A method for predicting a limited space ultraviolet light channel of a coal mine underground mining working face

Abstract

The application discloses a kind of coal mine underground mining working face restricted space ultraviolet light channel prediction method, belong to coal mine communication technology and wireless optical channel modeling field.This method is aimed at the characteristics such as narrow space, dense shielding, dust and gas coexistence and dynamic change of environment in underground, obtains the propagation data under different transceiving positions, orientations and array layouts through ultraviolet light channel measurement platform, and synchronously collects scene structure, surface material and environmental parameter information;On this basis, the measured data is preprocessed, the real CIR and related characteristic parameters are extracted, the line-of-sight, macroscopic reflection, microscopic scattering and turbulent fading model are established, and the parameters are iteratively calibrated by comparing the model output with the measured channel impulse response, to obtain the ultraviolet light channel model parameter set suitable for the restricted space of coal mine underground mining working face.The method can provide technical support for channel analysis and model construction of coal mine underground ultraviolet light communication system.

Description

Technical Field

[0001] This invention relates to the field of coal mine communication and wireless optical channel modeling technology, specifically to a method for predicting ultraviolet optical channels in confined spaces of underground coal mine working faces. Background Technology

[0002] In the process of intelligent underground coal mining, ultraviolet (UV) communication has the advantages of strong resistance to electromagnetic interference and good confidentiality, making it a potential application in underground coal mine communication. Existing research mostly focuses on simpler scenarios such as indoor environments, corridors, or free spaces, typically establishing channel models based on parameters such as path loss, delay spread, or channel impulse response. While these methods have some reference value in general scenarios, they still have shortcomings when applied to the confined spaces of underground coal mine working faces.

[0003] Existing models are mostly built for single propagation mechanisms or simplified propagation conditions, making it difficult to simultaneously characterize the combined effects of line-of-sight propagation, macroscopic object reflection, microscopic particle scattering, and air turbulence disturbances on ultraviolet light propagation in underground mines. Therefore, they struggle to accurately describe the actual propagation characteristics in confined underground spaces. Current technologies typically lack modeling methods that jointly constrain the measured channel impulse response (CIR) with environmental characteristics such as scene geometry, surface material, dust concentration, gas concentration, temperature, humidity, and wind speed. This results in unclear correspondence between model parameters and the actual propagation environment, leading to insufficient scene adaptability. While existing technologies can obtain measurement or simulation results, they generally lack a closed-loop mechanism to compare the model output CIR with the measured CIR and iteratively calibrate the model parameters accordingly. Consequently, the model accuracy and engineering applicability remain limited.

[0004] The confined space of underground coal mine working faces is characterized by narrow space, dense equipment, severe obstruction, coexistence of dust and gas, and significant air disturbance. During the propagation of ultraviolet light, multiple mechanisms such as line-of-sight propagation, macroscopic object reflection propagation, microscopic particle scattering propagation, and turbulence disturbance often occur simultaneously, and these mechanisms are coupled with each other. Traditional modeling methods for simplified ground scenarios or single propagation mechanisms are difficult to accurately characterize the actual propagation characteristics underground. Therefore, it is necessary to establish an ultraviolet light channel modeling method that can uniformly describe the effects of line-of-sight, reflection, scattering, and turbulence.

[0005] Existing channel models lack joint constraints based on measured channel impulse response and environmental characteristics. Underground ultraviolet light propagation is not only related to propagation distance and transmission / reception orientation, but is also influenced by environmental factors such as the three-dimensional structure of the tunnel, equipment surface materials, gas concentration, dust concentration, temperature and humidity, wind speed, and air turbulence. Current technologies often struggle to establish a clear correspondence between measured CIR and the aforementioned environmental information, leading to a disconnect between model parameters and the actual propagation environment. This makes it difficult to develop parameterized channel models suitable for specific underground scenarios. Therefore, a modeling method based on joint constraints of measured CIR and environmental characteristics is needed to improve the model's scenario adaptability and physical consistency.

[0006] Existing technologies still suffer from the problem of difficulty in effectively calibrating model parameters based on measured results. While existing technologies can obtain theoretical model outputs or simulation results, they typically lack a closed-loop mechanism to compare the model's output CIR with the measured CIR and iteratively update reflection, scattering, and turbulence parameters based on the comparison results. This leads to discrepancies between the model's output results and the actual propagation characteristics in the well, resulting in insufficient model accuracy and engineering applicability. Therefore, it is necessary to construct a parameter calibration method based on the comparison results between the model's output CIR and the measured CIR, so that the established channel model can continuously approximate the actual propagation process in the well. Summary of the Invention

[0007] To address the shortcomings of existing technologies, this paper provides a method for predicting ultraviolet (UV) light channels in confined spaces at underground coal mine working faces. Addressing issues such as model mismatch with the actual underground propagation environment, incomplete propagation mechanism characterization, and lack of measured constraints and calibration for model parameters, this method, based on obtaining real channel response and environmental characteristic information, distinguishes between line-of-sight (LOS) and non-LOS components. It establishes LOS channel models, macroscopic object reflection channel models, microscopic particle scattering channel models, and turbulent fading models respectively. Iterative parameter calibration is achieved by comparing the model output CIR with the measured CIR, thereby obtaining a parameter set for UV light channels in confined spaces at underground coal mine working faces.

