Holographic electromagnetic wave signal transmission method and system based on mine shaft

Abstract

The invention discloses a holographic electromagnetic wave signal transmission method and system based on a mine shaft, and relates to the technical field of signal transmission, and the method comprises the steps: obtaining mine shaft environment parameters and holographic electromagnetic wave signal basic setting parameters, carrying out the multipath propagation analysis based on the mine shaft environment parameters, and obtaining the multipath propagation characteristic data; performing signal delay spread simulation analysis on the multipath propagation characteristic data to obtain signal delay spread data, and performing inter-symbol interference association mining based on the signal delay spread data to obtain inter-symbol interference association data; signal modulation optimization design is carried out according to the intersymbol interference associated data, signal modulation optimization data are generated, holographic electromagnetic wave signal transmission planning is carried out through the signal modulation optimization data, holographic electromagnetic wave signal transmission planning data are obtained, and then signal transmission operation in the mine shaft is guided according to the holographic electromagnetic wave signal transmission planning data. The method has the effect of improving the holographic electromagnetic wave signal transmission efficiency.

Description

Technical Field

[0001] This application relates to the field of signal transmission technology, and in particular to a holographic electromagnetic wave signal transmission method and system based on mine shafts. Background Technology

[0002] In mine shaft production and maintenance operations, the signaling system is a crucial nerve center for ensuring safety and coordinating equipment interlocking operations. With the development of wireless communication technology, the industry has begun to explore the use of electromagnetic waves for wireless signal transmission within shafts, aiming to achieve remote, automatic, and intelligent signal control, thereby significantly improving operational efficiency and safety.

[0003] In related technologies, when electromagnetic waves propagate inside a wellbore, they are subjected to continuous reflection, scattering, and diffraction from numerous complex objects such as the well wall, guide beams, cables, pipelines, and operating cages, resulting in a strong multipath propagation effect. Consequently, an original signal emitted from the transmitting end will reach the receiving end through multiple paths of varying lengths, generating multiple signal copies with significant time delays. These copies superimpose at the receiving point, causing signal delay extension and severely damaging the signal's temporal integrity. Furthermore, for systems employing digital modulation, this temporal dispersion can trigger severe inter-symbol interference, where the waveform of the preceding symbol "tails" and intrudes into the time interval of the following symbol, causing bit errors at the receiving end during decision-making. This directly restricts the reliability and accuracy of signal transmission, thereby reducing the transmission efficiency of holographic electromagnetic wave signals, indicating areas for improvement. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this application provides a method and system for transmitting holographic electromagnetic wave signals based on mine shafts.

[0005] In a first aspect, this application provides a holographic electromagnetic wave signal transmission method based on a mine shaft, comprising the following steps:

[0006] Step S1: Obtain the environmental parameters of the mine shaft and the basic setting parameters of the holographic electromagnetic wave signal. Perform multipath propagation analysis based on the environmental parameters of the mine shaft and obtain multipath propagation characteristic data by identifying the multipath propagation path.

[0007] Step S2: Based on the basic setting parameters of the holographic electromagnetic wave signal, perform signal delay spread simulation analysis on the multipath propagation characteristic data to obtain signal delay spread data. Based on the signal delay spread data, perform inter-symbol interference correlation mining to obtain inter-symbol interference correlation data.

[0008] Step S3: Based on the inter-symbol interference correlation data, perform signal modulation optimization design to generate signal modulation optimization data. Use the signal modulation optimization data to plan the transmission of holographic electromagnetic waves, obtain holographic electromagnetic wave signal transmission planning data, and then guide the signal transmission operation in the mine shaft based on the holographic electromagnetic wave signal transmission planning data.

[0009] Preferably, step S1 includes the following steps:

[0010] Step S11: Obtain the environmental parameters of the mine shaft and the basic setting parameters of the holographic electromagnetic wave signal;

[0011] Step S12: Vectorize the environmental parameters of the mine shaft structure to generate a shaft structure vector map;

[0012] Step S13: Extract multipath propagation paths from the vertical shaft structure vector diagram to obtain multipath propagation path data;

[0013] Step S14: Perform multipath propagation analysis on the multipath propagation path data based on the vertical shaft structure vector diagram to obtain multipath propagation characteristic data.

[0014] Preferably, step S2 includes the following steps:

[0015] Step S21: Extract the signal center frequency and bandwidth from the basic setting parameters of the holographic electromagnetic wave signal;

[0016] Step S22: Based on the basic setting parameters of the holographic electromagnetic wave signal and the signal center frequency, perform signal delay spread simulation analysis on the multipath propagation characteristic data to obtain signal delay spread data;

[0017] Step S23: Fit the inter-symbol interference probability to the signal delay spread data to generate inter-symbol interference probability data;

[0018] Step S24: Based on the inter-symbol interference probability data, perform inter-symbol interference correlation mining on the signal center frequency to obtain inter-symbol interference correlation data.

[0019] Preferably, step S22 includes the following steps:

[0020] Step S221: Extract the signal center frequency and modulation type from the basic setting parameters of the holographic electromagnetic wave signal, and calculate the propagation delay between multipath paths based on the signal center frequency to obtain the multipath propagation delay;

[0021] Step S222: Perform vector superposition analysis on the multipath propagation delay based on multipath propagation characteristic data to obtain multipath signal superposition vector data;

[0022] Step S223: Based on the signal center frequency and modulation type, perform signal distortion simulation analysis on the multipath signal superimposed vector data during multi-frequency signal transmission to obtain the multi-frequency signal distortion field;

[0023] Step S224: Perform signal phase shift behavior imbalance analysis on the multi-frequency signal distortion field to obtain signal phase shift behavior imbalance data;

[0024] Step S225: Perform carrier frequency vibration regression analysis on the signal phase offset behavior imbalance data to obtain carrier frequency vibration regression data;

[0025] Step S226: Based on the signal phase offset behavior imbalance data and carrier frequency vibration regression data, perform signal delay spread simulation analysis on the multipath propagation characteristic data to obtain signal delay spread data.

