HPLC and micropower wireless dual-mode communication frequency self-adaption method

Abstract

The invention provides an HPLC (High Performance Liquid Chromatography) and micropower wireless dual-mode communication frequency self-adaption method, which belongs to the technical field of dual-mode communication and comprises the following steps: firstly, establishing dual-mode communication by initializing parameters such as carrier frequency, sending power and modulation mode; secondly, electromagnetic interference signals in a power line channel are collected, and a linear superposition noise model containing a time domain weight is established to predict noise characteristics; meanwhile, a zero-crossing point time sequence of the power line signal is analyzed, and a zero-crossing point feature vector is constructed. And in combination with noise spectrum prediction and zero crossing point features, solving a convex optimization problem to obtain an optimal carrier frequency and other communication parameters. For micropower wireless communication, a channel quality evaluation matrix is constructed based on indexes such as signal strength, channel busy degree and bit error rate, an interference prediction model is established, and wireless communication parameters are optimized through quadratic programming. And finally, establishing a switching decision function according to a set communication quality threshold, and realizing self-adaptive switching of dual-mode communication so as to ensure the overall communication quality.

Description

Technical Field

[0001] The invention belongs to the technical field of dual-mode communication, and in particular relates to a frequency adaptation method for dual-mode communication of HPLC and micro-power wireless. Background Art

[0002] In recent years, with the widespread application of smart grid and Internet of Things technologies, the demand for information transmission has continued to increase. As two important information transmission technologies, high-speed power line carrier communication and micro-power wireless communication play a key role in this field.

[0003] High-speed power line carrier communication utilizes the existing power line infrastructure to achieve the integration of power transmission and information transmission, and is an economical and efficient communication method. However, the power line environment is complex and changeable, and there are narrowband interference and pulse interference from various power equipment, which will seriously affect the signal transmission quality. In order to improve communication reliability, it is necessary to dynamically adjust parameters such as carrier frequency, transmission power and modulation method.

[0004] At the same time, micro-power wireless communication, with its flexible deployment and wide coverage, also plays an important role in applications such as the Internet of Things and smart cities. Micro-power wireless communication systems operate in unlicensed frequency bands such as 2.4 GHz and 5.8 GHz, and adopt technical standards such as WPAN and WLAN. Due to limited frequency resources, frequency reuse is the key to improving spectrum utilization. However, frequency reuse will cause serious intermodulation interference, and it is necessary to dynamically adjust the transmission power and modulation mode according to the real-time channel quality to meet the needs of reliable communication.

[0005] Therefore, how to achieve the coordinated work of high-speed power line carrier communication and micro-power wireless communication, make full use of the two communication resources, dynamically adjust communication parameters according to environmental changes, and optimize communication quality is a key technical problem that needs to be solved urgently. Summary of the invention

[0006] In view of this, the present invention provides a HPLC and micro-power wireless dual-mode communication frequency adaptation method, which can solve the technical problem that it is difficult to achieve coordinated work between high-speed power line carrier communication and micro-power wireless communication in the prior art.

[0007] The present invention is achieved in that:

[0008] The present invention provides a method for frequency adaptation of HPLC and micro-power wireless dual-mode communication, comprising the following steps:

[0009] S10, setting initial working parameters of high-speed power line carrier communication and micro-power wireless communication, and establishing a dual-mode communication collaborative control mechanism, wherein the initial working parameters include carrier frequency parameters, transmission power parameters, and modulation mode parameters;

[0010] S20, collecting electromagnetic interference signals in the power line channel, and establishing a linear superposition noise model containing a time domain weight coefficient according to the electromagnetic interference signals, wherein the linear superposition noise model includes a narrowband interference term, a pulse interference term, and a background noise term;

[0011] S30, calculating the spectrum distribution function of the linear superposition noise model based on fast Fourier transform, establishing a linear prediction matrix according to the spectrum distribution function, and solving the linear prediction matrix by least square method to obtain a noise spectrum prediction result;

[0012] S40, collecting a zero-crossing time series of a power line signal, calculating a mean, a standard deviation, and a correlation coefficient of the zero-crossing time series, and constructing a zero-crossing feature vector according to the mean, the standard deviation, and the correlation coefficient;

[0013] S50, establishing a convex optimization objective function according to the noise spectrum prediction result and the zero-crossing feature vector, and using a gradient descent method to solve the convex optimization objective function to obtain an optimal carrier frequency for high-speed power line carrier communication;

[0014] S60, substituting the optimal carrier frequency into a preset carrier adjustment equation group, solving the carrier adjustment equation group to obtain a transmission power parameter and a modulation mode parameter of high-speed power line carrier communication;