[0008] To achieve the above-mentioned technical objectives, this invention discloses a method for predicting the ultraviolet light channel in a confined space at an underground coal mine working face, the specific steps of which are as follows: S1. Obtain ultraviolet light propagation channel measurement data at the underground mining face of a coal mine, and simultaneously collect information in the roadway; S2. Obtain the true channel frequency domain response from the ultraviolet light propagation measurement data, perform time-domain separation on the channel response CIR, and obtain the line-of-sight (LOS) component and the non-line-of-sight (NLOS) component. S3. Construct an ultraviolet light propagation channel model in a confined space underground in a coal mine using LOS and NLOS components; S4. Compare the CIR predicted by the ultraviolet propagation channel model with the measured CIR, and iterate until it meets the preset requirements. Use the ultraviolet channel model that has completed the iteration to realize the prediction of the ultraviolet channel in the working plane.

[0009] Furthermore, the information within the tunnel includes the three-dimensional structural information of the tunnel and equipment, the surface material information of the equipment, gas concentration, dust concentration, temperature and humidity, wind speed and air turbulence information; a one-to-one corresponding associated dataset is constructed by using ultraviolet light propagation channel measurement data and tunnel information to provide environmental constraints for the time-domain separation of channel response CIR, the determination of ultraviolet light propagation channel model parameters, and the iterative calibration of model parameters.

[0010] Furthermore, the ultraviolet light propagation measurement data is processed to remove system response to obtain the true channel frequency domain response. The true channel frequency domain response is labeled and constrained by the transmit and receive distance, transmit and receive orientation and environmental parameters in the associated dataset. The channel impulse response, path loss, time delay spread and light intensity fluctuation parameters are extracted. The true channel frequency domain response is then separated in the time domain to obtain the line-of-sight (LOS) component and the non-line-of-sight (NLOS) component. The environmental feature vector contained in the ultraviolet light propagation measurement data is: , in, Indicates the sending and receiving distance. Indicates the orientation parameter of the transmitter. This indicates the orientation parameter of the receiving end. This represents the gas concentration parameter. This represents the dust concentration parameter. Indicates ambient temperature. Indicates ambient humidity. Indicates wind speed. Represents the turbulence intensity parameter. This represents the surface reflectivity parameter. This represents the surface roughness parameter; Let the true channel frequency domain response from the i-th transmitting unit to the j-th receiving unit be defined in the ultraviolet light propagation measurement. Represented as: , in, Indicates the received signal spectrum. Indicates the spectrum of the transmitted reference signal. This represents the overall system frequency response of the corresponding transceiver link; The true channel impulse response is obtained by inverse Fourier transform of the true channel frequency domain response, i.e.: , in, This represents the actual channel impulse response. Indicates the inverse Fourier transform; The path loss is calculated from the actual channel impulse response using the following formula. : , The actual channel impulse response is calculated using the following formula. Average latency of the corresponding link Root mean square delay spread : , , in, This represents the nth time delay sampling point. This represents the power value at the corresponding time delay sampling point.

[0011] Furthermore, a line-of-sight channel model associated with propagation distance, transmission direction, and reception direction is established based on the LOS component. Based on the NLOS component and combined with the three-dimensional structural information of roadways and equipment in the associated dataset, macroscopic reflector clusters are mapped to actual underground objects, and microscopic scatterer clusters are mapped to underground gas, dust, and water mist. Macroscopic object reflection channel model and microscopic particle scattering channel model are established respectively. Furthermore, a turbulent fading model is established by combining measured light intensity fluctuations, beam dispersion, and beam drift information, thus forming an ultraviolet light propagation channel model in the confined space of a coal mine.

[0012] Furthermore, the line-of-sight channel model is represented as follows: , , , in, This represents the impulse response of the line-of-sight path channel. Indicates the line-of-sight path amplitude gain. Represents the Dirac impulse function. Indicates the propagation delay along the line-of-sight path; This represents the line-of-sight path scaling factor. This represents the Lambertian order of the i-th transmitting unit. This represents the effective receiving area of ​​the j-th receiving unit. This represents the propagation distance from the i-th transmitting unit to the j-th receiving unit. Indicates the emission irradiance angle. Indicates the angle of incidence received. Indicates the total extinction coefficient. This represents the indicator function under the receiving field of view limitation. Let represent the receiving field of view of the j-th receiving unit, and c represent the speed of light.

[0013] Furthermore, the impulse response of the macroscopic object reflection path channel Represented as: , , , in, This indicates the total number of resolvable reflection paths. Indicates the reflection path number. Indicates the first Amplitude gain of each reflection path Indicates the first The propagation delay of each reflection path; Indicates the reflection path scaling factor. Indicates the first The reflectivity of the reflecting surface corresponds to each reflection path. Indicates the proportion of specular reflection component. Indicates the angle of incidence. Indicates the deviation angle relative to the mirror surface. Indicates the specular reflectance index. This represents the distance from the transmitting unit to the reflecting point. This indicates the distance from the reflection point to the receiving unit.