[0026] Preferably, step S224 includes the following steps:

[0027] Step S2241: Identify the signal power density non-uniformity vector of the multi-frequency signal distortion field to obtain the signal power density non-uniformity vector;

[0028] Step S2242: Perform differential coupling of signal envelope temporal variation based on the signal power density non-uniformity vector to generate envelope temporal variation differential coupling data;

[0029] Step S2243: Calculate the signal phase local variance based on the signal power density non-uniformity vector and the envelope time-series variation differential coupling data;

[0030] Step S2244: Based on the signal power density non-uniformity vector, envelope time-series variation differential coupling data and signal phase local variance, the signal attenuation heat release ratio difference between different multipath orientations is deduced, and the signal attenuation ratio difference between different multipath orientations is generated.

[0031] Step S2245: Perform signal phase shift behavior imbalance analysis based on the signal attenuation ratio difference between different multipath directions to obtain signal phase shift behavior imbalance data.

[0032] Preferably, step S23 includes the following steps:

[0033] Step S231: Perform delay spread network partitioning on the signal delay spread data to obtain delay spread network partitioning data;

[0034] Step S232: Perform time delay geometric deviation feature analysis on the time delay spread network partitioned data to obtain time delay geometric deviation feature data;

[0035] Step S233: Simulate the signal symbol collision interference distribution based on the time delay geometric deviation characteristic data to generate collision interference distribution data;

[0036] Step S234: Perform strain rate component decomposition on the collision disturbance distribution data to obtain strain rate component decomposition data;

[0037] Step S235: Perform inter-symbol interference probability fitting based on strain rate component decomposition data to generate inter-symbol interference probability data.

[0038] Preferably, step S3 includes the following steps:

[0039] Step S31: Perform feature learning on the inter-symbol interference (ISI) correlation data to obtain ISI correlation feature data;

[0040] Step S32: Based on the inter-symbol interference correlation feature data, perform signal modulation optimization design on the signal center frequency to generate signal modulation optimization data;

[0041] Step S33: Perform singular configuration optimization adjustment on the signal modulation optimization data to obtain signal modulation optimization adjustment data;

[0042] Step S34: Optimize and adjust the data through signal modulation to plan the transmission of holographic electromagnetic waves, and obtain the holographic electromagnetic wave signal transmission planning data.

[0043] Preferably, step S32 includes the following steps:

[0044] Step S321: Perform carrier verticality matching on the signal center frequency based on the inter-symbol interference correlation feature data to obtain carrier verticality matching data;

[0045] Step S322: Perform adaptive adjustment control of the beam pointing angle of the holographic electromagnetic wave signal based on the carrier verticality matching data and inter-symbol interference correlation characteristic data to obtain adaptive control data of the beam pointing angle;

[0046] Step S323: Perform signal transmission speed matching on the beam pointing angle adaptive control data to obtain signal transmission speed matching data;

[0047] Step S324: Based on carrier verticality matching data, beam pointing angle adaptive control data, and signal transmission speed matching data, perform signal modulation optimization design on the signal center frequency to generate signal modulation optimization data.

[0048] Secondly, this application provides a holographic electromagnetic wave signal transmission system based on a mine shaft, including:

[0049] The data acquisition module is used to acquire environmental parameters of the mine shaft and basic setting parameters of holographic electromagnetic wave signals. Based on the environmental parameters of the mine shaft, multipath propagation analysis is performed, and multipath propagation characteristic data is obtained by identifying multipath propagation paths.

[0050] The analysis module is used to perform signal delay spread simulation analysis on multipath propagation characteristic data based on the basic setting parameters of the holographic electromagnetic wave signal, to obtain signal delay spread data, and to perform inter-symbol interference correlation mining based on the signal delay spread data to obtain inter-symbol interference correlation data.

[0051] The planning module is used to perform signal modulation optimization design based on inter-symbol interference correlation data, generate signal modulation optimization data, perform holographic electromagnetic wave signal transmission planning based on the signal modulation optimization data, obtain holographic electromagnetic wave signal transmission planning data, and then guide the signal transmission operation in the mine shaft based on the holographic electromagnetic wave signal transmission planning data.

[0052] Thirdly, this application provides a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform any of the above-described methods for transmitting holographic electromagnetic wave signals based on mine shafts.

[0053] In summary, this application includes the following beneficial technical effects:

[0054] This application provides a holographic electromagnetic wave signal transmission method based on mine shafts. By acquiring environmental parameters of the mine shaft and basic setting parameters of the holographic electromagnetic wave signal, the shaft environment is vectorized and modeled. Signal delay spread simulation and inter-symbol interference correlation mining are performed, achieving a profound insight into the channel impairment mechanism and high-fidelity simulation. Furthermore, it provides precise data support for the optimized design of signal modulation, making the final signal modulation optimization and transmission planning no longer a blind trial and error, but a "targeted treatment" based on precise "pathological diagnosis". The system's intelligence level is upgraded from traditional passive response and post-event remediation to active planning and pre-event avoidance. Through adaptive beam pointing angle control, the optimal transmission strategy can be formed before the signal is emitted, thereby significantly improving the reliability, real-time performance, and security of the signal in the harsh shaft environment, thus effectively improving the transmission efficiency of holographic electromagnetic wave signals. Attached Figure Description

[0055] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0056] Figure 1This is a flowchart of a method for transmitting holographic electromagnetic wave signals based on a mine shaft, according to an embodiment of this application.