[0015] S70, according to the signal reception strength index, channel busyness index, and bit error rate index of the preset micro-power wireless communication channel, the background electromagnetic noise in the micro-power wireless communication environment is collected to construct a channel quality assessment matrix; and a wireless channel interference prediction model is established based on linear regression, and the recursive least squares method is used to update the parameters of the wireless channel interference prediction model to obtain a frequency interference prediction result;

[0016] S80, establishing a quadratic programming model according to the signal reception strength index, the channel busyness index, the bit error rate index and the frequency interference prediction result, and solving the quadratic programming model to obtain the transmission power parameters and modulation mode parameters of the micro-power wireless communication;

[0017] S90, establishing a switching decision function between high-speed power line carrier communication and micro-power wireless communication according to a preset communication quality threshold, implementing dual-mode communication switching based on the switching decision function, and monitoring communication quality indicators.

[0018] On the basis of the above technical solution, the frequency adaptation method of HPLC and micro-power wireless dual-mode communication of the present invention can also be improved as follows:

[0019] Furthermore, the initial working parameter setting in S10 is specifically expressed as follows:

[0020] fc =f0+Δf;

[0021] P t =P0+AP;

[0022] M={BPSK, QPSK, 16QAM, 64QAM};

[0023] In the formula, f c is the carrier frequency, the initial value f0 = 3MHz; Δf is the frequency offset, the range is [-0.5MHz, 0.5MHz]; P t is the transmission power, the initial value P0=100mW; ΔP is the power adjustment amount, the range is [-20mW, 20mW]; M is the modulation mode set.

[0024] The linear superposition noise model in S20 is specifically expressed as follows:

[0025]

[0026] Where n(t) is the total noise; N1 is the number of narrowband interference sources; A i ,f i ,φ i are the amplitude, frequency and phase of the i-th narrowband interference respectively; N2 is the number of pulse interference sources; B j ,α j ,t j ,f j are the amplitude, attenuation coefficient, occurrence time and frequency of the jth pulse interference respectively; σ b is the standard deviation of background noise; ω(t) is Gaussian white noise.

[0027] The spectrum distribution function and linear prediction matrix in S30 are specifically expressed as follows:

[0028]

[0029] Where S(f) is the noise spectrum; T is the sampling period; N is the number of sampling points; Δt is the sampling interval; is the spectrum prediction value; p is the prediction order; a i is the prediction coefficient; e(k) is the prediction error.

[0030] The zero-crossing feature vector in S40 is specifically expressed as follows:

[0031]

[0032] in,

[0033] In the formula, μ z is the mean time of zero crossing; σz is the standard deviation; z (k) is the k-order autocorrelation coefficient; t i is the i-th zero-crossing moment; N z is the total number of zero crossings; is the feature vector.

[0034] The convex optimization objective function in S50 is specifically expressed as follows:

[0035]

[0036] stf min ≤f≤f max ;

[0037] In the formula, f opt is the optimal carrier frequency; λ1, λ2 are weight coefficients; f min , f max are the lower and upper frequency limits respectively.

[0038] The carrier adjustment equation group in S60 is specifically expressed as follows:

[0039] P opt =P0·exp(-β1|f opt -f0|);

[0040] M opt = argmax m∈M {C m ·(1-β2|f opt -f0|)};

[0041] Where P opt is the optimal transmission power; M opt is the optimal modulation mode; β1, β2 are adjustment coefficients; C m is the channel capacity corresponding to modulation mode m.

[0042] The channel quality evaluation matrix and interference prediction model in S70 are specifically expressed as follows:

[0043]

[0044] Where Q is the channel quality matrix; RSSI is the signal receiving strength; CB is the channel busyness; BER is the bit error rate; ω1, ω2, ω3 are weights; I(t) is the interference intensity at time t; q is the prediction order; b i is the prediction coefficient; η(t) is the prediction error.

[0045] The quadratic programming model in S80 is specifically expressed as follows:

[0046] minJ=x T QxT x;

[0047] stAx≤b;

[0048] x=[P w , M w ] T ;

[0049] Where, J is the objective function; x is the decision variable vector; Q is the quadratic coefficient matrix; c is the linear coefficient vector; A is the constraint matrix; b is the constraint vector; P w , M w are the wireless communication power and modulation mode respectively.

[0050] The switching decision function in S90 is specifically expressed as follows:

[0051] D(t)=γ1Q p (t)+γ2Q w (t);

[0052]

[0053] Where D(t) is the decision function; Q p (t), Q w (t) are the power line and wireless communication quality indicators respectively; γ1, γ2 are weight coefficients; θ is the switching threshold; Mode is the communication mode.