[0014] Furthermore, the impulse response of the microscopic particle scattering path channel Represented as: , , in, Indicates the effective scattering volume. Indicates the scattering path scaling factor. Represents the equivalent scattering coefficient. Represents the scattering phase function. Indicates the scattering angle. This represents the distance from the emitting element to the scattering element. This represents the distance from the scattering element to the receiving element. Represents a volume element; Indicates the total extinction coefficient. Indicates the gas absorption coefficient. Represents the gas scattering coefficient. Indicates the dust absorption coefficient. Indicates the dust scattering coefficient; Working step Dust scattering coefficient at time With dust absorption coefficient They respectively satisfy: , , in, The wavelength represents the operating wavelength, and r represents the radius of the dust particle. and These represent the minimum and maximum radii of dust particles, respectively. This represents the scattering efficiency factor. This represents the absorption efficiency factor. The complex refractive index represents the dust particle's refractive index. This represents the particle size distribution function of dust.

[0015] Furthermore, the line-of-sight path channel impulse response Macroscopic object reflection path channel impulse response Impact response of the microscopic particle scattering path channel Superposition yields the total channel impulse response under turbulence-free conditions. : , To further characterize the random disturbance effect of downhole air turbulence on the ultraviolet light propagation process, the turbulence fading factor was calculated based on the measured intensity fluctuations, beam dispersion, and beam drift information of the received signal. : , , in, This represents a random process with fluctuating logarithmic amplitude. This indicates that the mean is 0 and the variance is 0. Gaussian distribution; Introducing measured statistical characteristics of turbulence into the turbulence fading factor, the variance of the random process satisfies: , in, The flicker index represents the flickering index, which satisfies the following: , in, Let represent the random variable of received optical intensity in the link from the i-th transmitting unit to the j-th receiving unit; Combining the turbulence fading factor, the CIR predicted by the ultraviolet light propagation channel model is formed, representing the total channel impulse response from the i-th transmitting unit to the j-th receiving unit. Represented as: .

[0016] Furthermore, the CIR predicted by the ultraviolet light propagation channel model is compared with the measured CIR in terms of total duration, energy distribution in different time delay intervals, and time delay spread parameters. The reflection parameters, scattering parameters, and turbulence parameters are iteratively updated based on the comparison results until the error between the CIR predicted by the ultraviolet light propagation channel model and the measured CIR is less than a preset threshold. Construct a parameter update model that targets the difference between the measured CIR and the model output CIR: , in, This represents the parameter vector of the ultraviolet light propagation channel model to be optimized. This represents the parameter vector of the optimized ultraviolet light propagation channel model. This describes the process of finding the parameters that minimize the objective function. This represents the measured channel impulse response value. This represents the channel impulse response value output by the ultraviolet light propagation channel model. Represents the weighting coefficient of the nth time-delay sampling point; ultraviolet light propagation channel model parameter vector. This includes parameters such as reflectivity of the reflecting surface, surface roughness, reflection order, scattering order, scatterer distribution, and turbulence intensity. During the parameter update process, the CIR output of the ultraviolet light propagation channel model is compared with the measured CIR in three aspects: total duration, energy distribution in different time delay intervals, and root mean square time delay spread. Based on the comparison results, the reflection order, scattering order, and scatterer distribution parameters are iteratively updated until the difference between the model output and the measured CIR is less than a preset threshold.

[0017] A computer device is characterized by comprising a processor and a memory, wherein the processor is electrically connected to the memory, the memory is used to store instructions and data, and the processor is used to execute a method for modeling ultraviolet light channels in confined spaces of underground coal mine working faces.

[0018] Beneficial Effects: This invention possesses strong scenario specificity and environmental adaptability. By jointly constraining the measured CIR with environmental characteristics such as tunnel structure, surface material, gas concentration, dust concentration, temperature and humidity, wind speed, and air disturbance, the established model can correspond to the actual propagation environment of the confined space in underground coal mine working faces. This makes it more suitable for analyzing the propagation characteristics of underground ultraviolet light than models established under traditional simplified surface scenarios. Secondly, this invention can uniformly characterize various propagation mechanisms in underground ultraviolet light propagation. By establishing separate models for line-of-sight, macroscopic reflection, microscopic scattering, and turbulent fading, and expressing them within a unified overall channel impulse response framework, it is beneficial to more clearly reflect the impact of various propagation mechanisms on the underground ultraviolet light channel, improving the physical meaning and modeling completeness of the channel model. Furthermore, this invention has good model calibration capabilities and engineering application value. By constructing a comparison and iterative update mechanism between the model's output CIR and the measured CIR, the model parameters can be corrected based on actual underground measurement results, reducing the deviation between the model parameters and the actual scenario. This improves the model's applicability in different underground scenarios and provides a basis for channel analysis, link design, and performance evaluation of underground ultraviolet communication systems in coal mines. Finally, the technical route of this invention is clear, and the modeling process is complete. Based on measured data and constrained by environmental characteristics, this method, with sub-mechanism modeling and parameter calibration as its core, forms a relatively complete underground ultraviolet channel modeling process. This is beneficial for analyzing the complex propagation characteristics of underground coal mines and also facilitates its widespread use in subsequent research and application of underground ultraviolet communication systems. Attached Figure Description

[0019] Figure 1 This is a flowchart illustrating the ultraviolet light channel modeling method for confined space in underground coal mine mining faces according to an embodiment of the present invention.

[0020] Figure 2 A schematic diagram of the process for acquiring measured data and extracting channel parameters in an embodiment of the present invention.

