages per second, and if a repeated message is detected, the time stamp, source node identification and unique identifier are recorded in the discarded record table, and the repetition rate is counted according to hours. When the repetition rate exceeds 1%, the sampling frequency is automatically adjusted. The circular queue uses a fixed length storage space of 1024 messages, and the counter updates the current cache message number in real time. In the power distribution network monitoring application, the message arrival interval is 500 milliseconds, and the queue stores in sequence according to the time stamp index. When the number of messages in the cache area reaches 1024, the earliest 64 messages are deleted to reserve space for new messages. During operation, the cache area space utilization rate is maintained at 85% to 95%, ensuring the real-time performance and accuracy of message comparison.

[0074] On the basis of the above technical solutions, in the actual power line carrier communication process, the channel interval and the crosstalk strength are continuously monitored, the communication quality of the message transmission is predicted in real time, and the channel configuration is dynamically optimized according to the communication quality prediction result.

[0075] Among them, the communication state monitor is used to obtain channel state data. The channel state data includes crosstalk value, signal-to-noise ratio and bit error rate. The crosstalk prediction value is calculated by support vector regression. The communication quality score is calculated according to the crosstalk value and the signal-to-noise ratio and the bit error rate according to the preset weight. The preset weight includes crosstalk weight, signal-to-noise ratio weight and bit error rate weight. The Kalman filter is used for adjusting the transmission power according to the communication quality score, and the filtered transmission power value is obtained. If the crosstalk prediction value exceeds the crosstalk prediction threshold, the transmission power increase, modulation mode switching or channel interval update are triggered according to the crosstalk prediction value.

[0076] Specifically, the communication state monitor is used to collect channel state data, including crosstalk value, signal-to-noise ratio, and bit error rate. The support vector regression of the radial basis kernel function is used to calculate the crosstalk prediction value. The kernel parameter is set to 0.5, and the iteration number is limited to 100 times. The communication quality score is calculated based on the weighted summation method, with a crosstalk weight of 0.4, a signal-to-noise ratio weight of 0.3, and a bit error rate weight of 0.3. The operation type is marked based on the score data in the cross-operating system interconnection scenario. The transmission power is adjusted according to the communication quality score. The Kalman filter is used for smoothing processing, with signal amplitude as the state variable and received power as the observation variable. The observation noise variance is set to 0.1, and the process noise variance is set to 0.01. In the terminal based on embedded real-time operation and open operation, the corresponding modulation mode is selected, the channel parameters are configured according to the frequency band occupation table, the frequency band range includes 6 frequency bands from 10MHz to 100MHz, and the power level, modulation mode, and frequency band number are written into the communication register. Forward error correction coding is used to process communication data. The error correction code length is 16 bits for binary phase shift keying modulation, 24 bits for quadrature phase shift keying modulation, and 32 bits for quadrature amplitude modulation. The cyclic redundancy check is performed through the standard polynomial x16+x12+x5+1. After generating the check code, the data frame is added with a synchronization header and a frame tail identifier. After data encapsulation, the error correction capability is verified. According to the optimization order of terminal type priority, modulation mode sub-optimization, and channel parameter optimization, it is detected whether the crosstalk prediction value exceeds the threshold. If the threshold is exceeded, the parameter optimization is triggered. If the crosstalk exceeds -40dB, the transmission power is increased. If the crosstalk exceeds -45dB, the modulation mode is switched. If the crosstalk exceeds -50dB, the channel interval is updated. The communication quality indicators of the optimized parameters are judged through online cruise testing, and the parameter configuration is updated after verification.