[0057] Figure 2 This is a schematic diagram of a holographic electromagnetic wave signal transmission system based on a mine shaft, according to an embodiment of this application. Detailed Implementation

[0058] The following is in conjunction with the appendix Figure 1-2 This application will be described in further detail.

[0059] Example 1

[0060] This application discloses a holographic electromagnetic wave signal transmission method based on mine shafts.

[0061] Reference Figure 1 A holographic electromagnetic wave signal transmission method based on mine shafts includes the following steps:

[0062] Step S1: Obtain the environmental parameters of the mine shaft and the basic setting parameters of the holographic electromagnetic wave signal. Perform multipath propagation analysis based on the environmental parameters of the mine shaft. By identifying the multipath propagation path, obtain multipath propagation characteristic data.

[0063] For example, the environmental parameters of the mine shaft include shaft geometry, distribution of internal obstacles, ambient humidity and temperature data; the basic setting parameters of the holographic electromagnetic wave signal include signal center frequency, bandwidth, modulation type and transmission power; the multipath propagation characteristic data include multipath delay distribution, signal attenuation characteristics and phase offset.

[0064] For example, step S1 includes the following steps:

[0065] Step S11: Obtain the environmental parameters of the mine shaft and the basic setting parameters of the holographic electromagnetic wave signal;

[0066] Step S12: Vectorize the environmental parameters of the mine shaft structure to generate a shaft structure vector map;

[0067] Step S13: Extract multipath propagation paths from the vertical shaft structure vector diagram to obtain multipath propagation path data;

[0068] Step S14: Perform multipath propagation analysis on the multipath propagation path data based on the vertical shaft structure vector diagram to obtain multipath propagation characteristic data.

[0069] Specifically, firstly, environmental parameters of the mine shaft and basic setting parameters of the holographic electromagnetic wave signal are acquired through data acquisition equipment or sensors. The environmental parameters include the shaft's geometric dimensions such as diameter and depth, the distribution of internal obstacles such as the location and size of pipes or equipment, and ambient humidity and temperature data. The basic setting parameters of the holographic electromagnetic wave signal cover the signal center frequency, bandwidth, modulation type (phase modulation or frequency modulation), and transmission power. These parameters serve as the input basis for subsequent analysis. Next, the acquired environmental parameters of the mine shaft are vectorized. This is done by converting the shaft's geometric dimensions and obstacle distribution data into coordinate-based vector elements. Data preprocessing methods such as noise removal and normalization are used. Then, a two-dimensional or three-dimensional vector map of the shaft is constructed using a polygon mesh generation algorithm. This involves calculating the coordinates of the boundary points and center points of each obstacle and applying linear algebraic methods for spatial transformation to ensure the vector map accurately reflects the actual structural characteristics of the shaft. Finally, based on the generated shaft structure vector... Multipath propagation path extraction is performed using a ray tracing algorithm to simulate electromagnetic wave propagation in a shaft. All possible paths from the signal transmission point to the receiving point are calculated, including direct paths and indirect paths generated by obstacle reflection and diffraction. Geometric optics principles and boundary condition analysis are used to identify the starting point, ending point, and intermediate reflection points of each path. The main multipath paths are then selected through path length calculation and angle evaluation, thus obtaining multipath propagation path data. Finally, multipath propagation analysis is performed on the multipath propagation path data based on the shaft structure vector diagram. An electromagnetic wave propagation model is established to calculate the propagation time difference of each path to obtain the multipath delay distribution. A signal attenuation model combined with path length and obstacle material properties is used to evaluate signal strength loss and obtain signal attenuation characteristics. A phase calculation model is used to derive the phase offset based on path length changes and frequency parameters, thus comprehensively obtaining multipath propagation characteristic data. The entire process is iteratively optimized to ensure data consistency and accuracy, providing a foundation for subsequent holographic electromagnetic wave signal processing.

[0070] The process of obtaining multipath propagation characteristic data by establishing an electromagnetic wave propagation model to calculate the propagation time difference of each path to obtain the multipath time delay distribution, using a signal attenuation model combined with path length and obstacle material properties to evaluate signal strength loss and obtain signal attenuation characteristics, and using a phase calculation model to derive the phase offset based on path length changes and frequency parameters, is as follows:

[0071] First, an electromagnetic wave propagation model is constructed. The core of this model is a hybrid algorithm based on geometric optics and uniform diffraction theory. By discretizing the shaft space into specific grid cells defined by vector diagrams and setting the positions of the signal source and receiver, a large number of rays are emitted from the emission point in all directions using ray tracing. When these rays encounter the boundary of an obstacle defined by the vector diagram during propagation, their reflection and transmission directions are calculated based on their incident angle and the electromagnetic properties of the obstacle (such as dielectric constant and conductivity, which can be empirically derived or obtained from tables based on environmental humidity and temperature data). At the same time, for the sharp edges of the obstacle, diffraction theory is activated to calculate new ray branches. All rays and their reflection and diffraction branches are tracked recursively until their energy is lower than the set threshold or they reach the receiving area. This dynamically simulates all possible propagation paths of electromagnetic waves in a complex shaft environment and summarizes them to form multipath propagation path data.