[0021] Figure 3 A schematic diagram of the channel modeling and parameter calibration process in this embodiment of the invention. Detailed Implementation

[0022] The implementation of the present invention will be further described below with reference to the accompanying drawings.

[0023] like Figure 1As shown, this invention provides a method for modeling ultraviolet (UV) light channels in confined spaces at underground coal mine working faces. The method first acquires UV propagation measurement data at the underground coal mine working face, and simultaneously collects information on the three-dimensional structure of the roadway, equipment surface material, gas concentration, dust concentration, temperature and humidity, wind speed, and air turbulence, constructing a one-to-one correspondence dataset between propagation environment characteristics and channel measurement data. Then, the UV propagation measurement data undergoes system response removal processing to obtain the true channel response, and channel impulse response, path loss, delay spread, and light intensity fluctuation parameters are extracted. The CIR (Content-Induced Response) is then separated in the time domain to obtain line-of-sight (LOS) and non-line-of-sight (NLOS) components. Subsequently, a line-of-sight channel model associated with propagation distance, transmission direction, and reception direction is established based on the LOS components, and the model is further refined based on the NLOS components and the roadway... Based on the three-dimensional structural information of the equipment, macroscopic reflector clusters are mapped to actual underground objects, and microscopic scattering clusters are mapped to underground gases, dust, and water mist. Macroscopic object reflection channel models and microscopic particle scattering channel models are established respectively. Furthermore, a turbulent fading model is established by combining measured light intensity fluctuations, beam dispersion, and beam drift information, forming a confined space ultraviolet light propagation channel model for coal mine underground mining faces. Finally, the model output CIR is compared with the measured CIR in terms of total duration, energy distribution across time delay intervals, and root mean square time delay spread. The model parameters are iteratively updated based on the comparison results until the difference between the model output and the measured data is less than a preset threshold, resulting in a set of ultraviolet light channel model parameters suitable for coal mine underground mining faces. Figure 2 As shown.

[0024] I. Ultraviolet Light Propagation Measurement and Data Acquisition Methods: 1) Ultraviolet Light Propagation Data Acquisition Method: This invention first constructs an ultraviolet light channel measurement system suitable for the confined space of underground coal mine working faces. By setting different transceiver positions, transceiver orientations, and array layouts, it acquires ultraviolet light propagation measurement data in underground scenarios. To adapt to the characteristics of coexistence of line-of-sight and non-line-of-sight paths and complex propagation directions underground, multiple ultraviolet light sources and multiple photodetectors are used to form a transceiver array. Combined with a three-dimensional automatic pan-tilt unit and time-division multiple access control, measurements are performed on different transceiver unit combinations and different orientation combinations to obtain multiple sets of ultraviolet light propagation measurement snapshots.

[0025] 2) Simultaneous Environmental Feature Acquisition Method: This invention simultaneously acquires environmental feature information related to channel modeling while performing propagation measurements. Specifically, this includes: utilizing scene reconstruction technology to obtain three-dimensional structural information of macroscopic objects such as roadways, coal mining machines, and hydraulic supports; collecting surface material information of equipment and roadway walls; and simultaneously acquiring environmental parameters such as gas concentration, dust concentration, temperature and humidity, wind speed, and air disturbance. By establishing a correspondence between propagation measurement data and scene structural information, medium distribution information, and environmental parameter information, a data foundation for correlating underground measured CIR with environmental features is constructed.

[0026] 3) System Response Removal and True Channel Response Extraction Method: Since the measurement results simultaneously include the frequency response and transmission loss of the measurement system itself, this invention preprocesses the original measurement data to remove the overall response of the measurement platform, extracts the true channel frequency domain response, and obtains the true channel impulse response (CIR) through inverse transformation. Based on this, propagation characteristic parameters such as path loss, average delay, root mean square delay spread, and light intensity fluctuations are further extracted to provide experimental basis for subsequent modeling.

[0027] II. Methods for extracting propagation characteristic parameters and modeling sub-mechanisms, such as Figure 3 As shown: 1) Line-of-sight component extraction and line-of-sight channel modeling method: This invention distinguishes between line-of-sight components and non-line-of-sight components based on preprocessed real CIR. For the line-of-sight component, a line-of-sight channel model related to propagation distance, transmitter orientation, and receiver orientation is established to characterize the propagation characteristics of the line-of-sight path in the confined space of the underground mining face. This process utilizes the time-domain characteristics of the measured CIR to extract the line-of-sight component and uses parameters such as path loss to constrain the line-of-sight model.

[0028] 2) Macroscopic object reflection channel modeling method: In view of the reflection effect of macroscopic objects such as underground roadway walls, coal mining machines, and hydraulic supports on ultraviolet light propagation, this invention is based on synchronously acquired scene three-dimensional structure information and surface material information to correspond actual underground objects with macroscopic reflectors, and establishes a macroscopic object reflection channel model according to reflectivity, surface roughness and spatial position relationship to characterize the contribution of macroscopic reflection path to total CIR.

[0029] 3) Microscopic Particle Scattering Channel Modeling Method: Addressing the scattering effect of microscopic media such as downhole gas molecules, dust, and water mist on ultraviolet light propagation, this invention establishes a microscopic particle scattering channel model based on the medium distribution and its absorption and scattering characteristics. By mapping microscopic scattering clusters to the actual medium distribution and combining environmental information such as gas concentration and dust concentration, the time delay and energy distribution characteristics of the microscopic scattering path are characterized, thereby obtaining the influence of the scattering components on the total CIR.