[0077] Further, the communication state monitor collects channel state data every 100 milliseconds, and in the terminal based on the embedded real-time operation, the display string value of a certain collection is -38 dB, the signal-to-noise ratio is 15 dB, and the error rate is 0.001. The support vector regression adopts the radial basis kernel function to process the data, and after 85 iterations, the crosstalk prediction value converges to -36 dB. In the communication quality score calculation, the crosstalk value weight 0.4 corresponds to the score 3.6, the signal-to-noise ratio weight 0.3 corresponds to the score 4.2, and the error rate weight 0.3 corresponds to the score 3.8. The weighted sum obtains the final score 3.85. The open operation terminal scores 4.1 under the same configuration. The Kalman filter smoothes the transmission power adjustment, and the observation noise variance 0.1 reflects the uncertainty of the power measurement, and the process noise variance 0.01 reflects the stability of the power adjustment. In the terminal based on the embedded real-time operation, when the transmission power is adjusted from 20 dBm to 23 dBm, the received signal strength is improved from -75 dBm to -70 dBm. The modulation mode is switched from binary phase shift keying to quadrature phase shift keying, the frequency band is adjusted from 35 MHz to 45 MHz, and the communication register stores the modulation identifier in the high 4 bits, the frequency band number in the middle 8 bits, and the power level in the low 4 bits. The forward error correction coding adopts a differentiated error correction strategy for different modulation modes. Under binary phase shift keying, 16-bit error correction code can correct 2-bit random error, under quadrature phase shift keying, 24-bit error correction code can correct 4-bit random error, and under quadrature amplitude modulation, 32-bit error correction code can correct 6-bit random error. The cyclic redundancy check adopts a standard polynomial to generate the check code, the data frame structure includes an 8-byte synchronization header, a 512-byte data segment, a 4-byte check code, and a 4-byte frame tail, and the error correction capability is verified by injecting random errors. The parameter optimization process follows a strict priority order. When the crosstalk prediction value is -38 dB, the transmission power is preferentially increased by 3 dB; when the crosstalk prediction value reaches -43 dB, the modulation mode is switched from binary phase shift keying to quadrature phase shift keying; and when the crosstalk prediction value is -48 dB, the channel interval is increased from 35 MHz to 45 MHz. The online cruise test shows that the signal-to-noise ratio is improved by 2 dB after the transmission power is adjusted, the data throughput is improved by 50% after the modulation mode is switched, and the crosstalk is reduced by 5 dB after the channel interval is updated. The optimized parameter combination meets the communication quality indicators in 100 tests.

[0078] It should be noted that the embodiment of the application discloses a cross-operating system interconnection communication method based on a power terminal. In view of the problem that when the power line carrier communication is cross-operating system interconnected, the unreasonable channel spacing setting leads to serious crosstalk, and the communication quality is affected, the application establishes a quantitative relationship model of channel spacing and crosstalk strength under different modulation modes. First, the channel characteristic parameters under different modulation modes are obtained, the model is trained after preprocessing by using a support vector machine regression algorithm, and the model performance is evaluated by using a cross-validation method. Then, according to the current channel spacing parameters of the current communication environment, the crosstalk strength is predicted by using the model, and the receiving threshold of the communication module is adaptively adjusted according to the prediction result, so as to strengthen the message checking. Meanwhile, the application designs a repeated message recognition and discarding mechanism, which avoids repeated processing by comparing the message content, the time stamp, the source node and the unique identifier. Finally, according to the crosstalk strength prediction result (the current crosstalk strength), the channel spacing and other parameters are dynamically adjusted until the communication quality requirements are met, and the channel state is monitored in real time, and the channel configuration parameters are dynamically optimized, so as to ensure that the cross-operating system interconnection communication of the power terminal is stable and reliable. By establishing the quantitative relationship between the channel spacing and the crosstalk strength, and combining the adaptive receiving threshold adjustment, the repeated message discarding and the dynamic channel configuration optimization, the application effectively solves the crosstalk problem in the cross-operating system interconnection scene of the power line carrier communication, and significantly improves the communication quality and reliability.

[0079] The following is an embodiment of a cross-operating system interconnection communication device based on a power terminal provided by the embodiment of the application. The device and the cross-operating system interconnection communication method based on a power terminal of each of the above embodiments belong to the same inventive concept. Details not described in the embodiment of the cross-operating system interconnection communication device based on a power terminal can be referred to the embodiment of the cross-operating system interconnection communication method based on a power terminal.

[0080] Embodiment two

[0081] Figure 2 A structure schematic diagram of a cross-operating system interconnection communication device based on a power terminal provided by the embodiment two of the application is shown in the figure. Figure 2 As shown in the figure, the device comprises an original data set acquisition module 210, a to-be-selected quantitative relationship model determination module 220, a target quantitative relationship model determination module 230, a current crosstalk strength determination module 240 and a dynamic adjustment module 250.

[0082] The original data set acquisition module 210 is configured to acquire original data sets of a channel of a power terminal across operating systems under various modulation modes; the original data set includes a channel spacing parameter and a crosstalk strength; the to-be-selected quantitative relationship model determination module 220 is configured to train a preset quantitative relationship model based on the original data set and a support vector machine regression algorithm, and determine a to-be-selected quantitative relationship model; the preset quantitative relationship model is used to reflect a mapping relationship between the channel spacing parameter and the crosstalk strength under each modulation mode; the target quantitative relationship model determination module 230 is configured to perform performance evaluation on the to-be-selected quantitative relationship model based on the original data set, and determine a target quantitative relationship model; the current crosstalk strength determination module 240 is configured to determine a current crosstalk strength corresponding to a current channel spacing parameter based on the current channel spacing parameter under a current communication environment and the target quantitative relationship model in a process of interconnecting communication of the power terminal across operating systems; and the dynamic adjustment module 250 is configured to perform dynamic adjustment of the current channel spacing parameter based on the current crosstalk strength and a preset crosstalk strength threshold.