[0072] Based on this, a signal attenuation model is established. This model is a multi-factor empirical model that comprehensively considers free space path loss and additional attenuation introduced by obstacles. For each identified propagation path, its total attenuation consists of two parts: free space basic attenuation and obstacle interaction attenuation. The free space basic attenuation is calculated by substituting the path length into the logarithmic path loss formula related to the signal frequency. The obstacle interaction attenuation is the sum of the signal strength loss caused by each reflection or transmission event with an obstacle on the path. The amount of loss for each reflection or transmission is determined by consulting a pre-established electromagnetic parameter table based on the obstacle material and incident angle. For diffraction paths, an additional attenuation factor caused by diffraction is calculated. Finally, all attenuation components are superimposed in linear or logarithmic units to obtain the total signal attenuation value of the path, thereby characterizing its attenuation characteristics.

[0073] Finally, a phase calculation model is established. This model is mainly used to accurately calculate the phase offset of each multipath signal relative to the direct path when it arrives at the receiver. Its core is to calculate the phase difference caused by the difference in path length. That is, by converting the difference between the physical length of a certain path and the physical length of the direct path into the corresponding number of signal wavelengths, and then multiplying the decimal part of this number of wavelengths by a multiple of the circumference angle to obtain the basic phase offset. At the same time, the model also considers the additional phase change that may be introduced when the signal is reflected on the surface of the obstacle. This phase change value is also obtained by looking up the predefined phase change table according to the electromagnetic properties of the obstacle and the angle of incidence. Finally, the phase difference caused by the difference in path length and the phase change caused by all reflection events are algebraically summed to obtain the total phase offset when the signal of the path arrives at the receiver.

[0074] Step S2: Based on the basic setting parameters of the holographic electromagnetic wave signal, perform signal delay spread simulation analysis on the multipath propagation characteristic data to obtain signal delay spread data. Based on the signal delay spread data, perform inter-symbol interference correlation mining to obtain inter-symbol interference correlation data.

[0075] For example, step S2 includes the following steps:

[0076] Step S21: Extract the signal center frequency and bandwidth from the basic setting parameters of the holographic electromagnetic wave signal;

[0077] Step S22: Based on the basic setting parameters of the holographic electromagnetic wave signal and the signal center frequency, perform signal delay spread simulation analysis on the multipath propagation characteristic data to obtain signal delay spread data;

[0078] Step S23: Fit the inter-symbol interference probability to the signal delay spread data to generate inter-symbol interference probability data;

[0079] Step S24: Based on the inter-symbol interference probability data, perform inter-symbol interference correlation mining on the signal center frequency to obtain inter-symbol interference correlation data.

[0080] For example, step S22 includes the following steps:

[0081] Step S221: Extract the signal center frequency and modulation type from the basic setting parameters of the holographic electromagnetic wave signal, and calculate the propagation delay between multipath paths based on the signal center frequency to obtain the multipath propagation delay;

[0082] Step S222: Perform vector superposition analysis on the multipath propagation delay based on multipath propagation characteristic data to obtain multipath signal superposition vector data;

[0083] Step S223: Based on the signal center frequency and modulation type, perform signal distortion simulation analysis on the multipath signal superimposed vector data during multi-frequency signal transmission to obtain the multi-frequency signal distortion field;

[0084] Step S224: Perform signal phase shift behavior imbalance analysis on the multi-frequency signal distortion field to obtain signal phase shift behavior imbalance data;

[0085] Step S225: Perform carrier frequency vibration regression analysis on the signal phase offset behavior imbalance data to obtain carrier frequency vibration regression data;

[0086] Step S226: Based on the signal phase offset behavior imbalance data and carrier frequency vibration regression data, perform signal delay spread simulation analysis on the multipath propagation characteristic data to obtain signal delay spread data.

[0087] Specifically, firstly, the signal center frequency and modulation type are extracted from the basic parameters of the holographic electromagnetic wave signal. Based on the signal center frequency, the propagation delay between multipath paths is calculated. The absolute time delay of each path is obtained by dividing the physical length of each multipath by the propagation speed of the electromagnetic wave in air. Simultaneously, the influence of signal frequency on wavelength is considered to adjust the delay accuracy, thus obtaining multipath propagation delay data. Next, vector superposition analysis is performed on the multipath propagation delay based on multipath propagation characteristic data (including multipath time delay distribution, signal attenuation characteristics, and phase shift). This is achieved by representing the signal of each multipath as having amplitude and... The phase vector is used, where the amplitude is adjusted by the signal attenuation characteristics according to the path loss model, and the phase is determined by the phase offset based on the path length difference and the reflection phase abrupt change. Then, the vector summation rule is used to synthesize the vectors of all paths in the complex plane, calculating the amplitude and phase angle of the synthesized vector to obtain the multipath signal superposition vector data. Subsequently, based on the signal center frequency and modulation type, signal distortion simulation analysis is performed on the multipath signal superposition vector data during multi-frequency signal transmission. This is achieved by establishing a signal waveform model based on the modulation type (e.g., simulating phase jumps for phase modulation and frequency offsets for frequency modulation), and combining... The signal center frequency is adjusted to control the dynamic range of the received signal. The time-domain convolution method is used to simulate the cumulative effect of multipath superposition in multiple transmissions, assessing inter-symbol interference and waveform distortion to generate a multi-frequency signal distortion field. Then, signal phase shift behavior imbalance analysis is performed on the multi-frequency signal distortion field. By calculating the standard deviation and mean deviation of the signal phase in the distortion field, anomalies of phase jumps or drifts are identified. The phase-locked loop principle is used to simulate phase tracking errors and quantify unstable phase regions, thus obtaining signal phase shift behavior imbalance data. Finally, carrier frequency vibration regression analysis is performed on the signal phase shift behavior imbalance data. The least squares regression method is used to fit the carrier frequency variation trend over time, calculate the amplitude and period of frequency jitter, and analyze the frequency stability through a vibration model to obtain carrier frequency vibration regression data. Finally, based on the signal phase offset behavior imbalance data and carrier frequency vibration regression data, signal delay spread simulation analysis is performed on multipath propagation characteristic data. By statistically analyzing the distribution variance of multipath delay and the difference between the maximum and minimum values, combined with the timing error caused by phase imbalance and the period spread caused by frequency vibration, the root mean square delay spread value is calculated, thereby outputting signal delay spread data to provide a basis for subsequent channel assessment and signal compensation.