[0030] 4) Turbulent Fading Modeling Method: Addressing the stochastic impact of underground airflow disturbances on ultraviolet light propagation, this invention establishes a turbulent fading model based on measured characteristics of received signal intensity fluctuations, beam dispersion, and beam drift. This model characterizes the stochastic modulation effect of air turbulence on the ultraviolet light propagation process. Combined with line-of-sight, reflection, and scattering models, this model describes the propagation characteristics under dynamic conditions within the confined space of underground mining faces.

[0031] III. Overall Channel Construction and Parameter Calibration Methods: 1) Unified Construction Method for Total Channel Impulse Response: After constructing the line-of-sight channel model, the macroscopic object reflection channel model, and the microscopic particle scattering channel model, this invention unifies the expression of the channel components of each of the above sub-mechanisms to form the total channel impulse response under turbulence-free conditions. Based on this, and combined with the turbulence fading model, the model output CIR considering the influence of turbulence is obtained. Through this unified construction method, a comprehensive characterization of multiple mechanisms such as line-of-sight, reflection, scattering, and turbulence in downhole ultraviolet light propagation is achieved.

[0032] 2) Iterative Calibration Method for Model Parameters: To improve the consistency between the model and the actual underground propagation environment, this invention compares the model output CIR with the measured CIR, and iteratively updates the reflection, scattering, and turbulence parameters based on the differences between the two in terms of total duration, energy distribution in different time delay intervals, and time delay spread. Through this parameter calibration process, the model output gradually approximates the measured results, thereby obtaining a set of ultraviolet light channel model parameters suitable for the confined space of underground coal mine working faces.

[0033] 3) Model Adaptability Verification Method: After parameter calibration, this invention verifies the model using measured data under different transmit / receive positions, orientation combinations, and environmental conditions to test the model's adaptability to different downhole propagation scenarios. By comparing the consistency between the model output and the measured results, the effectiveness and applicability of the modeling method are further confirmed.

[0034] The specific steps are as follows: Step 1: Acquisition of ultraviolet light propagation measurement data and environmental information: First, an ultraviolet (UV) light channel measurement platform for use in underground coal mines was constructed. The platform includes a general-purpose software-defined radio (USRP), an intrinsically safe portable computer, a UV light source drive circuit, a high-speed antenna switching unit, a UV light transmitting array (as a light source array), a UV light receiving array (as a photodetector array), a three-dimensional automatic pan-tilt unit, and a rubidium atomic clock. A MIMO system is constructed using multiple UV light sources and multiple photodetectors. The three-dimensional automatic pan-tilt unit adjusts the three-dimensional orientation of each light source and photodetector, sampling the spatial angles of the transmitting and receiving orientations according to a preset angle combination sequence to ensure sufficient acquisition of UV light signals from different propagation paths. The rubidium atomic clock serves as the clock and frequency reference for both the transmitting and receiving ends, addressing synchronization issues caused by difficulties in underground cable laying. The intrinsically safe portable computer acts as the control terminal and data storage unit, responsible for monitoring and adjusting the operating status of each unit in the system. The platform meets the explosion-proof requirements of underground coal mines, and the photodetector is preferably an avalanche photodiode (APD). Key parameters for downhole measurements were determined through testing, including the ultraviolet light band to be measured, the power of the transmitted signal, the type and length of the pseudo-random sequence of the measurement signal, and the type, number, and geometric layout of the transmitting and receiving arrays.

[0035] During underground measurements, given the complex environment of coal mine working faces and the coexistence of line-of-sight and non-line-of-sight paths, the three-dimensional structural information and texture features of macroscopic objects such as roadways, coal mining machines, and hydraulic supports are simultaneously collected. Their surface material information is also gathered, along with data on gas concentration, dust concentration, temperature and humidity, wind speed, and air turbulence. To this end, sensors deployed underground collect environmental parameters such as gas and dust concentrations. LiDAR SLAM technology is used to acquire the three-dimensional structural and texture features of macroscopic objects such as roadways, coal mining machines, and hydraulic supports. Furthermore, SLAM technology is used to measure air turbulence around large equipment such as ventilation openings, hydraulic supports, and coal mining machines, thereby constructing a measurement scene and environmental information dataset.

[0036] In this embodiment, the device environment feature vector used in step two is written as: , in, Indicates the sending and receiving distance. Indicates the orientation parameter of the transmitter. This indicates the orientation parameter of the receiving end. Indicates the gas concentration parameter, This represents the dust concentration parameter. Indicates ambient temperature. Indicates ambient humidity. Indicates wind speed. Represents the turbulence intensity parameter. This represents the surface reflectivity parameter. This represents the surface roughness parameter. Using the aforementioned environmental feature vectors, each ultraviolet light propagation measurement result is correlated with its corresponding propagation environment, forming a one-to-one correspondence dataset between propagation environment features and channel measurement data.