[0054] Furthermore, the switching threshold is set based on experience or adopts a default value of 0.69.

[0055] Compared with the prior art, the method for frequency adaptation of HPLC and micro-power wireless dual-mode communication provided by the present invention has the following beneficial effects:

[0056] The method first establishes the initial working parameters of high-speed power line carrier communication and micro-power wireless communication, including carrier frequency, transmission power and modulation mode. Then, the interference signal in the power line channel is collected, a linear superposition noise model with time domain weight coefficients is established, and its spectrum distribution is calculated by fast Fourier transform. Based on this, the linear prediction method is used to estimate the noise spectrum at future times. At the same time, the zero-crossing time series of the power line signal is collected and the feature vector is extracted. Next, the noise spectrum prediction result and the zero-crossing feature vector are combined to construct a convex optimization objective function to solve the optimal carrier frequency.

[0057] For micro-power wireless communication, this method collects indicators such as signal reception strength, channel busyness and bit error rate to establish a channel quality assessment matrix. At the same time, it collects environmental noise and uses linear regression to establish an interference prediction model. Based on this information, the quadratic programming optimization method is used to obtain the optimal wireless communication power and modulation method.

[0058] Finally, according to the preset communication quality threshold, the switching decision function of high-speed power line carrier communication and micro-power wireless communication is established to realize the automatic switching of the two communication modes. This can not only effectively reduce interference and improve communication quality, but also can flexibly adapt to different environmental conditions and has strong robustness.

[0059] In summary, the present invention solves the technical problem in the prior art that it is difficult to achieve coordinated work between high-speed power line carrier communication and micro-power wireless communication. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 A flow chart of the method provided by the present invention;

[0061] Figure 2 is the power spectrum density distribution diagram of the power line channel noise;

[0062] Figure 3 Performance diagram of the system disturbance prediction model;

[0063] Figure 4 It is a curve diagram for evaluating the performance of the communication system;

[0064] Figure 5 Figure 2 is the channel capacity diagram for different modulation methods. DETAILED DESCRIPTION

[0065] In order to make the purpose, technical solution and advantages of the embodiments of the present invention more clear, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0066] like Figure 1 As shown, it is a flow chart of a method for frequency adaptation of HPLC and micro-power wireless dual-mode communication provided by the present invention, and the method comprises the following steps:

[0067] S10, setting initial working parameters of high-speed power line carrier communication and micro-power wireless communication, and establishing a dual-mode communication collaborative control mechanism, the initial working parameters including carrier frequency parameters, transmission power parameters, and modulation mode parameters;

[0068] S20, collecting electromagnetic interference signals in the power line channel, and establishing a linear superposition noise model containing a time domain weight coefficient according to the electromagnetic interference signals, wherein the linear superposition noise model includes a narrowband interference term, a pulse interference term, and a background noise term;

[0069] S30, calculating the spectrum distribution function of the linear superposition noise model based on fast Fourier transform, establishing a linear prediction matrix according to the spectrum distribution function, and solving the linear prediction matrix by least square method to obtain a noise spectrum prediction result;

[0070] S40, collecting the zero-crossing time series of the power line signal, calculating the mean, standard deviation, and correlation coefficient of the zero-crossing time series, and constructing a zero-crossing feature vector according to the mean, standard deviation, and correlation coefficient;

[0071] S50, establishing a convex optimization objective function according to the noise spectrum prediction result and the zero-crossing feature vector, and using the gradient descent method to solve the convex optimization objective function to obtain the optimal carrier frequency of the high-speed power line carrier communication;

[0072] S60, substituting the optimal carrier frequency into a preset carrier adjustment equation group, solving the carrier adjustment equation group to obtain a transmission power parameter and a modulation mode parameter of the high-speed power line carrier communication;

[0073] S70, according to the preset signal reception strength index, channel busyness index, and bit error rate index of the micro-power wireless communication channel, the background electromagnetic noise in the micro-power wireless communication environment is collected to construct a channel quality assessment matrix; and a wireless channel interference prediction model is established based on linear regression, and the recursive least squares method is used to update the wireless channel interference prediction model parameters to obtain a frequency interference prediction result;

[0074] S80, establishing a quadratic programming model according to the signal reception strength index, the channel busyness index, the bit error rate index and the frequency interference prediction result, and solving the quadratic programming model to obtain the transmission power parameters and modulation mode parameters of the micro-power wireless communication;

[0075] S90, establishing a switching decision function between high-speed power line carrier communication and micro-power wireless communication according to a preset communication quality threshold, implementing dual-mode communication switching based on the switching decision function, and monitoring communication quality indicators.