[0088] For example, step S224 includes the following steps:

[0089] Step S2241: Identify the signal power density non-uniformity vector of the multi-frequency signal distortion field to obtain the signal power density non-uniformity vector;

[0090] Step S2242: Perform differential coupling of signal envelope temporal variation based on the signal power density non-uniformity vector to generate envelope temporal variation differential coupling data;

[0091] Step S2243: Calculate the signal phase local variance based on the signal power density non-uniformity vector and the envelope time-series variation differential coupling data;

[0092] Step S2244: Based on the signal power density non-uniformity vector, envelope time-series variation differential coupling data and signal phase local variance, the signal attenuation heat release ratio difference between different multipath orientations is deduced, and the signal attenuation ratio difference between different multipath orientations is generated.

[0093] Step S2245: Perform signal phase shift behavior imbalance analysis based on the signal attenuation ratio difference between different multipath directions to obtain signal phase shift behavior imbalance data.

[0094] Specifically, firstly, the signal power density non-uniformity vector is identified in the multi-frequency signal distortion field. By analyzing the non-uniformity of signal power distribution in the time and frequency dimensions of the distortion field, the power density change rate at each sampling point is calculated using the statistical gradient calculation method. Based on the vector synthesis principle, the spatial gradient direction and amplitude of the power density are integrated into a multi-dimensional vector, thereby characterizing the heterogeneity of power distribution and obtaining the signal power density non-uniformity vector. Next, based on this vector, differential coupling of the signal envelope temporal variation is performed. The envelope ripple rate is quantified by extracting the signal envelope line and calculating its first derivative with time. Then, the derivative sequence is subjected to dot product operation and normalization with the power density non-uniformity vector to couple the correlation between power distribution heterogeneity and envelope dynamic changes, generating envelope temporal variation differential coupling data. Then, based on the signal power density non-uniformity vector and the envelope temporal variation differential coupling data, the signal phase local variance is calculated. The sliding window method is used to divide the time series into local intervals. In each interval, the standard deviation and mean difference of the phase values ​​are calculated and combined with... The variance of the power density vector is adjusted by weighting factors to reflect the impact of power non-uniformity on phase stability, thus obtaining the signal phase local variance. Then, based on the signal power density non-uniformity vector, envelope time-series variation differential coupling data, and signal phase local variance, the signal attenuation heat release ratio difference between different multipath directions is extrapolated. By simulating the attenuation paths of multipath signals in different propagation directions, the ratio of signal energy loss to equivalent heat release for each path is calculated. The heat dissipation parameters in the attenuation model are corrected using phase variance data. Finally, the ratios of different directional paths are differentially calculated to highlight directional differences, generating the signal attenuation ratio difference between different multipath directions. Finally, based on this attenuation ratio difference, signal phase shift behavior imbalance analysis is performed. By establishing an empirical relationship model between phase shift and attenuation ratio difference, the fluctuation amplitude and frequency of phase shift are calculated. An imbalance index evaluation method (such as calculating the skewness and kurtosis of the phase shift sequence) is used to quantify the nonlinear deviation of phase behavior, thus obtaining signal phase shift behavior imbalance data. The entire process, through continuous data conversion and coupling analysis, ensures a comprehensive characterization of power distribution and phase behavior, providing accurate imbalance feature inputs for subsequent signal processing.

[0095] For example, step S23 includes the following steps:

[0096] Step S231: Perform delay spread network partitioning on the signal delay spread data to obtain delay spread network partitioning data;

[0097] Step S232: Perform time delay geometric deviation feature analysis on the time delay spread network partitioned data to obtain time delay geometric deviation feature data;

[0098] Step S233: Simulate the signal symbol collision interference distribution based on the time delay geometric deviation characteristic data to generate collision interference distribution data;

[0099] Step S234: Perform strain rate component decomposition on the collision disturbance distribution data to obtain strain rate component decomposition data;

[0100] Step S235: Perform inter-symbol interference probability fitting based on strain rate component decomposition data to generate inter-symbol interference probability data.

