Communication method for remotely controlling distribution box

Abstract

The invention provides a communication method for remotely controlling a distribution box, belongs to the technical field of remote distribution boxes, and realizes active identification and avoidance of an interference source by establishing a multi-channel transmission link based on orthogonal frequency division multiplexing and combining electromagnetic interference identification and frequency band dynamic adjustment technologies. In the method, adaptive sampling rate control and hierarchical data compression algorithms are adopted to optimize data acquisition and transmission, and a signal intensifier and an adaptive power control technology are utilized to ensure the stability of a communication link. The core innovation lies in that an electromagnetic field propagation physical model based on a Maxwell equation is applied to analyze signal attenuation characteristics, and signal transmission optimization is guided by predicting electromagnetic field intensity distribution. A digital signal processing algorithm is adopted at a receiving end to eliminate data distortion caused by interference, and the technical problem that communication reliability is insufficient in the complex electromagnetic environment of the distribution box is solved.

Description

Technical Field

[0001] The present invention belongs to the technical field of remote distribution boxes, and in particular relates to a communication method for remotely controlling a distribution box. Background Art

[0002] With the rapid development of smart grids and the Industrial Internet of Things (IIoT), remote monitoring and control of distribution boxes has become a crucial component of power system management. Traditional remote control technologies for distribution boxes primarily rely on single communication methods, such as narrowband wireless networks like GPRS and CDMA, or wired networks. These communication methods provide basic remote data acquisition and control functions and can meet general application requirements in a good communication environment.

[0003] However, distribution boxes are often located in complex electromagnetic environments, such as factory workshops and substations. These areas are home to a large number of electrical equipment, generating severe electromagnetic interference. Traditional single communication methods have limited anti-interference capabilities and are prone to data transmission interruptions and communication quality degradation in environments with strong electromagnetic interference. In addition, existing technologies often use fixed transmission parameters and sampling rates, which cannot be dynamically adjusted according to the communication environment, making it difficult to maintain stable communication performance when the interference intensity changes. In other words, the existing technology has the technical problem of insufficient communication reliability in the complex electromagnetic environment of the distribution box. Summary of the Invention

[0004] In view of this, the present invention provides a communication method for remotely controlling a distribution box, which can solve the technical problem in the prior art of insufficient communication reliability in a complex electromagnetic environment of a distribution box.

[0005] The present invention is implemented as follows: The present invention provides a communication method for remotely controlling a distribution box, including: establishing a multi-channel transmission link between a mobile terminal and a distribution box; implementing an electromagnetic interference identification algorithm to identify the frequency characteristics of an interference source; dynamically adjusting the signal transmission frequency band according to the frequency characteristics of the interference source; adjusting the data acquisition frequency using an adaptive sampling rate control module; compressing the collected data using a hierarchical data compression algorithm; improving the mobile network signal strength through a signal enhancer; applying an electromagnetic field propagation physical model to analyze signal attenuation characteristics; applying a digital signal processing algorithm at the receiving end to perform signal recovery; and dynamically adjusting communication parameters using a two-layer game optimization model to achieve overall performance improvement of the communication system.

[0006] The multi-channel transmission link is specifically a communication structure that uses multiple frequency bands or channels for data transmission in a single communication connection and improves communication reliability and anti-interference capability through frequency diversity technology.

[0007] The multi-channel transmission link adopts a signal transmission mechanism based on orthogonal frequency division multiplexing modulation.

[0008] The electromagnetic interference identification algorithm specifically uses fast Fourier transform technology to perform frequency domain analysis on the received signal, extracts the interference signal pattern from the spectrum characteristics, and determines the type and intensity of the interference source through pattern matching method.

[0009] The adaptive sampling rate control module is a module that dynamically adjusts the sampling frequency according to the data change rate. When the data changes rapidly, the sampling rate is increased, and when the data is stable, the sampling rate is reduced to achieve optimal allocation of sampling resources.

[0010] The hierarchical data compression algorithm is a data processing algorithm that divides data into layers according to their importance and uses different compression ratios for data at different layers to ensure high-precision retention of critical data and high compression ratio of non-critical data.

[0011] The adaptive power control technology specifically adjusts the signal transmission power in real time according to the quality of the communication link, thereby minimizing energy consumption and reducing interference to other wireless devices while ensuring communication quality.

[0012] Among them, the electromagnetic field propagation physical model is specifically an electromagnetic field propagation theoretical model based on Maxwell's equations, which is used to analyze the attenuation characteristics and interference mechanism of electromagnetic waves in the distribution environment; the electromagnetic field propagation physical model is used to predict the propagation characteristics and attenuation laws of electromagnetic waves in the environment around the distribution box. The input includes dielectric conductivity, environmental relative dielectric constant, signal frequency, transmission power, and propagation distance, and the output is the field strength at the receiving point and the signal attenuation coefficient.

[0013] The two-layer game optimization model is a hierarchical optimization structure composed of an upper-layer objective function and a lower-layer objective function, and is a mathematical model for finding the optimal parameter configuration of a communication system under multi-objective constraints.

[0014] Among them, the upper-level objective function of the two-layer game optimization model aims to maximize data transmission throughput while minimizing transmission delay. The input parameters include channel bandwidth, transmission power, modulation and coding scheme, frame length, number of retransmissions and the lower-level optimal solution, and the comprehensive communication efficiency index is calculated through nonlinear combination; the lower-level objective function of the two-layer game optimization model aims to minimize energy consumption while maximizing signal quality. The input parameters include operating frequency, transmission power, antenna gain, modulation order, coding rate and upper-level decision variables, and the energy efficiency signal quality index is calculated through nonlinear combination; the constraints of the two-layer game optimization model include power constraints, spectrum resource constraints, channel capacity constraints and energy constraints. The upper-level objective function and the lower-level objective function are bidirectionally coupled through the upper-level decision variables and the lower-level optimal solution.

[0015] The present invention effectively solves the problem of insufficient communication reliability in complex electromagnetic environments by establishing multi-channel transmission links, implementing electromagnetic interference identification and avoidance, dynamically adjusting data sampling rate and compression processing, applying electromagnetic field propagation physical models and two-layer game optimization.

[0016] This invention uses spectrum analysis to identify interference sources and their characteristics, dynamically adjusting the transmission frequency band to avoid interference, while also reducing data transmission volume through adaptive sampling rate control and hierarchical data compression algorithms. A physical model of electromagnetic field propagation based on Maxwell's equations provides theoretical guidance for signal transmission, while a two-layer game optimization model achieves a balance between transmission efficiency and energy consumption. These technical measures work together to form a complete anti-interference solution, from interference identification and avoidance to signal processing, enabling the system to maintain stable communications even in high-interference environments.

[0017] This invention addresses the technical issue of insufficient communication reliability in complex electromagnetic environments of distribution boxes, significantly improving the stability and anti-interference capabilities of the communication link. Compared to conventional technologies, this invention offers significant improvements in signal quality, communication stability, and resource utilization efficiency, providing more reliable communication support for remote monitoring of power distribution systems and suitable for remote control of distribution boxes in a variety of complex electromagnetic environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 is a flow chart of the method of the present invention.

[0019] Figure 2 This is the overall structural diagram of the remote control distribution box communication system in Example 2.

[0020] Figure 3 This is a partial structural diagram of the communication link of the remote control distribution box in Example 2.