[0076] The specific implementation methods of the above steps are described in detail below:

[0077] The specific implementation of step S10 is as follows: First, the initial operating parameters of high-speed power line carrier communication and micro-power wireless communication need to be set. This includes three aspects: carrier frequency parameter f c , Transmit power parameter P t And modulation mode parameter M.

[0078] Where, the carrier frequency f c The initial value f0 is set to 3MHz, and the allowed frequency offset Δf range is [-0.5MHz, 0.5MHz]. c The value range of is [2.5MHz, 3.5MHz], which can be expressed as:

[0079] f c =f0+Δf;

[0080] Transmit power Pt The initial value P0 is set to 100mW, and the allowed power adjustment AP range is [-20mW, 20mW]. t The value range is [80mW, 120mW], which can be expressed as:

[0081] P t =P0+AP;

[0082] The modulation mode parameter M can select common digital modulation modes, including BPSK, QPSK, 16QAM and 64QAM.

[0083] In addition, it is necessary to establish a coordinated control mechanism for high-speed power line carrier communication and micro-power wireless communication. This realizes the organic combination of the two communication modes, ensures that the working mode can be automatically switched according to the communication quality under different environmental conditions, and improves the reliability and stability of the overall communication system.

[0084] The specific implementation of step S20 is as follows: first, it is necessary to collect electromagnetic interference signals in the power line channel, including narrowband interference and pulse interference from various power equipment. According to the collected interference signals, a linear superposition noise model n(t) is established. This model can be expressed as:

[0085]

[0086] Where N1 represents the number of narrowband interference sources, A i 、f i and φ i They represent the amplitude, frequency and phase of the i-th narrowband interference respectively. N2 represents the number of pulse interference sources, B j , α j ,t j and f j Respectively represent the amplitude, attenuation coefficient, occurrence time and frequency of the jth pulse interference. b represents the standard deviation of the background noise, and ω(t) represents Gaussian white noise. This linear superposition noise model can better describe the actual interference situation in the power line channel.

[0087] The specific implementation of step S30 is: firstly, the collected power line noise signal is subjected to fast Fourier transform (FFT) calculation to obtain the noise power spectrum density function S(f), which is expressed as:

[0088]

[0089] Among them, T is the sampling period, Δt is the sampling interval, and N is the number of sampling points. Then, the noise spectrum prediction matrix is ​​established based on the linear prediction model. Expressed as:

[0090]

[0091] Here, p is the prediction order, a i is the prediction coefficient, and e(k) is the prediction error. The prediction coefficient a is obtained by the least squares method. i , we can get the noise power spectrum density prediction result at the future time This provides a basis for subsequent optimization of the carrier frequency.

[0092] The specific implementation of step S40 is as follows: first, the zero-crossing time series t of the power line signal needs to be collected. i Then calculate the statistical characteristics of the zero-crossing time series, including the mean μ z , standard deviation σ z and the autocorrelation coefficient ρ z (k), the specific expression is as follows:

[0093]

[0094] Among them, N z is the total number of zero-crossing points. These statistical features constitute a 5-dimensional feature vector Right now:

[0095]

[0096] This eigenvector reflects the periodicity and randomness of the power line signal, providing another basis for subsequent optimization of the carrier frequency.

[0097] The specific implementation of step S50 is as follows: first, the noise power spectrum density prediction result and the zero-crossing eigenvector Combined, construct a convex optimization objective function:

[0098]

[0099] stf min ≤f≤f max ;

[0100] Among them, f opt is the optimal carrier frequency, λ1 and λ2 are weight coefficients, f min and f max The optimization objective function takes into account the noise spectrum and signal characteristics. The goal is to find an optimal carrier frequency that minimizes the sum of the noise power spectrum density and the zero-crossing feature vector norm. By using the gradient descent method to solve this convex optimization problem, we can get the optimal carrier frequency f. opt .

[0101] The specific implementation of step S60 is as follows: first, according to the optimal carrier frequency f obtained in step S50 opt , combined with the preset carrier adjustment equations, the optimal transmission power P is calculated opt and modulation mode M opt The specific expression is as follows:

[0102] P opt =P0·exp(-β1|f opt -f0|);

[0103] M opt = argmax m∈M {C m ·(1-β2|f opt -f0|)};

[0104] Among them, β1 and β2 are adjustment coefficients, C m is the channel capacity corresponding to modulation mode m. This shows that as the carrier frequency f opt The deviation from the initial frequency f0 increases, and the transmission power P opt will gradually decrease; at the same time, the modulation mode M opt The larger the capacity and the smaller the f opt In this way, the working parameters of high-speed power line carrier communication can be dynamically adjusted to improve the communication quality.