[0101] Specifically, firstly, the signal delay spread data is processed into a delay spread network. Using density-based clustering analysis, multipath signal components with similar delay values ​​are grouped into the same network node, and the mean and variance of the delay for each node are calculated as network characteristic parameters. Simultaneously, network connections are established based on the delay correlation between nodes, thus constructing the network topology for delay spread and obtaining the delay spread network partitioning data. Next, the network partitioning data undergoes delay geometric deviation characteristic analysis. By calculating the delay offset of each network node relative to the ideal propagation path, principal component analysis is used to extract the main geometric feature vectors of the delay distribution, including skewness, kurtosis, and spatial dispersion, thus obtaining delay geometric deviation characteristic data characterizing the irregularity of the delay distribution. Then, based on the delay geometric deviation characteristic data, signal symbol collision interference distribution is simulated. By establishing a statistical model of inter-symbol interference, the delay geometric deviation is mapped to the overlap of different symbols on the time axis. The system employs a stacking probability model and a Monte Carlo method to simulate the random collision process of symbol boundaries in a multipath environment. It calculates the interference intensity distribution at each symbol location, generating collision interference distribution data. Then, it performs strain rate component decomposition on the collision interference distribution data. By analyzing the gradient of interference intensity over time, the interference distribution is decomposed into linear strain rate components and shear strain rate components. The linear strain rate represents the uniform rate of change of interference intensity, while the shear strain rate represents the relative difference in interference intensity between different symbols, thus obtaining strain rate component decomposition data. Finally, it performs inter-symbol interference probability fitting based on the strain rate component decomposition data. By establishing a mapping relationship between strain rate components and the probability of inter-symbol interference occurrence, it uses logistic regression to train historical interference data, obtaining interference probability prediction models under different strain rate conditions. It then calculates the probability of inter-symbol interference occurrence under specific network partitioning and delay characteristics, generating inter-symbol interference probability data to provide a quantitative basis for the anti-interference design of subsequent communication systems.

[0102] By employing the above technical solution, the topological structure of the delay distribution is revealed through networked clustering of the delay spread data; the nonlinear distortion law of the delay is accurately captured based on geometric deviation feature analysis; and the abstract interference distribution is transformed into a calculable physical quantity through strain rate component decomposition. The resulting inter-symbol interference probability fitting not only significantly improves the accuracy of channel state assessment but also provides key data support for parameter optimization of anti-interference technologies such as adaptive modulation and forward error correction, thereby enhancing the overall robustness and transmission reliability of the communication system in complex electromagnetic environments.

[0103] Step S3: Based on the inter-symbol interference correlation data, perform signal modulation optimization design to generate signal modulation optimization data. Use the signal modulation optimization data to plan the transmission of holographic electromagnetic waves, obtain holographic electromagnetic wave signal transmission planning data, and then guide the signal transmission operation in the mine shaft based on the holographic electromagnetic wave signal transmission planning data.

[0104] For example, the signal modulation optimization data includes optimized modulation parameters, error correction coding schemes, and power control strategies.

[0105] For example, step S3 includes the following steps:

[0106] Step S31: Perform feature learning on the inter-symbol interference (ISI) correlation data to obtain ISI correlation feature data;

[0107] Specifically, firstly, feature learning is performed on the inter-symbol interference (ISI) correlation data. A deep belief network is constructed and unsupervised pre-training is performed using a multi-layer restricted Boltzmann machine. The contrastive divergence algorithm is used to adjust the network weights to capture the deep statistical regularities of ISI. Then, the network parameters are fine-tuned using a supervised backpropagation algorithm. Finally, low-dimensional essential features representing interference intensity, time correlation, and frequency dependence are extracted from the high-dimensional interference data to obtain ISI correlation feature data.

[0108] Step S32: Based on the inter-symbol interference correlation feature data, perform signal modulation optimization design on the signal center frequency to generate signal modulation optimization data;

[0109] Step S33: Perform singular configuration optimization adjustment on the signal modulation optimization data to obtain signal modulation optimization adjustment data;

[0110] Step S34: Optimize and adjust the data through signal modulation to plan the transmission of holographic electromagnetic waves, and obtain the holographic electromagnetic wave signal transmission planning data.

[0111] For example, step S32 includes the following steps:

[0112] Step S321: Perform carrier verticality matching on the signal center frequency based on the inter-symbol interference correlation feature data to obtain carrier verticality matching data;

[0113] Step S322: Perform adaptive adjustment control of the beam pointing angle of the holographic electromagnetic wave signal based on the carrier verticality matching data and inter-symbol interference correlation characteristic data to obtain adaptive control data of the beam pointing angle;

[0114] Step S323: Perform signal transmission speed matching on the beam pointing angle adaptive control data to obtain signal transmission speed matching data;

[0115] Step S324: Based on carrier verticality matching data, beam pointing angle adaptive control data, and signal transmission speed matching data, perform signal modulation optimization design on the signal center frequency to generate signal modulation optimization data.

[0116] Specifically, firstly, carrier verticality matching is performed on the signal center frequency based on inter-symbol interference (ISI) correlation characteristic data. This process involves analyzing the phase perturbation patterns and amplitude fluctuation characteristics in the interference characteristic data, calculating the projection angle of the carrier signal in the orthogonal modulation plane, evaluating the degree of orthogonality deviation between carrier components using a method based on covariance matrix eigenvalue decomposition, and dynamically adjusting the phase locking parameters of the local oscillator signal to achieve optimal orthogonality between the in-phase and quadrature branches, thus obtaining carrier verticality matching data. Next, based on the carrier verticality matching data and ISI correlation characteristic data, adaptive adjustment control of the holographic electromagnetic wave signal beam pointing angle is performed. By establishing a mapping relationship between the spatial distribution of interference and the beamforming weight matrix, the phase excitation coefficient of each antenna element is calculated using an adaptive array algorithm. Combined with carrier orthogonality data, the main lobe direction of the beam is precisely calibrated. Simultaneously, based on real-time interference characteristics, the gradient descent method is used to iteratively optimize the beam pointing angle to maximize the signal-to-interference-plus-noise ratio (SNR). This process yields adaptive beam pointing angle control data. Then, signal transmission speed matching is performed on this data. By analyzing the propagation delay differences caused by beam pointing variations, the maximum tolerable symbol period under different transmission paths is calculated. The optimal symbol rate is derived using the Nyquist criterion and channel capacity model, and the transmission timing is dynamically adjusted using Doppler frequency shift compensation technology to obtain signal transmission speed matching data. Finally, based on carrier verticality matching data, adaptive beam pointing angle control data, and signal transmission speed matching data, signal modulation optimization design is performed on the signal center frequency. A multi-parameter joint optimization model is constructed, and a weighted comprehensive evaluation method is used to balance spectral efficiency and anti-interference performance. A genetic algorithm is used to perform parallel searches in multiple dimensions such as modulation order, coding rate, and power allocation to generate optimal signal modulation data with the best overall performance under specific channel conditions, completing the overall optimization link from carrier processing to beam control and transmission parameters.