[0021] Figure 4 This is the structural diagram of the two-layer game optimization model in Example 2. DETAILED DESCRIPTION

[0022] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

[0023] like Figure 1 FIG. 1 is a flow chart of a communication method for remotely controlling a distribution box provided by the present invention. The method includes the following steps:

[0024] S01. Establish a multi-channel transmission link between the mobile terminal and the distribution box, using a signal transmission mechanism based on orthogonal frequency division multiplexing modulation;

[0025] S02. Implement electromagnetic interference identification algorithm to identify the main interference sources and their frequency characteristics in the power distribution environment through spectrum analysis;

[0026] S03. Dynamically adjust the signal transmission frequency band according to the frequency characteristics of the interference source to avoid the main interference frequency band and form an interference avoidance plan;

[0027] S04. Using the adaptive sampling rate control module, the data collection frequency is adjusted according to the data change gradient to reduce redundant data collection;

[0028] S05. compress the collected data using a hierarchical data compression algorithm to generate a low-redundancy data packet;

[0029] S06. Use signal boosters to improve mobile network signal strength and combine adaptive power control technology to ensure communication link stability;

[0030] S07. Apply electromagnetic field propagation physical models to analyze signal attenuation characteristics and predict electromagnetic interference field intensity distribution based on Maxwell equations;

[0031] S08. Applying digital signal processing algorithms at the receiving end to perform signal recovery and eliminate data distortion caused by electromagnetic interference;

[0032] S09. A two-layer game optimization model is used to dynamically adjust communication parameters. The upper layer optimizes transmission efficiency, and the lower layer optimizes energy consumption and signal quality to achieve overall performance improvement of the communication system.

[0033] Among them, a multi-channel transmission link is specifically a communication structure that uses multiple frequency bands or channels for data transmission in a single communication connection, and improves communication reliability and anti-interference capabilities through frequency diversity technology.

[0034] Among them, the electromagnetic interference identification algorithm specifically uses fast Fourier transform technology to perform frequency domain analysis on the received signal, extracts the interference signal pattern from the spectrum characteristics, and determines the type and intensity of the interference source through pattern matching method.

[0035] Among them, the adaptive sampling rate control module is specifically a module that dynamically adjusts the sampling frequency according to the data change rate. When the data changes rapidly, the sampling rate is increased, and when the data is stable, the sampling rate is reduced to achieve optimal allocation of sampling resources.

[0036] Among them, the hierarchical data compression algorithm specifically divides the data into layers according to importance, uses different compression ratios for data at different levels, and ensures high-precision retention of critical data and high compression ratio of non-critical data.

[0037] Among them, adaptive power control technology is specifically a technology that adjusts the signal transmission power in real time according to the quality of the communication link, minimizing energy consumption and reducing interference to other wireless devices while ensuring communication quality.

[0038] Among them, the electromagnetic field propagation physical model is specifically an electromagnetic field propagation theoretical model based on Maxwell's equations, which is used to analyze the attenuation characteristics and interference mechanism of electromagnetic waves in the power distribution environment.

[0039] Among them, the electromagnetic field propagation physical model equation is used to predict the propagation characteristics and attenuation law of electromagnetic waves in the environment around the distribution box. The input includes dielectric conductivity, environmental relative dielectric constant, signal frequency, transmission power, and propagation distance. The dielectric conductivity is derived from the measurement of distribution environment material properties, the environmental relative dielectric constant is derived from the environmental medium parameter database, the signal frequency is derived from the communication system operating frequency band setting, the transmission power is derived from the mobile terminal power setting, and the propagation distance is derived from the position measurement of the mobile terminal and the distribution box; the output is the receiving point field strength and signal attenuation coefficient. The receiving point field strength is used to determine the power requirement of the signal enhancer, and the signal attenuation coefficient is used to optimize the signal transmission frequency band selection; the electromagnetic field propagation physical model provides a theoretical basis for signal enhancement and interference suppression by calculating the propagation characteristics of electromagnetic waves in complex environments.

[0040] Among them, the digital signal processing algorithm specifically uses adaptive filtering technology to process the received signal, estimates the interference characteristics in real time and constructs corresponding filters to effectively suppress electromagnetic interference and improve signal quality.

[0041] Among them, the two-layer game optimization model is a hierarchical optimization structure composed of an upper-layer objective function and a lower-layer objective function. It is a mathematical model used to find the optimal parameter configuration of the communication system under multi-objective constraints.

[0042] Among them, the upper-level objective function of the two-layer game optimization model aims to maximize data transmission throughput while minimizing transmission delay. The input parameters include channel bandwidth, transmission power, modulation and coding scheme, frame length, number of retransmissions and the lower-level optimal solution. The channel bandwidth comes from the available spectrum resource allocation, the transmission power comes from the mobile terminal power setting, the modulation and coding scheme comes from the signal modulation module configuration, the frame length comes from the data packaging module setting, the number of retransmissions comes from the communication protocol parameters, and the lower-level optimal solution comes from the optimization result of the lower-layer objective function; the upper-level objective function calculates the comprehensive communication efficiency index by nonlinearly combining the input parameters, and uses the comprehensive communication efficiency index as output. The comprehensive communication efficiency index is used to evaluate the overall communication performance of the system.

[0043] Among them, the lower-level objective function of the two-layer game optimization model aims to minimize energy consumption while maximizing signal quality. The input parameters include operating frequency, transmission power, antenna gain, modulation order, coding rate and upper-level decision variables. The operating frequency comes from the signal transmission band setting, the transmission power comes from the signal enhancer power setting, the antenna gain comes from the communication equipment antenna parameters, the modulation order comes from the modulator configuration, the coding rate comes from the encoder setting, and the upper-level decision variables come from the optimization results of the upper-level objective function; the lower-level objective function calculates the energy-efficiency signal quality index by nonlinearly combining the input parameters, and uses the energy-efficiency signal quality index as output. The energy-efficiency signal quality index is used to evaluate the resource utilization efficiency of the communication system; the upper-level objective function and the lower-level objective function are bidirectionally coupled through the upper-level decision variables and the lower-level optimal solution, wherein the upper-level decision variables affect the lower-level optimization space, and the lower-level optimal solution in turn affects the upper-level objective function value.

[0044] Among them, the constraints of the two-layer game optimization model include power constraints, spectrum resource constraints, channel capacity constraints and energy constraints. The power constraint limits the transmission power to not exceed the maximum power limit of the hardware and not be lower than the signal detection threshold. The spectrum resource constraint limits the channel bandwidth to not exceed the total amount of available spectrum resources. The channel capacity constraint limits the data transmission throughput to not exceed the theoretical channel capacity calculated by Shannon's theorem. The energy constraint limits the energy consumption to not exceed the maximum power supply capacity of the energy supply system.

[0045] The comprehensive communication efficiency index is specifically a dimensionless performance evaluation index calculated by weighted combination of the data transmission throughput and the transmission delay, and is used to quantitatively evaluate the overall transmission efficiency of the communication system.

[0046] The energy efficiency signal quality index is a dimensionless evaluation index calculated by weighted combination of the energy consumption and signal quality, and is used to quantitatively evaluate the resource utilization efficiency of the communication system.

[0047] The specific implementation of the above steps is described in detail below. The specific implementation of step S01 is to establish a multi-channel transmission link between the mobile terminal and the distribution box. First, 3 to 5 available frequency channels are identified by scanning the available frequency band, and the bandwidth of each channel is set to 20MHz. Then, the signal-to-noise ratio of each available channel is measured, and the channel with a signal-to-noise ratio higher than 8dB is selected as the candidate channel. Then, orthogonal frequency division multiplexing modulation technology is applied to each candidate channel, and each channel is divided into 64 subcarriers with a subcarrier spacing of 312.5kHz. Then, adaptive modulation technology is applied to each subcarrier, and the modulation mode is dynamically selected according to the subcarrier signal-to-noise ratio. BPSK modulation is used for signal-to-noise ratios below 5dB, QPSK modulation is used for signal-to-noise ratios of 5-12dB, 16QAM modulation is used for signal-to-noise ratios of 12-18dB, and 64QAM modulation is used for signal-to-noise ratios higher than 18dB. Finally, channel bundling technology is used to form multiple physical channels into a logical communication link to achieve multi-channel parallel transmission. This step aims to improve the spectrum efficiency and anti-interference capability of the communication system through frequency diversity and multi-channel parallel transmission technology, and provide a reliable communication foundation for subsequent data transmission.