[0037] Step 2: System response removal, true CIR extraction, and basic parameter calculation. The specific implementation steps are as follows: After acquiring the raw measurement data, the ultraviolet light propagation measurement data undergoes system response removal processing. For the i-th transmitting unit to the j-th receiving unit, the true channel frequency domain response of the corresponding link is expressed as: , in, Indicates the received signal spectrum. Indicates the spectrum of the transmitted reference signal. This represents the integrated system frequency response of the corresponding transmit / receive link. The true channel impulse response is obtained by inverse Fourier transform of the true channel frequency domain response, i.e.: , in, This represents the actual channel impulse response. This represents the inverse Fourier transform. Through this process, the overall response of the ultraviolet channel measurement platform can be removed, yielding the true channel response used for modeling.

[0038] The path loss, average delay, and root mean square delay spread are further obtained from the actual channel impulse response, as follows: , , , in, Indicates path loss. For average delay, For root mean square delay spread, This represents the nth time delay sampling point. This corresponds to the power value at the sampling point with the time delay. In addition to the above parameters, the light intensity fluctuation parameters can be further extracted from the received signal envelope statistics, providing experimental basis for subsequent turbulence modeling.

[0039] To ensure that the above-mentioned real channel response can be obtained under different array element combinations and different orientation combinations, different transceiver array element combinations are activated in a time-division multiple access manner through a high-speed antenna switching unit. Only one transceiver array element combination is activated in a single time slot, and multiple time slots constitute a measurement frame. In each measurement frame, the orientation combination of the transceiver array elements is kept fixed and all transceiver array element activation methods are traversed. Multiple measurement frames then traverse all transceiver array element orientation combinations to obtain channel measurement snapshots. Multiple sets of channel measurement snapshots are obtained by repeating the above process.

[0040] After obtaining the true CIR, it is subjected to time-domain separation to obtain the LOS and NLOS components. That is, the measured... Windowing and segmentation are performed to extract the line-of-sight (LOS) component corresponding to the direct path, and the remaining multipath components are merged into non-line-of-sight (NLOS) components. The LOS component is used to subsequently establish the line-of-sight channel model, and the NLOS component is used to subsequently establish the macroscopic reflection channel model and the microscopic particle scattering channel model.

[0041] Step 3: Construct the sub-mechanism channel model. The specific implementation steps are as follows: Unified characterization of total channel impulse response To provide a unified characterization of line-of-sight propagation, macroscopic reflection propagation, microscopic scattering propagation, and turbulent disturbances in the confined space of an underground coal mine working face, the total channel impulse response from the i-th transmitting unit to the j-th receiving unit is expressed as: , in, Indicates the turbulence decay factor. This represents the impulse response of the line-of-sight path channel. This represents the impulse response of a macroscopic object's reflection path channel. This represents the impulse response of the microscopic particle scattering path channel.

[0042] Line-of-sight channel model construction A line-of-sight (LOS) channel model is established based on the LOS components, relating it to propagation distance, transmission direction, and reception direction. The LOS channel model satisfies: , in, Indicates the line-of-sight path amplitude gain. Represents the Dirac impulse function. Indicates the propagation delay along the line-of-sight path; Furthermore, the line-of-sight path amplitude gain and propagation delay respectively satisfy: , , in, Indicates the line-of-sight path scaling factor. This represents the Lambertian order of the i-th transmitting unit. This represents the effective receiving area of ​​the j-th receiving unit. This represents the propagation distance from the i-th transmitting unit to the j-th receiving unit. Indicates the emission irradiance angle. Indicates the angle of incidence received. Indicates the total extinction coefficient. This represents the indicator function under the receiving field of view limitation. Let represent the receiving field of view of the j-th receiving unit, and c represent the speed of light.

[0043] Construction of macroscopic object reflection channel model Based on the NLOS components and combined with the three-dimensional structural information of the roadway and equipment, the macroscopic reflector clusters are mapped to actual underground objects. Reflection path parameters are determined according to the reflectivity, surface roughness, and spatial positional relationships of the reflecting surfaces, thereby establishing a macroscopic object reflection channel model. Specifically, based on the three-dimensional structural information and surface material information of macroscopic objects such as roadways, coal mining machines, and hydraulic supports acquired synchronously in steps 1 and 2, actual underground macroscopic objects are mapped to macroscopic reflector clusters. The amplitude gain and propagation delay of each reflection path are determined according to the reflectivity, surface roughness, and spatial positional relationships of the reflecting surfaces to characterize the contribution of macroscopic object reflection propagation to the channel impulse response.

[0044] Let the total number of resolvable reflection paths be Then, the macroscopic object reflection path channel impulse response from the i-th light source to the j-th photodetector can be expressed as: , in, Indicates the reflection path number. Indicates the first Amplitude gain of each reflection path Indicates the first The propagation delay of each reflection path.

[0045] Furthermore, the first The amplitude gain and propagation delay of each reflection path satisfy the following: , , in, Indicates the reflection path scaling factor. Indicates the first The reflectivity of the reflecting surface corresponds to each reflection path. Indicates the proportion of specular reflection component. Indicates the angle of incidence. Indicates the deviation angle relative to the mirror surface. Indicates the specular reflectance index. This represents the distance from the transmitting unit to the reflecting point. This represents the distance from the reflection point to the receiving unit. Using this model, the three-dimensional structure, surface reflectivity, and surface roughness of macroscopic objects such as tunnel walls, coal mining machines, and hydraulic supports can be incorporated into the reflection propagation modeling process.