[0105] The specific implementation of step S70 is as follows: First, it is necessary to collect indicators such as signal receiving strength (RSSI), channel busyness (CB) and bit error rate (BER) for the micro-power wireless communication channel, and construct a channel quality evaluation matrix Q, which is expressed as:

[0106]

[0107] Among them, ω1, ω2 and ω3 are the weight factors of the corresponding indicators.

[0108] Then, the background electromagnetic noise in the micro-power wireless communication environment is collected, and an interference prediction model is established based on linear regression:

[0109]

[0110] Among them, I(t) is the interference intensity at time t, q is the prediction order, and b i is the prediction coefficient, η(t) is the prediction error. The recursive least squares method is used to continuously update the model parameter b i , we can get the frequency interference prediction results at future moments.

[0111] This information provides a basis for subsequent optimization of wireless communication parameters.

[0112] The specific implementation of step S80 is as follows: First, according to the channel quality evaluation matrix Q and the frequency interference prediction result I(t) obtained in step S70, a quadratic programming optimization model is established:

[0113] minJ=x T Qx T x;

[0114] stAx≤b;

[0115] x=[P w , M w ] T ;

[0116] Among them, J is the objective function, x is the decision variable vector (including wireless communication power P w and modulation mode M w ), Q is the quadratic coefficient matrix, c is the linear coefficient vector, A and b are the constraint matrix and constraint vector respectively.

[0117] By solving this quadratic programming model, the optimal wireless communication power P can be obtained. w and modulation mode M w , meeting the requirements of indicators such as signal reception strength, channel busyness and bit error rate, while also taking into account the impact of frequency interference.

[0118] The specific implementation of step S90 is as follows: first, according to a preset communication quality threshold, a switching decision function D(t) between high-speed power line carrier communication and micro-power wireless communication is established, which is expressed as:

[0119] D(t)=γ1Q p (t)+γ2Q w (t);

[0120]

[0121] Among them, Q p (t) and Q w (t) are the quality indicators of power line and wireless communication at time t, γ1 and γ2 are the corresponding weight coefficients, and θ is the switching threshold.

[0122] Then, the communication quality indicators are monitored in real time, and the working modes of high-speed power line carrier communication and micro-power wireless communication are automatically switched according to the result of the decision function D(t). This can dynamically adapt to different communication environments and ensure the stability and reliability of the overall communication quality.

[0123] Through the specific implementation of the above steps, a frequency adaptive method for high-speed power line carrier communication and micro-power wireless communication can be realized. This method dynamically optimizes the working parameters of the two communication modes, including carrier frequency, transmission power, and modulation mode, by establishing a collaborative control mechanism and combining the power line channel noise characteristics and wireless channel quality indicators. This can not only improve the communication quality, but also flexibly switch the communication mode under different environmental conditions, with strong adaptability and robustness.

[0124] Specifically, the principle of the present invention is:

[0125] First, there are narrowband interference and pulse interference from various power equipment in the power line channel, which will seriously affect the signal transmission quality. By establishing a linear superposition noise model, these interference characteristics can be described more accurately. Combined with the statistical characteristics of the zero-crossing time series, the periodicity and randomness of the power line signal can be reflected. Therefore, by combining the noise spectrum prediction results and the zero-crossing feature vector and constructing a convex optimization objective function, an optimal carrier frequency can be found to achieve the best communication quality. This provides a basis for frequency adaptation of high-speed power line carrier communication.

[0126] Secondly, micro-power wireless communication faces frequency resource limitations and interference problems. By real-time monitoring of indicators such as signal reception strength, channel busyness, and bit error rate, the quality of the wireless channel can be evaluated. At the same time, environmental noise is collected and an interference prediction model is established using linear regression to predict the interference level at future times. Based on this, the quadratic programming optimization method can be used to obtain the optimal wireless communication power and modulation method to meet the needs of reliable communication.

[0127] Finally, the present invention establishes a coordinated control mechanism for high-speed power line carrier communication and micro-power wireless communication. By switching the decision function, the two communication modes are automatically switched. When the power line communication quality is good, power line communication is preferred; when the power line communication quality decreases, it automatically switches to micro-power wireless communication. This not only improves the overall communication quality, but also has strong environmental adaptability.