[0048] The specific implementation of step S02 is to implement an electromagnetic interference identification algorithm. First, the background electromagnetic signal in the power distribution environment is collected, and the sampling rate is set to an upper limit of 2.5 times the highest possible interference frequency. The collected signal is then preprocessed using a Hanning window function to reduce spectrum leakage effects. A 512-point fast Fourier transform algorithm is then executed to convert the time domain signal into a frequency domain representation to obtain spectral characteristics. Subsequently, a peak detection algorithm is used to identify significant peaks in the spectrum, with the peak identification threshold set at 3 times the average spectral power. The detected spectral peaks are then compared with the interference source feature database, and the interference source type is identified using a similarity matching algorithm with a similarity threshold set at 85%. Finally, the identified interference sources are classified and sorted according to frequency range and interference intensity, and an interference source frequency characteristics report is generated. This step aims to accurately identify the main electromagnetic interference sources and their frequency characteristics in the power distribution environment, providing data support for the development of subsequent interference avoidance plans.

[0049] The specific implementation of step S03 is to dynamically adjust the signal transmission frequency band according to the frequency characteristics of the interference source. First, according to the interference source frequency characteristics report obtained in step S02, an interference frequency band mapping table is established, and the interference intensity is divided into three levels. The interference power density is higher than 10 -8 W / Hz is a strong interference area, and the interference power density is 10 -10 ~10 -8 The interference power density is lower than 10 W / Hz. -10W / Hz is the weak interference area; then the interference density distribution of each frequency band is calculated to form a spectrum availability evaluation map; then the heuristic spectrum allocation algorithm is applied to allocate the optimal frequency band for data transmission, giving priority to the interference power density below 10 -10 W / Hz frequency bands; a dynamic frequency band switching mechanism is then established, triggering the frequency band switching process when the interference intensity of the currently used frequency band increases by more than 30%. Finally, a frequency band negotiation protocol is designed to ensure that the transmitter and receiver switch frequency bands synchronously, with a negotiation timeout threshold set to 200ms. This step aims to improve the reliability and stability of the communication system in strong electromagnetic interference environments by intelligently avoiding major interfering frequency bands.

[0050] The specific implementation of step S04 is to use an adaptive sampling rate control module to adjust the data acquisition frequency. First, the change gradient of the monitored signal is calculated, and the rate of change between the current data point and the previous and next data points is calculated using the central difference method. Then, a change gradient threshold is set, dividing the change gradient into a high change zone, a medium change zone, and a low change zone. The high change zone threshold is set to 10% / s, and the low change zone threshold is set to 1% / s. Next, the sampling rate is dynamically adjusted based on the change gradient. The sampling rate in the high change zone is set to twice the baseline sampling rate, the medium change zone maintains the baseline sampling rate, and the low change zone sampling rate is set to 1 / 3 of the baseline sampling rate, which is set to 100Hz. Then, a sliding window mechanism is implemented to monitor the effect of the sampling rate adjustment, with the window width set to 30 sampling points. Finally, the effectiveness of the sampling strategy is evaluated through data redundancy analysis. The redundancy calculation formula is the ratio of the data change amount between adjacent sampling points to the sampling frequency, and the redundancy target value is controlled at 5% to 15%. This step aims to avoid data redundancy and resource waste caused by high-frequency sampling by intelligently adjusting the sampling frequency based on the data change rate, while ensuring the acquisition accuracy of rapidly changing data.

[0051] The specific implementation of step S05 is to compress the collected data using a hierarchical data compression algorithm. First, the data is stratified according to data type and importance, and divided into three layers: key operating parameters, general status information, and historical trend data. Then, a lossless compression algorithm is applied to the key operating parameters using Huffman coding technology, with a compression ratio controlled at 1.5:1 to 2:1. Next, a mild lossy compression algorithm is applied to the general status information using discrete cosine transform combined with quantization technology, with a compression ratio controlled at 5:1 to 8:1. Subsequently, a high compression ratio is applied to the historical trend data using wavelet transform combined with threshold filtering technology, with a compression ratio controlled at 15:1 to 20:1. Finally, the compressed data of each layer is encapsulated and the necessary metadata identifiers are added to form a low-redundancy data packet. This step aims to minimize the amount of transmitted data and improve communication efficiency while ensuring the integrity of key data through differentiated compression strategies.

[0052] The specific implementation of step S06 is to enhance the mobile network signal strength through a signal booster. First, a directional antenna booster is deployed with an antenna gain set to 8-12dBi. Then, the received signal strength is monitored in real time, with the minimum acceptable signal strength set to -95dBm. Adaptive power control technology is then applied to dynamically adjust the transmit power based on the link quality. The required power is calculated using a link quality evaluation function. The evaluation function inputs include the received signal strength, bit error rate, and signal-to-noise ratio, and the output is a power adjustment instruction. A power adjustment step control mechanism is then established, with a power adjustment step of 0.5dB under normal conditions and 2dB under emergency conditions. Finally, a power cap control strategy is implemented, with the transmit power cap set to 85% of the mobile terminal's maximum allowable power. Taking energy consumption into consideration, the power cap is automatically reduced to 70% when the battery charge drops below 20%. This step aims to ensure the quality of the communication link while minimizing energy consumption and reducing interference to other devices through intelligent control of transmit power.

[0053] The specific implementation of step S07 is to apply the electromagnetic field propagation physics model to analyze signal attenuation characteristics. First, a propagation model is established based on the dielectric parameters of the power distribution environment, with the dielectric conductivity ranging from 0.001 to 0.1 S / m and the relative dielectric constant of the environment ranging from 1 to 5. Maxwell's equations are then used to express the laws of electromagnetic wave propagation and calculate the electric field intensity distribution. Next, a ray tracing model is established to analyze signal reflection and scattering characteristics, taking into account multipath effects. Path loss is then calculated based on transmission distance, frequency, and environmental parameters, using an improved free-space loss model. Finally, an electromagnetic field intensity distribution map is generated to predict signal reception strength at different locations and identify weak signal areas with field strengths below -100 dBm. This step aims to predict electromagnetic field distribution through theoretical models, providing a scientific basis for signal enhancement and communication parameter optimization.

[0054] The specific implementation of step S08 involves applying a digital signal processing algorithm to the receiving end for signal recovery. First, the received signal quality is assessed, calculating the signal-to-noise ratio, bit error rate, and constellation deviation. A Wiener filter is then applied to suppress Gaussian noise within the bandwidth, with the filter parameters dynamically adjusted based on the noise power spectrum estimate. An adaptive equalizer is then used to compensate for channel distortion, using a least mean square algorithm to update coefficients with a convergence step size of 0.01. Notch filtering is then used to address burst interference, rapidly detecting the interference frequency and placing a notch at that frequency, with a notch depth set to 20dB. Finally, forward error correction is performed using a Reed-Solomon code with a bit rate of 3 / 4. This step aims to eliminate signal distortion caused by electromagnetic interference through advanced digital signal processing techniques, thereby improving data transmission reliability.

[0055] The specific implementation of step S09 is to dynamically adjust communication parameters using a two-layer game optimization model. First, an upper-layer objective function is constructed, with maximizing data transmission throughput and minimizing transmission delay as the optimization goals. The upper-layer objective function expression is a weighted combination of throughput and delay, and the weight coefficient is dynamically adjusted according to the service type. Then, a lower-layer objective function is constructed, with minimizing energy consumption and maximizing signal quality as the optimization goals. The lower-layer objective function expression is a weighted combination of energy consumption and signal quality indicators. Optimization constraints are then set, including power constraints, spectrum resource constraints, channel capacity constraints, and energy constraints. The upper limit of the power constraint is set to the maximum allowable power of the device, and the lower limit is set to the power value required to ensure minimum communication quality. A hierarchical iterative optimization algorithm is then used to solve the two-layer game problem, using a particle swarm optimization algorithm in the upper layer and a Lagrange multiplier method in the lower layer. Finally, key communication parameters, including transmit power, channel bandwidth, modulation and coding scheme, are dynamically adjusted based on the optimization results, with an adjustment period set to 5 seconds. This step aims to achieve an optimal balance in the overall performance of the communication system through multi-objective optimization within the game theory framework, improving the system's ability to adapt to complex environments.