[0083] The technical scheme of the embodiment of the application acquires original data sets of a channel of a power terminal across operating systems under various modulation modes; the original data set includes a channel spacing parameter and a crosstalk strength; a preset quantitative relationship model is trained based on the original data set and a support vector machine regression algorithm, and a to-be-selected quantitative relationship model is determined; the preset quantitative relationship model is used to reflect a mapping relationship between the channel spacing parameter and the crosstalk strength under each modulation mode; performance evaluation is performed on the to-be-selected quantitative relationship model based on the original data set, and a target quantitative relationship model is determined; in a process of interconnecting communication of the power terminal across operating systems, a current crosstalk strength corresponding to a current channel spacing parameter is determined based on the current channel spacing parameter under a current communication environment and the target quantitative relationship model; and dynamic adjustment of the current channel spacing parameter is performed based on the current crosstalk strength and a preset crosstalk strength threshold, so that dynamic adjustment of the channel spacing parameter is accurately and conveniently realized, crosstalk between channels is avoided, and the communication quality of the interconnecting communication of the power terminal across operating systems is improved.

[0084] On the basis of the above technical scheme, the original data set acquisition module 210 is specifically configured to acquire phase parameters, amplitude parameters and spectral feature parameters output by a modem under each modulation mode; perform data preprocessing based on the phase parameters, the amplitude parameters and the spectral feature parameters, and determine a preprocessed data set under each modulation mode; perform feature extraction and spectral distribution based on the preprocessed data set, and determine a channel characteristic parameter; and establish a mapping relationship between the channel spacing parameter and the crosstalk strength based on the channel characteristic parameter, to obtain the original data set of the channel of the power terminal across operating systems under each modulation mode.

[0085] On the basis of the above technical solutions, the to-be-selected quantitative relationship model determination module 220 is specifically configured to: perform standardization processing and feature screening based on the original data set to determine a standardized feature matrix; determine training parameters based on a support vector machine regression algorithm and mapping features corresponding to training data in the standardized feature matrix; the training parameters are model parameters corresponding to a preset quantitative relationship model; determine regression prediction parameters based on the training parameters and verification data in the standardized feature matrix; the regression prediction parameters are model parameters corresponding to the to-be-selected quantitative relationship model.

[0086] On the basis of the above technical solutions, the target quantitative relationship model determination module 230 is specifically configured to: determine a training set and a verification set in the original data set; determine candidate model parameters corresponding to a qualified training result based on the training set, the to-be-selected quantitative relationship model, and a residual probability density curve generated by a preset bandwidth parameter Gaussian kernel density estimator; determine candidate model parameters with the highest weighted prediction accuracy as target model parameters based on the verification set and the candidate model parameters; the target model parameters are model parameters corresponding to the target quantitative relationship model.

[0087] On the basis of the above technical solutions, the current crosstalk strength determination module 240 is specifically configured to: acquire communication environment data of the power terminal in a current communication environment, and determine a current channel interval parameter through Fourier transform; extract a current modulation mode identification bit based on an operation identification field in a current communication message header of the power terminal; determine a current crosstalk strength corresponding to the current channel interval parameter based on a target quantitative relationship model corresponding to the current channel interval parameter and the current modulation mode identification bit.

[0088] On the basis of the above technical solutions, the dynamic adjustment module 250 is specifically configured to: if the current crosstalk strength is greater than a preset crosstalk strength threshold, determine a channel interval correction value based on a step value corresponding to the current modulation mode; dynamically adjust the current channel interval parameter based on the channel interval correction value to obtain a target channel interval parameter.

[0089] The power terminal cross-operating system interconnection communication device provided in the embodiments of the present application can execute the power terminal cross-operating system interconnection communication method provided in any of the embodiments of the present application, and has the corresponding functional modules and beneficial effects of executing the power terminal cross-operating system interconnection communication method.

[0090] It should be noted that, in the above embodiments of the power terminal cross-operating system interconnection communication, each unit and module included is only divided according to functional logic, but is not limited to the above division, as long as the corresponding functions can be implemented; in addition, the specific names of each functional unit are only for easy mutual differentiation, and do not serve to limit the protection scope of the present application.