[0128] In summary, the solution of the present invention makes full use of both power line and wireless communication resources, adopts advanced signal processing and optimization control technology, realizes frequency adaptation and mode switching, and solves the problem of communication quality in complex environments.

[0129] The following is an example of a specific application scenario of the present invention: a certain smart distribution network system adopts the frequency adaptation method of high-speed power line carrier communication and micro-power wireless communication proposed in the present invention, giving full play to the advantages of the two communication technologies, and realizing environmental adaptation and communication quality optimization.

[0130] The application scenarios of the smart distribution network system are as follows: the length of the distribution line from the main transformer to each user is about 20 kilometers, the power line environment is complex and changeable, and there is strong interference from equipment such as variable frequency motors and electric welders. At the same time, the system deploys a large number of smart meters, distribution terminals and other equipment, and has high requirements for real-time communication. In order to meet this application requirement, the communication solution proposed in this invention is adopted.

[0131] In the initial system settings, the initial operating parameters of high-speed power line carrier communication and micro-power wireless communication are configured first. The initial carrier frequency f0 of power line carrier communication is set to 3MHz, and the allowed frequency offset Δf range is ±0.5MHz. Therefore, the carrier frequency f c The value range is [2.5MHz, 3.5MHz]. Transmit power P t The initial value P0 is set to 100mW, and the allowable power adjustment range ΔP is ±20mW, so P t The value range is [80mW, 120mW]. The modulation parameter M selects four common digital modulation methods: BPSK, QPSK, 16QAM and 64QAM.

[0132] At the same time, a coordinated control mechanism for high-speed power line carrier communication and micro-power wireless communication is established. Specifically, when the power line communication quality is good, the power line communication mode is used first; when the power line communication quality decreases, it automatically switches to the micro-power wireless communication mode. The judgment criteria for communication quality include signal reception strength, bit error rate and other indicators, and mode switching is achieved by setting corresponding thresholds.

[0133] Next, the system starts to collect noise interference signals in the power line channel and establishes a linear superposition noise model. After multiple field tests, a total of 10 narrowband interference sources and 5 pulse interference sources were detected, and their parameters are shown in Table 1:

[0134] Table 1 Power line channel interference parameters

[0135] Interference Type quantity Amplitude frequency Phase / attenuation coefficient Narrowband Interference 10 <![CDATA[A i ]]> <![CDATA[f i ]]> <![CDATA[φ i ]]> Pulse interference 5 <![CDATA[B j ]]> <![CDATA[f j ]]> <![CDATA[α j ]]>

[0136] According to these parameters, the following linear superposition noise model is established:

[0137]

[0138] Among them, the background noise standard deviation σ b Measured to be 0.05.

[0139] Then, perform FFT calculation on the collected power line noise signal to obtain the power spectrum density function S(f). Taking a certain test as an example, the spectrum distribution of S(f) is shown in Table 2:

[0140] Table 2 Spectrum distribution table

[0141] Frequency (MHz) 3.0 3.1 3.2 3.3 3.4 s(f) 0.2 0.4 0.6 0.8 0.5

[0142] Figure 2 The power spectrum density distribution of power line channel noise is shown, with the horizontal axis being frequency (MHz) and the vertical axis being power spectrum density (mW / Hz). It can be seen from the figure that there is a large amount of noise interference around 3.3MHz.

[0143] Based on the linear prediction model, a noise spectrum prediction matrix was established, and the prediction order p was 3. The prediction coefficients a1 = 0.6, a2 = 0.3, and a3 = 0.1 were obtained by least squares method. Substituting into the prediction model, the noise power spectrum density prediction result at the future moment can be obtained.

[0144] At the same time, the system also collects the zero-crossing time series of the power line signal. After statistical analysis, the mean value μ z =0.2ms, standard deviation σ z =0.05ms, the first, second and third order autocorrelation coefficients are ρ z (1)=0.8,ρ z (2)=0.6,ρ z (3) = 0.4. These feature quantities are combined into a feature vector

[0145] Next, the noise spectrum prediction results and the zero-crossing eigenvector Introduce convex optimization objective function:

[0146]

[0147] st2.5MHz≤f≤3.5MHz;

[0148] By solving the gradient descent method, the optimal carrier frequency f is obtained. opt =3.2MHz.

[0149] According to f opt , combined with the preset carrier adjustment equations, calculate the optimal transmission power P opt =90mW and modulation mode M opt =16QAM, as follows:

[0150] P opt =100·exp(-0.2|3.2-3|)=90mW;

[0151] M opt = argmax m∈M {C m(1-0.1|3.2-3|)}=16QAM;

[0152] Among them, the channel capacity C corresponding to the modulation mode m m They are: BPSK 2bit / Hz, QPSK 4bit / Hz, 16QAM 8bit / Hz, 64QAM 12bit / Hz. Since 16QAM has the largest capacity near 3.2MHz and the deviation from the initial frequency 3MHz is also small, 16QAM is selected as the optimal modulation method.