[0056] This communication method for remotely controlling distribution boxes effectively addresses issues such as strong electromagnetic interference, unstable signal transmission, and large data volumes in power distribution environments through the integrated application of multi-channel transmission, electromagnetic interference identification and avoidance, adaptive sampling, hierarchical data compression, adaptive power control, electromagnetic field modeling, signal recovery processing, and two-layer game optimization. This significantly improves the reliability, stability, and efficiency of the remote control distribution box communication system. In practical applications, this method allows for flexible adjustment of parameter thresholds based on specific distribution environment characteristics and business requirements to achieve optimal system performance.

[0057] Specifically, the principle of the present invention is as follows: The technical principle of the present invention is based on a multi-dimensional collaboratively optimized communication system architecture. The core lies in the organic combination of electromagnetic field theory and communication theory to form a mechanism for sensing, analyzing, and adapting to complex electromagnetic environments. First, orthogonal frequency division multiplexing modulation technology is used to establish a multi-channel transmission link, and frequency diversity is used to improve anti-interference capabilities. This technology enables the system to use multiple frequency bands to transmit data simultaneously. When one frequency band is interfered with, other frequency bands can still maintain communication.

[0058] Secondly, the electromagnetic interference identification algorithm based on fast Fourier transform can accurately analyze the spectral characteristics of the interference source, providing a decision-making basis for dynamic frequency band adjustment. This active interference identification and avoidance method is superior to passive interference mitigation technology because it can take appropriate measures based on the specific interference characteristics. Furthermore, the adaptive sampling rate control module dynamically adjusts the sampling frequency based on the data change gradient, realizing intelligent data acquisition and avoiding the oversampling or undersampling problems caused by a fixed sampling rate.

[0059] The core innovation of this invention lies in the introduction of a physical model of electromagnetic field propagation based on Maxwell's equations. This model accurately analyzes and predicts the propagation characteristics of electromagnetic waves in power distribution environments, including phenomena such as reflection, scattering, and attenuation. Through electromagnetic theoretical calculations, the system can predict the signal quality of each frequency band, providing theoretical support for frequency band selection and power control. This physics-based approach is superior to empirical models because it considers the physical nature of electromagnetic wave propagation and can adapt to various complex environments.

[0060] Furthermore, a two-layer game optimization model decomposes the communication system parameter optimization problem into two levels: the upper level optimizes transmission efficiency, and the lower level optimizes energy consumption and signal quality. This hierarchical optimization structure can handle multi-objective conflicts. For example, increasing transmission rate often requires increasing power, which in turn increases energy consumption. Using game theory, the system can find the optimal parameter configuration within various constraints to achieve optimal overall performance. This model considers practical constraints such as power, spectrum resource constraints, channel capacity constraints, and energy constraints to ensure the feasibility of the optimization results.

[0061] In summary, the present invention forms a complete solution through the synergy of multiple technologies, conforms to the basic theory of electromagnetic wave propagation and communication system optimization, and can effectively solve the communication reliability problem in the complex electromagnetic environment of the distribution box.

[0062] A specific embodiment 1 of the present invention is provided below. The specific implementation of each step in this embodiment 1 is described in detail as follows.

[0063] The specific implementation of step S01 is to establish a multi-channel transmission link between the mobile terminal and the distribution box. First, 3 to 5 available frequency channels are identified by scanning the available frequency band, and the bandwidth of each channel is set to 20MHz; then the signal-to-noise ratio of each available channel is measured, and the channel with a signal-to-noise ratio higher than 8dB is selected as the candidate channel; then, orthogonal frequency division multiplexing modulation technology is applied to each candidate channel, and each channel is divided into 64 subcarriers with a subcarrier spacing of 312.5kHz; then, adaptive modulation technology is applied to each subcarrier, and the modulation mode is dynamically selected according to the subcarrier signal-to-noise ratio. BPSK modulation is used for signal-to-noise ratios below 5dB, QPSK modulation is used for signal-to-noise ratios of 5 to 12dB, 16QAM modulation is used for signal-to-noise ratios of 12 to 18dB, and 64QAM modulation is used for signal-to-noise ratios higher than 18dB; finally, multiple physical channels are formed into a logical communication link through channel bundling technology to realize multi-channel parallel transmission. The signal transmission mechanism based on orthogonal frequency division multiplexing can be expressed as:

[0064]

[0065] Where, X k (t) represents the composite signal of the kth channel, s i(t) represents the modulated signal on the i-th subcarrier, f i Indicates the frequency of the ith subcarrier, N indicates the total number of subcarriers, which is 64, and t indicates the time variable. Subcarrier frequency f i The calculation formula is:

[0066] f i =f c +iΔf;

[0067] Where, f c represents the channel center frequency in Hz, Δf represents the subcarrier spacing, which is 312.5kHz, and i represents the subcarrier index, which is in the range of 0≤i≤N-1. The formula for calculating the channel capacity of adaptive modulation is:

[0068]

[0069] Where C is the channel capacity in bit / s, B is the bandwidth in Hz, which is 20 MHz, S is the signal power, and N is the noise power. The signal-to-noise ratio (SNR) is expressed in dB. This step aims to improve the spectrum efficiency and anti-interference capability of the communication system through frequency diversity and multi-channel parallel transmission technology, providing a reliable communication foundation for subsequent data transmission.

[0070] The specific implementation method of step S02 is to implement the electromagnetic interference identification algorithm. First, the background electromagnetic signal in the power distribution environment is collected, and the sampling rate is set to an upper limit of 2.5 times the highest possible interference frequency; then the Hanning window function is applied to pre-process the collected signal to reduce the spectrum leakage effect; then a 512-point fast Fourier transform algorithm is executed to convert the time domain signal into a frequency domain representation to obtain the spectrum characteristics; then the peak detection algorithm is used to identify significant peaks in the spectrum, and the peak identification threshold is set to 3 times the average spectrum power; then the detected spectrum peak is compared with the interference source feature database, and the similarity matching algorithm is used to identify the interference source type, and the similarity threshold is set to 85%; finally, the identified interference sources are classified and sorted according to the frequency range and interference intensity to generate an interference source frequency characteristic report. In the electromagnetic interference identification algorithm, the Hanning window function is expressed as:

[0071]

[0072] Where w(n) represents the window function value, n represents the sampling point index, which ranges from 0≤n≤N-1, and N represents the total number of sampling points, which is 512. The signal after applying the window function is expressed as:

[0073] x′(n)=x(n)·w(n);

[0074] Where x(n) represents the original acquired signal and x′(n) represents the signal after windowing. The fast Fourier transform algorithm is expressed as:

[0075]

[0076] Where X(k) represents the frequency domain representation of the signal, and k represents the frequency index, which ranges from 0 ≤ k ≤ N-1. The peak detection condition is:

[0077]

[0078] In the formula, |X(k)| represents the spectrum amplitude, α is the peak determination coefficient, which is 3. Represents the average spectrum amplitude. The interference source similarity calculation formula is:

[0079]

[0080] Where S(X, Y) represents the similarity, with a value range of 0 ≤ S(X, Y) ≤ 1. X represents the measured spectrum feature vector, Y represents the interference source feature vector in the database, X·Y represents the vector inner product, and |X| and |Y| represent the Euclidean norm of the vectors, respectively. This step aims to accurately identify the main electromagnetic interference sources and their frequency characteristics in the power distribution environment, providing data support for the subsequent development of interference avoidance solutions.