[0091] Embodiment three

[0092] Figure 3 A structural diagram of an electronic device 10 that can be used to implement embodiments of the present application is shown. The electronic device is intended to represent various forms of digital computers, such as laptops, desktops, tablets, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular telephones, smart phones, wearable devices (e.g., headsets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not intended to limit the implementations of the present application described and / or claimed in this document.

[0093] As shown in Figure 3 The electronic device 10 includes at least one processor 11, and a memory, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., connected in communication with the at least one processor 11, where the memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes according to the computer programs stored in the read-only memory (ROM) 12 or loaded into the random access memory (RAM) 13 from the storage unit 18. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0094] A plurality of components in the electronic device 10 are connected to the I / O interface 15, including an input unit 16, such as a keyboard, a mouse, etc., an output unit 17, such as various types of displays, a speaker, etc., a storage unit 18, such as a magnetic disk, an optical disk, etc., and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network, such as the Internet, and / or various telecommunication networks.

[0095] The processor 11 can be various general and / or special-purpose processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 performs various methods and processes described above, such as the cross-OS intercommunication communication method based on power terminals.

[0096] In some embodiments, the power terminal cross-OS interconnection communication method can be implemented as a computer program tangibly embodied in a computer readable storage medium, e.g., storage unit 18. In some embodiments, parts or all of the computer program can be loaded and / or installed onto electronic device 10 via, e.g., ROM 12 and / or communication unit 19. When the computer program is loaded onto RAM 13 and executed by processor 11, one or more steps of the power terminal cross-OS interconnection communication method described above can be performed. Alternatively, in other embodiments, processor 11 can be configured to perform the power terminal cross-OS interconnection communication method by other any suitable means, e.g., by way of firmware.

[0097] Various implementations of the systems and techniques described above can be realized in digital electronic circuitry, integrated circuitry, a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), a system on a chip (SOC), a programmable logic device (PLD), a computer hardware, firmware, software, and / or combinations thereof. These various implementations can include implementation in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which can be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.

[0098] Computer programs used to implement the processes of the application can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the computer program, when executed by the processor, implements the functions / acts specified in the flowcharts and / or block diagrams. The computer program can be executed entirely on a machine, partially on a machine, partially on a machine as part of a standalone software package, partially on a machine and partially on a remote machine or entirely on a remote machine or server.

[0099] In the context of the present application, a computer-readable storage medium can be a tangible medium that can contain or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. A computer-readable storage medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium can be a machine-readable signal medium. More specific examples of a machine-readable storage medium will include one or more lines of a program of instructions in a transitory signal, 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 disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0100] To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the electronic device. Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form, including acoustic, speech, or tactile input.

[0101] The systems and techniques described here can be implemented in a computing system that includes a back end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front end component (e.g., a user computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back end, middleware, or front end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0102] The computing system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. Servers can be cloud servers, also known as cloud computing servers or cloud hosts, which are a host product in the cloud computing service system to solve the defects of great management difficulty and weak business scalability in traditional physical hosts and VPS services.

[0103] The embodiment of the present application further provides a computer program product comprising a computer program which, when executed by a processor, implements the cross-operating system interconnection communication method based on a power terminal as provided in any embodiment of the present application.

[0104] The computer program product can be written in one or more programming languages or combinations of languages including object-oriented languages, such as Java, Smalltalk, C++, 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). The program product of the present application and the cross-operating system interconnection communication method based on a power terminal disclosed in the embodiments of the present application belong to the same inventive concept, and thus are not described herein.

[0105] It should be understood that the various forms of flow shown above can be re-ordered, added to, or deleted from without departing from the scope of the present application. For example, the steps recited in the present application can be performed in parallel, in series, or in a different order, without departing from the desired results of the technical solutions of the present application, and are not limited herein.

[0106] The above detailed description does not constitute a limitation on the protection scope of the present application. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent replacements, and improvements made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A method for cross-OS interconnection communication based on power terminal, characterized in that, The method comprises the following steps: acquiring original data sets of a channel of a power terminal across operating systems under various modulation modes; the original data sets comprise channel spacing parameters and crosstalk intensity; training a preset quantitative relationship model based on the original data sets and a support vector machine regression algorithm, and determining a to-be-selected quantitative relationship model; the preset quantitative relationship model is used to reflect a mapping relationship between the channel spacing parameters and the crosstalk intensity under each modulation mode; performing performance evaluation on the to-be-selected quantitative relationship model based on the original data sets, and determining a target quantitative relationship model; in a process of interconnecting and communicating between the power terminal across operating systems, determining a current crosstalk intensity corresponding to a current channel spacing parameter based on a current channel spacing parameter under a current communication environment and the target quantitative relationship model; performing dynamic adjustment on the current channel spacing parameter based on the current crosstalk intensity and a preset crosstalk intensity threshold.