[0153] For micro-power wireless communication, the system first collects indicators such as RSSI, CB and BER to construct the channel quality assessment matrix Q. The weight factors are as follows:

[0154]

[0155] At the same time, the background noise in the wireless communication environment is collected to establish an interference prediction model:

[0156] I(t+1)=0.6I(t)+0.3I(t-1)+0.1I(t-2)+η(t);

[0157] By continuously updating the model parameters through the recursive least squares method, the frequency interference prediction results at future moments can be obtained.

[0158] Figure 3 The performance of the system disturbance prediction model is analyzed. The upper part shows the comparison between the actual disturbance and the predicted disturbance, and the lower part shows the change of the prediction error.

[0159] Based on the Q matrix and the interference prediction result I(t), the system establishes the following quadratic programming optimization model:

[0160]

[0161] sP w ≤120mW,M w ∈{BPSK, QPSK, 16QAM, 64QAM};

[0162] Solve the optimization problem and get the optimal power P of wireless communication w =100mW and modulation mode M w =QPSK.

[0163] Finally, the system established the switching decision function between high-speed power line carrier communication and micro-power wireless communication:

[0164] D(t)=0.6Q p (t)+0.4Q w (t);

[0165]

[0166] Among them, Q p (t) and Q w (t) are the quality indicators of power line and wireless communication at time t, respectively. The automatic switching between the two communication modes is achieved by setting the threshold value to 0.8.

[0167] Figure 4 The communication system performance evaluation curve is shown, including the change of power line communication quality, wireless communication quality and decision function over time. The horizontal dotted line represents the threshold (0.8) for communication mode switching.

[0168] During actual operation, the system will monitor the communication quality indicators in real time, and dynamically adjust the working parameters and switch the communication mode according to the result of the decision function D(t). For example, when the power line communication quality decreases (such as D(t)<0.8), the system will automatically switch to the micro-power wireless communication mode, and set the transmission power to 100mW and the modulation mode to QPSK. When the power line communication quality is restored (such as D(t)≥0.8), the system will switch back to the power line communication mode and set the carrier frequency to 3.2MHz, the transmission power to 90mW, and the modulation mode to 16QAM.

[0169] Figure 5 The channel capacities of different modulation modes (BPSK, QPSK, 16QAM, 64QAM) at different frequency offsets are compared, and the performance characteristics of various modulation modes are demonstrated.

[0170] Through the above adaptive adjustment and mode switching, the intelligent distribution network system can always provide reliable real-time communication in a complex and changeable power line environment. Compared with the traditional fixed parameter solution, the frequency adaptive method provided by the present invention can not only effectively reduce interference and improve communication quality, but also has strong environmental adaptability and can perform well in various complex scenarios.

[0171] The above description is only a specific implementation mode of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A frequency adaptation method for HPLC and micro-power wireless dual-mode communication, characterized in that: The following steps are involved: S10, setting initial working parameters of high-speed power line carrier communication and micro-power wireless communication, and establishing a dual-mode communication collaborative control mechanism, wherein the initial working parameters include carrier frequency parameters, transmission power parameters, and modulation mode parameters; S20, collecting electromagnetic interference signals in the power line channel, and establishing a linear superposition noise model containing a time domain weight coefficient according to the electromagnetic interference signals, wherein the linear superposition noise model includes a narrowband interference term, a pulse interference term, and a background noise term; S30, calculating the spectrum distribution function of the linear superposition noise model based on fast Fourier transform, establishing a linear prediction matrix according to the spectrum distribution function, and solving the linear prediction matrix by least square method to obtain a noise spectrum prediction result; S40, collecting a zero-crossing time series of a power line signal, calculating a mean, a standard deviation, and a correlation coefficient of the zero-crossing time series, and constructing a zero-crossing feature vector according to the mean, the standard deviation, and the correlation coefficient; S50, establishing a convex optimization objective function according to the noise spectrum prediction result and the zero-crossing feature vector, and using a gradient descent method to solve the convex optimization objective function to obtain an optimal carrier frequency for high-speed power line carrier communication; S60, substituting the optimal carrier frequency into a preset carrier adjustment equation group, solving the carrier adjustment equation group to obtain a transmission power parameter and a modulation mode parameter of high-speed power line carrier communication; S70, according to the signal reception strength index, channel busyness index, and bit error rate index of the preset micro-power wireless communication channel, the background electromagnetic noise in the micro-power wireless communication environment is collected to construct a channel quality assessment matrix; and a wireless channel interference prediction model is established based on linear regression, and the recursive least squares method is used to update the parameters of the wireless channel interference prediction model to obtain a frequency interference prediction result; S80, establishing a quadratic programming model according to the signal reception strength index, the channel busyness index, the bit error rate index and the frequency interference prediction result, and solving the quadratic programming model to obtain the transmission power parameters and modulation mode parameters of the micro-power wireless communication; S90, establishing a switching decision function between high-speed power line carrier communication and micro-power wireless communication according to a preset communication quality threshold, implementing dual-mode communication switching based on the switching decision function, and monitoring communication quality indicators.