[0081] The specific implementation of step S03 is to dynamically adjust the signal transmission frequency band according to the frequency characteristics of the interference source. First, according to the interference source frequency characteristics report obtained in step S02, an interference frequency band mapping table is established, and the interference intensity is divided into three levels. The interference power density is higher than 10 -8 W / Hz is a strong interference area, and the interference power density is 10 -10 ~10 -8 The interference power density is lower than 10 W / Hz. -10 W / Hz is the weak interference area; then the interference density distribution of each frequency band is calculated to form a spectrum availability evaluation map; then the heuristic spectrum allocation algorithm is applied to allocate the optimal frequency band for data transmission, giving priority to the interference power density below 10 -10 W / Hz frequency band; then establish a dynamic frequency band switching mechanism, triggering the frequency band switching procedure when the interference intensity of the currently used frequency band increases by more than 30%; finally, design a frequency band negotiation protocol to ensure that the transmitter and receiver switch frequency bands synchronously, and set the negotiation timeout threshold to 200ms. The frequency band availability evaluation index calculation formula is:

[0082]

[0083] Where V(f) represents the availability index of frequency band f, and its value range is 0≤V(f)≤1. The larger the value, the higher the availability. I (f) represents the interference power density of frequency band f, in W / Hz, P I_threshold Indicates the interference power density threshold, the value is 10 -8 W / Hz. The frequency band switching criteria are:

[0084]

[0085] Where, P I (f, t) represents the interference power density of frequency band f at time t, P I (f, t-Δt) represents the interference power density in frequency band f at time t-Δt, and β represents the interference growth threshold ratio, which is set to 0.3, or 30%. This step aims to improve the reliability and stability of the communication system in strong electromagnetic interference environments by intelligently avoiding the main interfering frequency bands.

[0086] The specific implementation of step S04 is to use the adaptive sampling rate control module to adjust the data acquisition frequency. First, the change gradient of the monitored signal is calculated, and the change rate of the current data point and the previous and next data points is calculated using the central difference method. Then, the change gradient threshold is set, and the change gradient is divided into a high change area, a medium change area, and a low change area. The high change area threshold is set to 10% / s, and the low change area threshold is set to 1% / s. Then, the sampling rate is dynamically adjusted according to the change gradient. The sampling rate of the high change area is set to 2 times the baseline sampling rate, the baseline sampling rate is maintained in the medium change area, and the sampling rate of the low change area is set to 1 / 3 of the baseline sampling rate. The baseline sampling rate is set to 100Hz. Then, a sliding window mechanism is implemented to monitor the sampling rate adjustment effect, and the window width is set to 30 sampling points. Finally, the effectiveness of the sampling strategy is evaluated through data redundancy analysis. The redundancy calculation formula is the ratio of the data change amount of adjacent sampling points to the sampling frequency. The redundancy target value is controlled at 5% to 15%. The signal change gradient calculation formula is:

[0087]

[0088] Where G(t) represents the signal change gradient at time t, in % / s, x(t) represents the signal value at time t, and Δt represents the sampling time interval, in s. The formula for determining the adaptive sampling rate is:

[0089]

[0090] Where, f s (t) represents the sampling rate at time t, in Hz, f0 represents the base sampling rate, which is 100 Hz, G high Indicates the high change area threshold, the value is 10% / s, G lowIndicates the low change zone threshold, which is set to 1% / s. The data redundancy calculation formula is:

[0091]

[0092] Where R represents data redundancy, and its value range is 0≤R≤1; N represents the number of sampling points in the sliding window, and its value is 30. i ) represents the time t i The signal value, f s (t i ) represents the time t i This step aims to avoid data redundancy and resource waste caused by high-frequency sampling by intelligently adjusting the sampling frequency according to the data change rate, while ensuring the collection accuracy of rapidly changing data.

[0093] The specific implementation method of step S05 is to compress the collected data using a hierarchical data compression algorithm. First, the data is layered according to the data type and importance, and the data is divided into three layers: key operating parameters, general status information, and historical trend data; then a lossless compression algorithm is applied to the key operating parameters, using Huffman coding technology, and the compression ratio is controlled at 1.5:1 to 2:1; then a mild lossy compression is applied to the general status information, using discrete cosine transform combined with quantization technology, and the compression ratio is controlled at 5:1 to 8:1; then a high compression ratio processing is applied to the historical trend data, using wavelet transform combined with threshold filtering technology, and the compression ratio is controlled at 15:1 to 20:1; finally, the compressed data of each layer is encapsulated, and the necessary metadata identifiers are added to form a low-redundancy data packet. The discrete cosine transform formula is:

[0094]

[0095] Where Y(k) represents the transformed frequency domain coefficient, k represents the frequency index, and its value range is 0≤k≤N-1, x(n) represents the time domain sampling signal, n represents the time domain index, and its value range is 0≤n≤N-1, N represents the number of sampling points, and α(k) is the normalization coefficient. When k=0 When k>0 The wavelet transform formula is:

[0096]

[0097] Where W(a, b) represents the wavelet coefficient, a represents the scale parameter, b represents the translation parameter, x(t) represents the time domain signal, ψ * Represents the conjugate of the wavelet basis function. The threshold filtering formula is:

[0098]

[0099] Where, represents the wavelet coefficient after threshold processing, and λ represents the threshold value, which is dynamically set according to the desired compression ratio and has a value range of 0.01≤λ≤0.1. The compression ratio calculation formula of the comprehensive compression algorithm is:

[0100]

[0101] In the formula, CR represents the comprehensive compression ratio, CR i represents the compression ratio of the i-th layer data, w i Represents the weight coefficient of the i-th layer data, which is determined by the importance of the data and has a value range of 0≤w i ≤1 and satisfies This step aims to minimize the amount of transmitted data and improve communication efficiency while ensuring the integrity of key data through differentiated compression strategies.

[0102] In this step, the distinction between key data and non-key data is crucial for system optimization and resource allocation. Key data mainly include: real-time voltage and current data of the distribution box. These data directly reflect the operating status of the power system and require high-precision and high-reliability transmission. They are usually processed by lossless compression, and the compression ratio is controlled at 1.5:1 to 2:1; signal-to-noise ratio and bit error rate indicators in signal transmission, especially the signal-to-noise ratio threshold in the interference environment (such as the 8dB threshold for candidate channel selection) and the signal-to-noise ratio range corresponding to various modulation modes (BPSK corresponds to <5dB, QPSK corresponds to 5~12dB, 16QAM corresponds to 12~18dB, 64QAM corresponds to >18dB); electromagnetic interference identification results, including the type, frequency characteristics and intensity of the interference source, especially the interference power density classification (>10 -8 W / Hz is a strong interference area, 10 -10 ~10 -8W / Hz is a medium interference zone, and <10-10W / Hz is a weak interference zone); as well as link quality assessment parameters (link quality threshold 0.7 and received signal strength -95dBm). Non-critical data includes: historical trend data, such as temperature change curves and long-term load records. This type of data can accept a higher compression ratio (15:1 to 20:1) and a certain degree of information loss; environmental status information, such as auxiliary monitoring parameters such as distribution room ambient temperature and humidity; management information such as equipment operating time and maintenance records; and general status information (such as power factor, phase angle, etc.). This type of data uses mild lossy compression with a compression ratio controlled at 5:1 to 8:1. The system uses a hierarchical data compression algorithm to adopt differentiated processing strategies for data of different importance, ensuring that the transmission quality of critical data is prioritized within limited communication resources, while reasonably compressing non-critical data to improve overall transmission efficiency. In the two-layer game optimization model, the upper-layer objective function focuses more on the transmission performance of key data (such as throughput and latency), while the lower-layer objective function optimizes energy consumption while ensuring signal quality. The two work together to achieve the optimal balance of overall system performance.