2. The method of claim 1, wherein, The method of acquiring the original data sets of the channel of the power terminal across operating systems under various modulation modes comprises the following steps: acquiring phase parameters, amplitude parameters and spectral feature parameters output by a modem under each modulation mode; performing data preprocessing based on the phase parameters, the amplitude parameters and the spectral feature parameters, and determining a preprocessed data set under each modulation mode; performing feature extraction and spectral distribution based on the preprocessed data set, and determining channel characteristic parameters; establishing a mapping relationship between the channel spacing parameters and the crosstalk intensity based on the channel characteristic parameters, and obtaining the original data sets of the channel of the power terminal across operating systems under various modulation modes.

3. The method of claim 1, wherein, The method of training the preset quantitative relationship model based on the original data sets and the support vector machine regression algorithm, and determining the to-be-selected quantitative relationship model comprises the following steps: performing standardization processing and feature screening based on the original data sets, and determining a standardized feature matrix; determining training parameters based on a mapping feature corresponding to training data in the standardized feature matrix by using the support vector machine regression algorithm; the training parameters are model parameters corresponding to the preset quantitative relationship model; determining regression prediction parameters based on verification data in the standardized feature matrix by using the training parameters; the regression prediction parameters are model parameters corresponding to the to-be-selected quantitative relationship model.

4. The method of claim 1, wherein, The method of performing performance evaluation on the to-be-selected quantitative relationship model based on the original data sets, and determining the target quantitative relationship model comprises the following steps: determining a training set and a verification set in the original data sets; determining candidate model parameters corresponding to a qualified training result based on the training set, the to-be-selected quantitative relationship model and a residual probability density curve generated by a preset bandwidth parameter Gaussian kernel density estimator; determining the candidate model parameters with the highest weighted prediction accuracy as target model parameters based on the verification set and the candidate model parameters; the target model parameters are model parameters corresponding to the target quantitative relationship model.

5. The method of claim 1, wherein, The method of determining the current crosstalk intensity corresponding to the current channel spacing parameter based on the current channel spacing parameter under the current communication environment and the target quantitative relationship model comprises the following steps: acquiring communication environment data of the power terminal under the current communication environment, and determining the current channel spacing parameter by using Fourier transform; extract a current modulation mode identification bit from an operation identification field in a current communication packet header of the power terminal; determine a current crosstalk intensity corresponding to the current channel interval parameter based on the current channel interval parameter and a target quantitative relationship model corresponding to the current modulation mode identification bit.

6. The method of claim 1, wherein, The dynamic adjustment of the current channel interval parameter based on the current crosstalk intensity and a preset crosstalk intensity threshold value comprises: if the current crosstalk intensity is greater than the preset crosstalk intensity threshold value, determining a channel interval correction value based on a step value corresponding to the current modulation mode; dynamically adjusting the current channel interval parameter based on the channel interval correction value to obtain a target channel interval parameter.

7. An apparatus for cross-OS interconnection communication based on power termination, comprising: The device comprises: an original data set acquisition module configured to acquire original data sets of a channel of a power terminal across operating systems under various modulation modes; the original data sets comprise a channel interval parameter and a crosstalk intensity; a candidate quantitative relationship model determination module configured to train a preset quantitative relationship model based on the original data sets and a support vector machine regression algorithm, and determine a candidate quantitative relationship model; the preset quantitative relationship model is used to reflect a mapping relationship between the channel interval parameter and the crosstalk intensity under each modulation mode; a target quantitative relationship model determination module configured to perform performance evaluation on the candidate quantitative relationship model based on the original data sets, and determine a target quantitative relationship model; a current crosstalk intensity determination module configured to determine a current crosstalk intensity corresponding to a current channel interval parameter in a power terminal cross-operating system interconnection communication process based on the current channel interval parameter and the target quantitative relationship model under a current communication environment; a dynamic adjustment module configured to perform dynamic adjustment of the current channel interval parameter based on the current crosstalk intensity and a preset crosstalk intensity threshold value.

8. An electronic device, comprising: The electronic device comprises: one or more processors; a memory configured to store one or more programs; when the one or more programs are executed by the one or more processors, the one or more processors implement the power terminal cross-operating system interconnection communication method according to any one of claims 1-6.

9. A computer readable storage medium having stored thereon a computer program, characterized in that, The program is executed by the processor to implement the power terminal cross-operating system interconnection communication method according to any one of claims 1-6.

10. A computer program product comprising a computer program, characterized in that, The computer program is executed by the processor to implement the power terminal cross-operating system interconnection communication method according to any one of claims 1-6.

Images