2. A frequency adaptation method for HPLC and micro-power wireless dual-mode communication according to claim 1, characterized in that: The linear superposition noise model is specifically expressed as follows: Where n(t) is the total noise; N1 is the number of narrowband interference sources; A i , f i ,φ i are the amplitude, frequency and phase of the i-th narrowband interference respectively; N2 is the number of pulse interference sources; B j , α j , t j , f j are the amplitude, attenuation coefficient, occurrence time and frequency of the jth pulse interference respectively; σ b is the standard deviation of background noise; ω(t) is Gaussian white noise.

3. A frequency adaptation method for HPLC and micro-power wireless dual-mode communication according to claim 2, characterized in that: The spectrum distribution function is specifically expressed as follows: The linear prediction matrix is ​​specifically expressed as follows: Where S(f) is the noise spectrum; T is the sampling period; N is the number of sampling points; Δt is the sampling interval; is the spectrum prediction value; p is the prediction order; a i is the prediction coefficient; e(k) is the prediction error.

4. A frequency adaptation method for HPLC and micro-power wireless dual-mode communication according to claim 3, characterized in that: The zero-crossing feature vector is specifically expressed as follows: in, In the formula, μ z is the mean time of zero crossing; σ z is the standard deviation; z (k) is the k-order autocorrelation coefficient; t i is the i-th zero-crossing moment; N z is the total number of zero crossings; is the feature vector.

5. A frequency adaptation method for HPLC and micro-power wireless dual-mode communication according to claim 4, characterized in that: The convex optimization objective function is specifically expressed as follows: s.t. f min ≤f≤f max ; In the formula, f opt is the optimal carrier frequency; λ1, λ2 are weight coefficients; f min , f max are the lower and upper frequency limits respectively.

6. A frequency adaptation method for HPLC and micro-power wireless dual-mode communication according to claim 5, characterized in that: The carrier adjustment equation group is specifically expressed as follows: P opt =P0·exp(-β1|f opt -f0|); M opt =argmax m∈M {C m ·(1-β2|f opt -f0|)}; Where P opt is the optimal transmission power; M opt is the optimal modulation mode; β1, β2 are adjustment coefficients; C m is the channel capacity corresponding to modulation mode m.

7. A frequency adaptation method for HPLC and micro-power wireless dual-mode communication according to claim 6, characterized in that: The channel quality assessment matrix is ​​specifically expressed as follows: The interference prediction model is specifically expressed as follows: Where Q is the channel quality matrix; RSSI is the signal receiving strength; CB is the channel busyness; BER is the bit error rate; ω1, ω2, ω3 are weights; I(t) is the interference intensity at time t; q is the prediction order; b i is the prediction coefficient; η(t) is the prediction error.

8. A frequency adaptation method for HPLC and micro-power wireless dual-mode communication according to claim 7, characterized in that: The quadratic programming model is specifically expressed as follows: minJ=x T Qx+c T x; st Ax≤b; x=[P w ,M w ] T ; Where, J is the objective function; x is the decision variable vector; Q is the quadratic coefficient matrix; c is the linear coefficient vector; A is the constraint matrix; b is the constraint vector; P w , M w are the wireless communication power and modulation mode respectively.

9. A frequency adaptation method for HPLC and micro-power wireless dual-mode communication according to claim 8, characterized in that: The switching decision function is specifically expressed as follows: D(t)=γ1Q p (t)+γ2Q w (t); Where D(t) is the decision function; Q p (t), Q w (t) are the power line and wireless communication quality indicators respectively; γ1, γ2 are weight coefficients; θ is the switching threshold; Mode is the communication mode.

10. A frequency adaptation method for HPLC and micro-power wireless dual-mode communication according to claim 9, characterized in that: The switching threshold is set based on experience or adopts a default value of 0.69.

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