[0103] The specific implementation of step S06 is to improve the mobile network signal strength through a signal booster. First, a directional antenna booster is deployed, and the antenna gain is set to 8-12dBi; then, real-time monitoring of the received signal strength is implemented, and the minimum acceptable signal strength is set to -95dBm; then, adaptive power control technology is applied to dynamically adjust the transmission power according to the link quality, and the required power is calculated through the link quality evaluation function. The evaluation function input includes the received signal strength, bit error rate and signal-to-noise ratio, and the output is a power adjustment instruction; then a power adjustment step control mechanism is established. The power adjustment step is 0.5dB under normal conditions and 2dB under emergency conditions; finally, a power cap control strategy is implemented, and the transmission power cap is set to 85% of the maximum allowable power of the mobile terminal. At the same time, considering energy consumption balance, when the battery power is lower than 20%, the power cap is automatically reduced to 70%. The link quality evaluation function is expressed as:

[0104]

[0105] Where Q represents the link quality index, with a value range of 0≤Q≤1, RSSI represents the received signal strength index, in dBm, RSSI min Indicates the minimum acceptable signal strength, the value is -95dBm, RSSI max Indicates the maximum expected signal strength, the value is -50dBm, BER indicates the bit error rate, BER max Indicates the maximum acceptable bit error rate, the value is 10 -3 , SNR means signal-to-noise ratio, in dB, SNR maxrepresents the maximum expected signal-to-noise ratio, which is 30 dB. w1, w2, and w3 are weight coefficients, and they satisfy w1+w2+w3=1. The value ranges are 0.3≤w1≤0.4, 0.3≤w2≤0.4, and 0.2≤w3≤0.3. The adaptive power control formula is:

[0106]

[0107] Where P(t) represents the transmit power at time t, in dBm, and ΔP normal Indicates the power adjustment step size under normal conditions, which is 0.5dB. ΔP emergency Indicates the power adjustment step size in emergency state, the value is 2dB, Q threshold Indicates the link quality threshold, which is set to 0.7. The transmit power upper limit control formula is:

[0108]

[0109] Where, P max Indicates the current power limit, in dBm, P device_max Indicates the maximum allowed power of the device in dBm. E indicates the battery power percentage of the device. threshold This step aims to intelligently control transmit power to ensure communication link quality while minimizing energy consumption and reducing interference with other devices.

[0110] The specific implementation method of step S07 is to apply the electromagnetic field propagation physical model to analyze the signal attenuation characteristics. First, a propagation model is established based on the medium parameters in the power distribution environment. The medium conductivity range is 0.001~0.1S / m, and the relative dielectric constant of the environment ranges from 1 to 5. Then, the Maxwell equations are used to express the law of electromagnetic wave propagation and calculate the electric field intensity distribution. Then, considering the multipath effect, a ray tracing model is established to analyze the signal reflection and scattering characteristics. Then, the path loss is calculated based on the transmission distance, frequency and environmental parameters. The path loss calculation adopts an improved free space loss model. Finally, an electromagnetic field intensity distribution map is generated to predict the signal reception strength at different locations and identify weak signal areas with field strength below -100dBm. The electromagnetic wave propagation equation in Maxwell's equations is expressed as:

[0111]

[0112] Where, represents the electric field strength vector, with the unit being V / m, represents the Laplace operator, μ represents the magnetic permeability of the medium, in H / m, ε represents the dielectric constant of the medium, in F / m, σ represents the dielectric conductivity, in S / m, and t represents the time variable, in s. The formula for calculating the attenuation coefficient of electromagnetic wave propagation is:

[0113]

[0114] Where α represents the attenuation coefficient, the unit is m -1 , ω represents the angular frequency, the unit is rad / s, ω=2πf, f represents the signal frequency, the unit is Hz. The signal field strength calculation formula is:

[0115]

[0116] Where, E(d) represents the electric field strength at a distance d from the emission point, in V / m, and E0 represents the electric field strength at the emission point, in V / m, which is proportional to the emission power P. t The relationship is P t The unit is W, d represents the propagation distance, the unit is m, G t represents the transmitting antenna gain, G r Represents the receiving antenna gain. The relationship between received power and field strength is:

[0117]

[0118] Where, P r Indicates the received power in W, λ indicates the wavelength in m, c represents the speed of light, which is 3×10 8 m / s. This step aims to predict the electromagnetic field distribution through theoretical models and provide a scientific basis for signal enhancement and communication parameter optimization.

[0119] The specific implementation method of step S08 is to apply a digital signal processing algorithm to perform signal recovery at the receiving end. First, the received signal quality assessment is performed to calculate the signal-to-noise ratio, bit error rate, and constellation diagram deviation. Then, a Wiener filter is applied to suppress Gaussian noise within the bandwidth, and the filter parameters are dynamically adjusted according to the noise power spectrum estimation. Then, an adaptive equalizer is used to compensate for channel distortion. The equalizer uses the least mean square algorithm to update the coefficients, and the convergence step size is set to 0.01. Then, a notch filter technology is used to target burst interference. The interference frequency is quickly detected and a notch filter is set at this frequency point. The notch depth is set to 20dB. Finally, a forward error correction code is used for error correction. Reed-Solomon code is used, and the code rate is set to 3 / 4. The Wiener filter transfer function is:

[0120]

[0121] where \(H(f)\) represents the frequency-domain transfer function of the Wiener filter, \(S\) x (f) represents the signal power spectral density, and \(S\) n (f) represents the noise power spectral density. The update formula for the adaptive equalizer coefficients is:

[0122] w i (n + 1)=w i (n)+\(\mu\cdot e(n)\cdot x(n - i)\);

[0123] where \(w\) i (n) represents the \(i\)-th tap coefficient of the equalizer at the \(n\)-th iteration, \(\mu\) represents the convergence step size, with a value of 0.01, \(e(n)\) represents the error signal, \(e(n)=d(n)-y(n)\), \(d(n)\) represents the desired output, \(y(n)\) represents the actual output, and \(x(n - i)\) represents the delayed input signal. The transfer function of the notch filter is:

[0124]

[0125] where \(H\) notch (z) represents the \(z\)-domain transfer function of the notch filter, \(\omega_0\) represents the notch frequency, with the unit of rad / sample, \(r\) represents the notch filter bandwidth parameter, and its value range is \(0\lt r\lt1\). The closer \(r\) is to 1, the narrower the notch bandwidth. The Reed - Solomon code can be expressed as \(RS(n,k)\), where \(n\) represents the codeword length, \(k\) represents the number of information symbols, and the error - correction ability is The code rate is This step aims to eliminate signal distortion caused by electromagnetic interference through advanced digital signal processing techniques and improve the reliability of data transmission.

[0126] The specific implementation of step S09 is to dynamically adjust communication parameters using a two - layer game optimization model. First, construct the upper - layer objective function, with the optimization objectives of maximizing data - transmission throughput and minimizing transmission delay. The expression of the upper - layer objective function is a weighted combination of throughput and delay, and the weight coefficient is dynamically adjusted according to the service type. Then, construct the lower - layer objective function, with the optimization objectives of minimizing energy consumption and maximizing signal quality. The expression of the lower - layer objective function is a weighted combination of energy consumption and signal - quality metrics. Next, set the optimization constraint conditions, including power constraint, spectrum - resource constraint, channel - capacity constraint, and energy constraint. The upper limit of the power constraint is set to the maximum allowable power of the device, and the lower limit is set to the power value required to ensure the minimum communication quality. Subsequently, use a hierarchical iterative optimization algorithm to solve the two - layer game problem. The particle - swarm algorithm is used for the upper layer, and the Lagrange multiplier method is used for the lower layer. Finally, dynamically adjust key communication parameters, including transmit power, channel bandwidth, modulation - coding scheme, etc., and the adjustment period is set to 5 seconds. The upper - layer objective function of the two - layer game optimization model is:

[0127]

[0128] Where, F upper represents the upper layer objective function, X represents the upper layer decision variable vector, including channel bandwidth, transmission power, modulation and coding scheme, frame length and number of retransmissions, and Y * (X) represents the optimal solution of the lower layer given the upper layer decision X. TP represents the data transmission throughput in bits per second. D represents the transmission delay in seconds. α1 and α2 are weight coefficients that satisfy α1+α2=1 and have a value range of 0.4≤α1≤0.6 and 0.4≤α2≤0.6. The data transmission throughput is calculated as follows:

[0129]

[0130] Where B represents the channel bandwidth in Hz, P t Indicates the transmit power in W, G t represents the transmitting antenna gain, G r represents the receiving antenna gain, λ represents the wavelength in meters, d represents the transmission distance in meters, N0 represents the noise power spectral density in W / Hz, BER represents the bit error rate, L data Indicates the length of the data part, in bits, L total Indicates the total frame length in bits. The transmission delay calculation formula is:

[0131]

[0132] Where RTT is the round trip time in seconds, D proc Represents the signal processing delay, in seconds. The lower objective function of the two-layer game optimization model is:

[0133] F lower (Y,X)=β1·EC(Y,X)-β2·SQ(Y,X);

[0134] Where, F lower represents the lower-level objective function, Y represents the lower-level decision variable vector, including operating frequency, transmit power, antenna gain, modulation order, and coding rate, X represents the upper-level decision variable vector, EC represents energy consumption in J / bit, SQ represents the signal quality index, β1 and β2 are weight coefficients, and they satisfy β1+β2=1, with a value range of 0.4≤β1≤0.6 and 0.4≤β2≤0.6. The energy consumption calculation formula is:

[0135]

[0136] Where, P t Indicates the transmit power in W, P circuitIndicates circuit power consumption in W, and TP(X, Y) indicates data transmission throughput in bit / s. The signal quality index calculation formula is:

[0137] SQ(Y,X)=γ1·SNR+γ2·(1-BER)+γ3·MOS;

[0138] Where SNR is the signal-to-noise ratio (dB), BER is the bit error rate, and MOS is the mean opinion score, which is used to evaluate service quality and has a value range of 1≤MOS≤5. γ1, γ2, and γ3 are weight coefficients that satisfy γ1+γ2+γ3=1 and have values ​​in the ranges of 0.3≤γ1≤0.4, 0.3≤γ2≤0.4, and 0.2≤γ3≤0.3, respectively. The solution to the two-level game optimization problem can be expressed as:

[0139] max X F upper (X, Y * (X)

[0140] stY * (X) = argmin Y F lower (Y, X)

[0141] g i (X, Y)≤0, i=1, 2,...,m

[0142] h j (X, Y)=0, j=1, 2,..., n;

[0143] Where g i (X, Y)≤0 represents the inequality constraint, h j (X, Y) = 0 represents the equality constraint. The power constraint can be expressed as:

[0144] P min ≤P t ≤P max ;

[0145] Where, P t Indicates the transmit power in W, P min Indicates the minimum power threshold, which is the power required to ensure the lowest communication quality, in W, P max Indicates the maximum power limit, which is the maximum allowed power of the device in W. Spectrum resource constraints can be expressed as:

[0146]

[0147] Where B i Indicates the bandwidth of the i-th channel in Hz, N chIndicates the number of channels, B total Represents the total amount of available spectrum resources, in Hz. The channel capacity constraint can be expressed as:

[0148] TP≤C=B·log2(1+SNR);

[0149] Where TP is the data throughput in bit / s, C is the theoretical channel capacity in bit / s, B is the bandwidth in Hz, and SNR is the signal-to-noise ratio. The energy constraint can be expressed as:

[0150] P t +P circuit ≤P supply ;

[0151] Where, P t Indicates the transmit power in W, P circuit Indicates circuit power consumption in W, P supply Indicates the maximum power supply capacity of the energy supply system, in W. The calculation formula for the comprehensive communication efficiency index is:

[0152]

[0153] Where CEI represents the comprehensive communication efficiency index, TP represents the data transmission throughput in bit / s, D represents the transmission delay in seconds, EC represents the energy consumption in J / bit, and w tp 、w d and w e is the weight coefficient, and satisfies w tp +w d +w e =1, the value range is 0.4≤w tp ≤0.5,0.3≤w d ≤0.4,0.2≤w e ≤0.3. The calculation formula for energy efficiency signal quality index is:

[0154]

[0155] Where ESQI represents the energy efficiency signal quality index, SQ represents the signal quality index, EC represents energy consumption (unit: J / bit), and w sq and w ec is the weight coefficient, and satisfies w sq +w ec =1, the value range is 0.5≤w sq ≤0.6,0.4≤w ec≤0.5. This step aims to achieve the optimal balance of the overall performance of the communication system through multi-objective optimization under the framework of game theory and improve the system's ability to adapt to complex environments.

[0156] The communication method for remotely controlling distribution boxes in this embodiment effectively addresses issues such as strong electromagnetic interference, unstable signal transmission, and large data volumes in the power distribution environment through the integrated application of technologies such as multi-channel transmission, electromagnetic interference identification and avoidance, adaptive sampling, hierarchical data compression, adaptive power control, electromagnetic field modeling, signal recovery processing, and two-layer game optimization. This significantly improves the reliability, stability, and communication efficiency of the remote control distribution box communication system. In practical applications, the parameter thresholds and coefficients in the calculation formula can be flexibly adjusted based on the specific characteristics of the power distribution environment and business requirements to achieve optimal system performance.

[0157] To better understand and implement the present invention, Example 2 of a specific application scenario is provided below: Researchers conducted field tests using the present invention's communication method for remotely controlling distribution boxes to remotely monitor a power distribution system in a complex electromagnetic environment. The industrial park contained a variety of high-power electrical equipment, including large motors, inverters, and welding machines, which generated strong electromagnetic interference. Conventional communication methods were prone to communication interruptions or data errors in this environment, impacting the monitoring and control of the power distribution system.

[0158] The researchers first conducted detailed measurements of the electromagnetic environment in the power distribution room, as shown in Table 1:

[0159] Table 1 Measurement data of electromagnetic interference sources in the power distribution room

[0160] Interference source type Main frequency range (kHz) Interference power density (W / Hz) Interference characteristics frequency converter 10～50 <![CDATA[3.5×10 -8 ]]> Narrowband harmonics Large motor starting 0.1～5 <![CDATA[8.7×10 -8 ]]> transient broadband Switching Power Supply 30～100 <![CDATA[1.2×10 -9 ]]> High-frequency pulse welding equipment 5～30 <![CDATA[4.6×10 -8 ]]> Random Pulse Lighting system 0.05～1 <![CDATA[5.3×10 -10 ]]> Steady-state narrowband

[0161] In this environment, researchers deployed Figure 2 The remote control communication system for distribution boxes shown in the figure consists of a mobile terminal, a signal booster, and a distribution box. The mobile terminal, which can be a tablet or mobile phone carried by a worker, is used to send control commands and receive distribution box status data. The signal booster, installed in the signal transmission path between the mobile terminal and the distribution box, boosts signal strength and enables adaptive power control. The distribution box has a built-in communication module that establishes a multi-channel transmission link with the mobile terminal. The entire system operates in a distribution room environment with strong electromagnetic interference and needs to effectively resist the influence of various interference sources.

[0162] The communication system's multi-channel transmission utilizes orthogonal frequency division multiplexing (OFDM) modulation, dividing the available spectrum into 64 subcarriers with a subcarrier spacing of 312.5 kHz. The system first scans the available frequency bands and identifies four candidate channels with a signal-to-noise ratio (SNR) greater than 8 dB, each with a 20 MHz channel bandwidth. Based on the interference sources measured in Table 1, the system dynamically adjusts the signal transmission frequency band to avoid the main interfering bands. For example, if frequency converter interference is concentrated in the 10-50 kHz band, the system will prioritize spectrum resources outside this band when allocating channels.

[0163] Figure 3 The paper demonstrates the local structure of a communication link, comprising three main components: the transmitter, the transmission channel, and the receiver. The transmitter integrates an electromagnetic interference identification module, a dynamic frequency band adjustment module, an adaptive sampling rate control module, a hierarchical data compression module, and a signal booster. The transmission channel comprises a multi-channel link, orthogonal frequency division multiplexing, and electromagnetic field propagation. The receiver includes a digital signal processing module, a Wiener filter, an adaptive equalizer, a forward error correction module, and a data decompression module. The entire communication process is coordinated by an underlying two-layer game optimization model, enabling dynamic optimization of various parameters.

[0164] In terms of data collection, the system adopts an adaptive sampling rate control strategy for different types of power distribution parameters. The actual test results are shown in Table 2:

[0165] Table 2 Test results of adaptive sampling strategy

[0166]

[0167]

[0168] Collected data is processed using a hierarchical data compression algorithm, dividing the data into three layers: key operating parameters, general status information, and historical trend data. Each layer uses a different compression algorithm and compression ratio. Key operating parameters such as voltage and current are losslessly compressed using Huffman coding, achieving a compression ratio of approximately 1.8:1. General status information such as power factor and phase angle uses discrete cosine transform combined with quantization, achieving a compression ratio of approximately 6.5:1. Historical trend data, such as temperature curves, uses wavelet transform combined with threshold filtering, achieving a compression ratio of 18:1.