[0046] Construction of microscopic particle scattering channel model After establishing the macroscopic object reflection channel model, the distribution of microscopic scattering particles formed by downhole gas molecules, dust, and water mist is further used to map the microscopic scattering clusters to the actual medium distribution, and a microscopic particle scattering channel model is established based on the medium's absorption and scattering characteristics. The microscopic particle scattering channel model satisfies the following: , in, Indicates the effective scattering volume. Indicates the scattering path scaling factor. Represents the equivalent scattering coefficient. Represents the scattering phase function. Indicates the scattering angle. This represents the distance from the emitting element to the scattering element. This represents the distance from the scattering element to the receiving element. This represents a volume element.

[0047] Furthermore, the total extinction coefficient satisfies: , in, Indicates the gas absorption coefficient. Represents the gas scattering coefficient. Indicates the dust absorption coefficient. This represents the dust scattering coefficient.

[0048] Furthermore, the dust scattering coefficient and the dust absorption coefficient respectively satisfy: , , in, The wavelength represents the operating wavelength, and r represents the radius of the dust particle. and These represent the minimum and maximum radii of dust particles, respectively. This represents the scattering efficiency factor. This represents the absorption efficiency factor. The complex refractive index represents the dust particle's refractive index. This represents the particle size distribution function of dust.

[0049] After obtaining the microscopic particle scattering channel model, the line-of-sight path channel impulse response, the macroscopic object reflection path channel impulse response, and the microscopic particle scattering path channel impulse response are superimposed to obtain the total channel impulse response under turbulence-free conditions. Based on this, the following turbulence fading factor is combined to form the model output CIR for comparison with the measured CIR in step 4.

[0050] Construction of turbulent decay model After obtaining the total channel impulse response under turbulence-free conditions, a turbulent fading model was established based on the measured intensity fluctuations, beam dispersion, and beam drift information of the received signal to further characterize the random disturbance effect of downhole air turbulence on the ultraviolet light propagation process. The turbulent fading model satisfies: , , in, This represents a random process with fluctuating logarithmic amplitude. This represents the variance of the random process. This indicates that the mean is 0 and the variance is 0. The Gaussian distribution.

[0051] Furthermore, in order to incorporate the measured statistical characteristics of turbulence into the turbulence fading model, the variance of the stochastic process satisfies: , Among them, among them, The scintillation index is defined as follows. Furthermore, to characterize turbulence intensity using received light intensity statistics, the scintillation index satisfies the following: , in, Let represent the random variable of received optical intensity in the link from the i-th transmitting unit to the j-th receiving unit.

[0052] After establishing the turbulent fading model, it is applied to the total channel impulse response under turbulent conditions to obtain the model output CIR considering the turbulence effect. The model output CIR is then used as the input for comparison and calibration with the measured CIR in step S4.

[0053] Step 4: Compare the model output CIR with the measured CIR and calibrate the parameters. The specific implementation steps are as follows: After obtaining the model output CIR considering turbulence effects, a parameter update model is constructed to progressively approximate the measured CIR, with the difference between the measured CIR and the model output CIR as the objective. The model parameter update satisfies: , in, This represents the vector of model parameters to be optimized. This represents the optimized model parameter vector. This describes the process of finding the parameters that minimize the objective function. This represents the measured channel impulse response value. This represents the channel impulse response value output by the model. This represents the weight coefficient for the nth time-delay sampling point. The model parameter vector... This includes parameters such as reflectivity of the reflecting surface, surface roughness, reflection order, scattering order, scatterer distribution, and turbulence intensity.

[0054] Furthermore, during the parameter update process, the model output CIR is compared with the measured CIR in three aspects: total duration, energy distribution in time-delay intervals, and root mean square delay spread. The model parameters are iteratively updated based on the comparison results until the difference between the model output and the measured CIR is less than a preset threshold. Specifically, the model output CIR and the measured CIR are first compared to see if their total durations are consistent; then the energy distribution after time-delay partitioning is compared to see if they match; finally, the root mean square delay spread of the two is compared to see if they are close. If there are still significant deviations among the three, adjustments are continued. The reflectivity parameters, surface roughness parameters, reflection order parameters, scattering order parameters, scatterer distribution parameters, and turbulence intensity parameters of the reflecting surface are calculated, and the model output CIR is recalculated until the predetermined accuracy requirement is met, thereby obtaining a set of ultraviolet light channel model parameters suitable for the confined space of underground coal mine working faces.

[0055] After completing the above parameter calibration, the model output CIR and measured CIR can be repeatedly compared under different transmit / receive positions, different transmit / receive orientations, and different array layouts to verify the adaptability of the established model to different underground propagation scenarios. When the model maintains good consistency under multiple sets of measurement conditions, it can be considered that the ultraviolet light channel model for underground coal mine working faces established in this invention has good engineering application value and can be used for channel analysis, link design, and performance evaluation of underground ultraviolet light communication systems in coal mines.