[0117] By adopting the above technical solution, the phase noise in the modulation and demodulation process is effectively reduced by accurately calibrating the carrier orthogonality; the beam pointing adjustment based on real-time interference characteristics enhances the spatial focusing capability of the signal; and the transmission speed matching combined with the channel state ensures the timing accuracy of symbol transmission. The combined effect of these three factors enables the modulation scheme to adaptively balance spectral efficiency and anti-interference performance. The resulting optimized signal modulation data not only improves the bit error rate performance of the system, but also provides key technical support for high-reliability communication and significantly enhances the transmission robustness in harsh electromagnetic environments.

[0118] Example 2

[0119] This application also discloses a holographic electromagnetic wave signal transmission system based on mine shafts.

[0120] Reference Figure 2 A holographic electromagnetic wave signal transmission system based on mine shafts includes:

[0121] The data acquisition module is used to acquire environmental parameters of the mine shaft and basic setting parameters of holographic electromagnetic wave signals. Based on the environmental parameters of the mine shaft, multipath propagation analysis is performed, and multipath propagation characteristic data is obtained by identifying multipath propagation paths.

[0122] The analysis module is used to perform signal delay spread simulation analysis on multipath propagation characteristic data based on the basic setting parameters of the holographic electromagnetic wave signal, to obtain signal delay spread data, and to perform inter-symbol interference correlation mining based on the signal delay spread data to obtain inter-symbol interference correlation data.

[0123] The planning module is used to perform signal modulation optimization design based on inter-symbol interference correlation data, generate signal modulation optimization data, perform holographic electromagnetic wave signal transmission planning based on the signal modulation optimization data, obtain holographic electromagnetic wave signal transmission planning data, and then guide the signal transmission operation in the mine shaft based on the holographic electromagnetic wave signal transmission planning data.

[0124] The above content is merely an example and illustration of the concept of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described or use similar methods to replace them, as long as they do not deviate from the concept of the invention, they should all fall within the protection scope of the present invention.

[0125] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0126] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to any specific implementation. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention.

Claims

1. A holographic electromagnetic wave signal transmission method based on mine shafts, characterized in that, Includes the following steps: Step S1: Obtain the environmental parameters of the mine shaft and the basic setting parameters of the holographic electromagnetic wave signal. Perform multipath propagation analysis based on the environmental parameters of the mine shaft and obtain multipath propagation characteristic data by identifying the multipath propagation path. Step S2: Based on the basic setting parameters of the holographic electromagnetic wave signal, perform signal delay spread simulation analysis on the multipath propagation characteristic data to obtain signal delay spread data. Based on the signal delay spread data, perform inter-symbol interference correlation mining to obtain inter-symbol interference correlation data. Step S3: Based on the inter-symbol interference correlation data, perform signal modulation optimization design to generate signal modulation optimization data. Use the signal modulation optimization data to plan the transmission of holographic electromagnetic waves, obtain holographic electromagnetic wave signal transmission planning data, and then guide the signal transmission operation in the mine shaft based on the holographic electromagnetic wave signal transmission planning data.

2. The holographic electromagnetic wave signal transmission method based on a mine shaft according to claim 1, characterized in that, Step S1 includes the following steps: Step S11: Obtain the environmental parameters of the mine shaft and the basic setting parameters of the holographic electromagnetic wave signal; Step S12: Vectorize the environmental parameters of the mine shaft structure to generate a shaft structure vector map; Step S13: Extract multipath propagation paths from the vertical shaft structure vector diagram to obtain multipath propagation path data; Step S14: Perform multipath propagation analysis on the multipath propagation path data based on the vertical shaft structure vector diagram to obtain multipath propagation characteristic data.

3. The holographic electromagnetic wave signal transmission method based on a mine shaft according to claim 1, characterized in that, Step S2 includes the following steps: Step S21: Extract the signal center frequency and bandwidth from the basic setting parameters of the holographic electromagnetic wave signal; Step S22: Based on the basic setting parameters of the holographic electromagnetic wave signal and the signal center frequency, perform signal delay spread simulation analysis on the multipath propagation characteristic data to obtain signal delay spread data; Step S23: Fit the inter-symbol interference probability to the signal delay spread data to generate inter-symbol interference probability data; Step S24: Based on the inter-symbol interference probability data, perform inter-symbol interference correlation mining on the signal center frequency to obtain inter-symbol interference correlation data.