[0169] Figure 4 The core structure of the system, a two-layer game optimization model, is demonstrated. The model consists of two layers. The upper layer's objective function is to maximize data throughput and minimize transmission delay, with decision variables including channel bandwidth and transmission power. The lower layer's objective function is to minimize energy consumption and maximize signal quality, with decision variables including operating frequency and modulation order. The two layers are bidirectionally coupled through decision variables and constraints, forming a closed-loop optimization system.

[0170] The two-layer game optimization model is solved by a hierarchical iterative algorithm. The upper layer uses the particle swarm optimization algorithm with a population size of 30, a number of iterations of 100, and acceleration factors c1 = 1.5 and c2 = 1.8. The lower layer uses the Lagrange multiplier method with a convergence accuracy of 10. -6 During the optimization process, the system adjusts parameters every 5 seconds to adapt to changes in the electromagnetic environment in real time.

[0171] In actual tests, the performance of the system under different interference intensities is shown in Table 3:

[0172] Table 3 Comparison of system performance under different interference intensities

[0173]

[0174] Test results show that the communication method of the present invention significantly reduces the bit error rate and communication interruption frequency in strong interference environments. Through electromagnetic interference identification and dynamic frequency adjustment, the system can effectively avoid major interference bands. Adaptive sampling rate control strategies save an average of 63.6% of sampling resources. A hierarchical data compression algorithm reduces the total data volume by 78.5%, effectively reducing the transmission burden. Finally, a digital signal processing algorithm effectively suppresses interference signals at the receiving end, improving signal quality.

[0175] Traditional communication methods for remote control distribution boxes mainly use single-channel transmission in a fixed frequency band, which lacks the ability to perceive and adapt to the electromagnetic environment. They use fixed sampling rates and simple compression algorithms, resulting in data redundancy and resource waste. Their signal processing capabilities are limited and they cannot effectively resist complex electromagnetic interference. In strong interference environments, the bit error rate of traditional methods can reach as high as 1.5×10 -2 ,communication interruptions occur frequently, seriously affecting the remote monitoring and control functions of the power distribution system.

[0176] In contrast, the present invention effectively solves the problems of strong electromagnetic interference, unstable signal transmission, and large data volume in the power distribution environment through the comprehensive application of technologies such as multi-channel transmission, electromagnetic interference identification and avoidance, adaptive sampling, hierarchical data compression, adaptive power control, electromagnetic field modeling, signal recovery processing, and two-layer game optimization. In a strong interference environment, the bit error rate of the method of the present invention is only 4.3×10 -4 , a 97.1% reduction compared to traditional methods; the frequency of communication interruptions was reduced from 8.7 times / hour to 0.4 times / hour, a 95.4% reduction, achieving high reliability and stability of the communication system. Furthermore, by optimizing sampling strategies and data compression, the proposed method significantly reduces data redundancy, improves communication efficiency and system response speed, and provides reliable communication support for remote monitoring and control of power distribution systems.

[0177] It should be noted that signal boosters generally refer to electronic devices that amplify and improve wireless signal quality. These devices primarily include RF amplifiers, repeaters, distributed antenna systems, and signal amplification repeaters. In industrial and power system applications, common signal boosting devices include RF power amplifiers (RF power amplifiers), bidirectional amplifiers (BDAs), and distributed antenna systems (DASs). Examples include Huawei's DBS3900 series and ZTE's ZXC10 BTS series. These devices typically have power ranging from a few watts to hundreds of watts, depending on the specific application scenario and communication technology.

[0178] It should be noted that the variables involved in the present invention are explained in detail as shown in Tables 4, 5 and 6 below.

[0179] Table 4 Variable Explanation Table (Part 1)

[0180]

[0181] Table 5 Variable Explanation Table (Part 2)

[0182]

[0183]

[0184] Table 6 Variable Explanation Table (Part 3)

[0185]

[0186] The above description is only a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with this technical field can easily think of changes or replacements within the technical scope disclosed by the present invention, which should be covered by the scope of protection of the present invention.

Claims

1. A communication method for remotely controlling a distribution box, characterized in that: include: Establish a multi-channel transmission link between the mobile terminal and the distribution box; implement an electromagnetic interference identification algorithm to identify the frequency characteristics of the interference source; dynamically adjust the signal transmission frequency band according to the frequency characteristics of the interference source; use an adaptive sampling rate control module to adjust the data acquisition frequency; use a hierarchical data compression algorithm to compress the collected data; improve the mobile network signal strength through a signal booster; apply an electromagnetic field propagation physical model to analyze the signal attenuation characteristics; apply a digital signal processing algorithm to perform signal recovery at the receiving end; and use a two-layer game optimization model to dynamically adjust communication parameters.

2. The communication method for remotely controlling a distribution box according to claim 1, characterized in that: The multi-channel transmission link specifically uses multiple frequency bands or channels to transmit data simultaneously in a single communication connection.

3. The communication method for remotely controlling a distribution box according to claim 2, characterized in that: The multi-channel transmission link adopts a signal transmission mechanism based on orthogonal frequency division multiplexing modulation.

4. The communication method for remotely controlling a distribution box according to claim 3, characterized in that: The electromagnetic interference identification algorithm specifically uses fast Fourier transform technology to perform frequency domain analysis on the received signal, extracts the interference signal pattern from the spectrum characteristics, and determines the type and intensity of the interference source through pattern matching method.

5. The communication method for remotely controlling a distribution box according to claim 4, characterized in that: The adaptive sampling rate control module is specifically a module that dynamically adjusts the sampling frequency according to the data change rate. When the data changes rapidly, the sampling rate is increased, and when the data is stable, the sampling rate is reduced.

6. The communication method for remotely controlling a distribution box according to claim 5, characterized in that: The hierarchical data compression algorithm is a data processing algorithm that divides data into layers according to their importance and uses different compression ratios for data at different layers to ensure that critical data is retained with high precision and non-critical data is compressed at a high ratio.

7. The communication method for remotely controlling a distribution box according to claim 6, characterized in that: The adaptive power control technology specifically adjusts the signal transmission power in real time according to the quality of the communication link.

8. The communication method for remotely controlling a distribution box according to claim 1, characterized in that: The electromagnetic field propagation physical model is specifically an electromagnetic field propagation theoretical model based on Maxwell's equations, which is used to analyze the attenuation characteristics and interference mechanism of electromagnetic waves in the power distribution environment; the electromagnetic field propagation physical model is used to predict the propagation characteristics and attenuation laws of electromagnetic waves in the environment around the distribution box. The input includes dielectric conductivity, environmental relative dielectric constant, signal frequency, transmission power, and propagation distance, and the output is the field strength at the receiving point and the signal attenuation coefficient.

9. The communication method for remotely controlling a distribution box according to claim 1, characterized in that: The two-layer game optimization model is a hierarchical optimization structure composed of an upper layer objective function and a lower layer objective function, and is a mathematical model for finding the optimal parameter configuration of a communication system under multi-objective constraints.

10. The communication method for remotely controlling a distribution box according to claim 9, characterized in that: The upper-level objective function of the two-layer game optimization model aims to maximize data transmission throughput while minimizing transmission delay. The input parameters include channel bandwidth, transmission power, modulation and coding scheme, frame length, number of retransmissions and the lower-level optimal solution, and the comprehensive communication efficiency index is calculated through nonlinear combination; the lower-level objective function of the two-layer game optimization model aims to minimize energy consumption while maximizing signal quality. The input parameters include operating frequency, transmission power, antenna gain, modulation order, coding rate and upper-level decision variables, and the energy efficiency signal quality index is calculated through nonlinear combination; the constraints of the two-layer game optimization model include power constraints, spectrum resource constraints, channel capacity constraints and energy constraints. The upper-level objective function and the lower-level objective function are bidirectionally coupled through the upper-level decision variables and the lower-level optimal solution.

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