4. The holographic electromagnetic wave signal transmission method based on a mine shaft according to claim 3, characterized in that, Step S22 includes the following steps: Step S221: Extract the signal center frequency and modulation type from the basic setting parameters of the holographic electromagnetic wave signal, and calculate the propagation delay between multipath paths based on the signal center frequency to obtain the multipath propagation delay; Step S222: Perform vector superposition analysis on the multipath propagation delay based on multipath propagation characteristic data to obtain multipath signal superposition vector data; Step S223: Based on the signal center frequency and modulation type, perform signal distortion simulation analysis on the multipath signal superimposed vector data during multi-frequency signal transmission to obtain the multi-frequency signal distortion field; Step S224: Perform signal phase shift behavior imbalance analysis on the multi-frequency signal distortion field to obtain signal phase shift behavior imbalance data; Step S225: Perform carrier frequency vibration regression analysis on the signal phase offset behavior imbalance data to obtain carrier frequency vibration regression data; Step S226: Based on the signal phase offset behavior imbalance data and carrier frequency vibration regression data, perform signal delay spread simulation analysis on the multipath propagation characteristic data to obtain signal delay spread data.

5. The holographic electromagnetic wave signal transmission method based on a mine shaft according to claim 4, characterized in that, Step S224 includes the following steps: Step S2241: Identify the signal power density non-uniformity vector of the multi-frequency signal distortion field to obtain the signal power density non-uniformity vector; Step S2242: Perform differential coupling of signal envelope temporal variation based on the signal power density non-uniformity vector to generate envelope temporal variation differential coupling data; Step S2243: Calculate the signal phase local variance based on the signal power density non-uniformity vector and the envelope time-series variation differential coupling data; Step S2244: Based on the signal power density non-uniformity vector, envelope time-series variation differential coupling data and signal phase local variance, the signal attenuation heat release ratio difference between different multipath orientations is deduced, and the signal attenuation ratio difference between different multipath orientations is generated. Step S2245: Perform signal phase shift behavior imbalance analysis based on the signal attenuation ratio difference between different multipath directions to obtain signal phase shift behavior imbalance data.

6. The holographic electromagnetic wave signal transmission method based on a mine shaft according to claim 3, characterized in that, Step S23 includes the following steps: Step S231: Perform delay spread network partitioning on the signal delay spread data to obtain delay spread network partitioning data; Step S232: Perform time delay geometric deviation feature analysis on the time delay spread network partitioned data to obtain time delay geometric deviation feature data; Step S233: Simulate the signal symbol collision interference distribution based on the time delay geometric deviation characteristic data to generate collision interference distribution data; Step S234: Perform strain rate component decomposition on the collision disturbance distribution data to obtain strain rate component decomposition data; Step S235: Perform inter-symbol interference probability fitting based on strain rate component decomposition data to generate inter-symbol interference probability data.

7. The holographic electromagnetic wave signal transmission method based on a mine shaft according to claim 1, characterized in that, Step S3 includes the following steps: Step S31: Perform feature learning on the inter-symbol interference (ISI) correlation data to obtain ISI correlation feature data; Step S32: Based on the inter-symbol interference correlation feature data, perform signal modulation optimization design on the signal center frequency to generate signal modulation optimization data; Step S33: Perform singular configuration optimization adjustment on the signal modulation optimization data to obtain signal modulation optimization adjustment data; Step S34: Optimize and adjust the data through signal modulation to plan the transmission of holographic electromagnetic waves, and obtain the holographic electromagnetic wave signal transmission planning data.

8. The holographic electromagnetic wave signal transmission method based on a mine shaft according to claim 7, characterized in that, Step S32 includes the following steps: Step S321: Perform carrier verticality matching on the signal center frequency based on the inter-symbol interference correlation feature data to obtain carrier verticality matching data; Step S322: Perform adaptive adjustment control of the beam pointing angle of the holographic electromagnetic wave signal based on the carrier verticality matching data and inter-symbol interference correlation characteristic data to obtain adaptive control data of the beam pointing angle; Step S323: Perform signal transmission speed matching on the beam pointing angle adaptive control data to obtain signal transmission speed matching data; Step S324: Based on carrier verticality matching data, beam pointing angle adaptive control data, and signal transmission speed matching data, perform signal modulation optimization design on the signal center frequency to generate signal modulation optimization data.

9. A holographic electromagnetic wave signal transmission system based on a mine shaft, applied to the holographic electromagnetic wave signal transmission method based on a mine shaft as described in any one of claims 1-8, characterized in that, include: The data acquisition module is used to acquire environmental parameters of the mine shaft and basic setting parameters of holographic electromagnetic wave signals. Based on the environmental parameters of the mine shaft, multipath propagation analysis is performed, and multipath propagation characteristic data is obtained by identifying multipath propagation paths. The analysis module is used to perform signal delay spread simulation analysis on multipath propagation characteristic data based on the basic setting parameters of the holographic electromagnetic wave signal, to obtain signal delay spread data, and to perform inter-symbol interference correlation mining based on the signal delay spread data to obtain inter-symbol interference correlation data. The planning module is used to perform signal modulation optimization design based on inter-symbol interference correlation data, generate signal modulation optimization data, perform holographic electromagnetic wave signal transmission planning based on the signal modulation optimization data, obtain holographic electromagnetic wave signal transmission planning data, and then guide the signal transmission operation in the mine shaft based on the holographic electromagnetic wave signal transmission planning data.

10. A computer-readable storage medium, characterized in that: The system stores instructions that, when executed on a computer, cause the computer to perform the holographic electromagnetic wave signal transmission method based on a mine shaft as described in any one of claims 1 to 8.

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