[0056] In summary, this invention, based on measured CIR and environmental characteristic information from underground mining operations, achieves sub-mechanism modeling and parameter calibration of the ultraviolet light propagation channel in underground coal mine working faces through steps such as system response removal, true CIR extraction, LOS and NLOS separation, line-of-sight channel model construction, macroscopic object reflection channel model construction, microscopic particle scattering channel model construction, turbulence fading model construction, and parameter iterative calibration. The above embodiments are merely preferred embodiments of this invention and are not intended to limit the scope of protection of this invention; all equivalent substitutions, improvements, or modifications made within the spirit and principles of this invention should be included within the scope of protection of this invention.

[0057] The effects of this invention: This invention establishes an ultraviolet (UV) light channel modeling method based on measured underground channel impulse response and environmental characteristics. By acquiring UV light propagation measurement data under different transmit / receive positions, orientations, and array layouts, and simultaneously collecting environmental information such as the three-dimensional structure of the tunnel, equipment surface materials, gas concentration, dust concentration, temperature and humidity, wind speed, and air disturbance, and after system response removal and preprocessing, the true channel response and related characteristic parameters corresponding to the propagation environment can be obtained, providing a data foundation for subsequent channel modeling.

[0058] This invention establishes a separate channel model for ultraviolet light propagation in the confined space of underground coal mine working faces. By distinguishing between line-of-sight and non-line-of-sight components in measured CIR, line-of-sight channel models, macroscopic object reflection channel models, microscopic particle scattering channel models, and turbulent fading models are established respectively. Each type of component is then uniformly characterized to form the total channel impulse response, thereby providing a relatively complete description of the ultraviolet light propagation characteristics under the combined effects of line-of-sight propagation, reflection propagation, scattering propagation, and air turbulence disturbances in the confined space underground.

[0059] This invention establishes a parameter calibration mechanism that combines the model output CIR with the measured CIR. By comparing the model output with the measured results and iteratively updating the reflection, scattering, and turbulence parameters based on the comparison results, the established channel model can gradually approximate the actual propagation process underground. This yields a parameter set for the ultraviolet channel model suitable for the confined space of underground coal mine working faces, which is beneficial for improving the model's scene adaptability and result reliability.

[0060] The above description is merely one embodiment of the present invention and is not intended to limit the present invention. Any minor modifications, equivalent substitutions, and improvements made to the above embodiment based on the technical essence of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for predicting the ultraviolet light channel in a confined space at an underground coal mine working face, characterized in that, The specific steps are as follows: S1. Obtain ultraviolet light channel propagation measurement data at the underground mining face of a coal mine, and simultaneously collect information in the roadway; S2. Obtain the true channel frequency domain response from the ultraviolet light propagation measurement data, perform time-domain separation on the channel response CIR, and obtain the line-of-sight (LOS) component and the non-line-of-sight (NLOS) component. S3. Construct an ultraviolet light propagation channel model in a confined space underground in a coal mine using LOS and NLOS components; S4. Compare the CIR predicted by the ultraviolet propagation channel model with the measured CIR, and iterate until it meets the preset requirements. Use the ultraviolet channel model that has completed the iteration to realize the prediction of the ultraviolet channel in the working plane.

2. The method for predicting the ultraviolet light channel in a confined space at a coal mine underground mining face according to claim 1, characterized in that, The information within the tunnel includes the three-dimensional structural information of the tunnel and equipment, the surface material information of the equipment, gas concentration, dust concentration, temperature and humidity, wind speed and air turbulence information; a one-to-one corresponding associated dataset is constructed by using ultraviolet light propagation channel measurement data and tunnel information to provide environmental constraints for the time-domain separation of channel response CIR, the determination of ultraviolet light propagation channel model parameters, and the iterative calibration of model parameters.

3. The method for predicting the ultraviolet light channel in a confined space at an underground coal mine working face according to claim 1, characterized in that, The ultraviolet light propagation measurement data is processed by removing the system response to obtain the true channel frequency domain response. The true channel frequency domain response is labeled and constrained by the transmit and receive distance, transmit and receive orientation and environmental parameters in the associated dataset. The channel impulse response, path loss, time delay spread and light intensity fluctuation parameters are extracted. The true channel frequency domain response is separated in the time domain to obtain the line-of-sight (LOS) component and the non-line-of-sight (NLOS) component. The environmental feature vector contained in the ultraviolet light propagation measurement data is: ， in, Indicates the sending and receiving distance. Indicates the orientation parameter of the transmitter. This indicates the orientation parameter of the receiving end. Indicates the gas concentration parameter, This represents the dust concentration parameter. Indicates ambient temperature. Indicates ambient humidity. Indicates wind speed. Represents the turbulence intensity parameter. This represents the surface reflectivity parameter. This represents the surface roughness parameter; Let the true channel frequency domain response from the i-th transmitting unit to the j-th receiving unit be defined in the ultraviolet light propagation measurement. Represented as: ， in, Indicates the received signal spectrum. Indicates the spectrum of the transmitted reference signal. This represents the overall system frequency response of the corresponding transceiver link; The true channel impulse response is obtained by inverse Fourier transform of the true channel frequency domain response, i.e.: ， in, This represents the actual channel impulse response. Indicates the inverse Fourier transform; The path loss is calculated from the actual channel impulse response using the following formula. : ， The actual channel impulse response is calculated using the following formula. Average latency of the corresponding link Root mean square delay spread : ， ， in, This represents the nth time delay sampling point. This represents the power value at the corresponding time delay sampling point.

4. The method for predicting the ultraviolet light channel in a confined space at an underground coal mine working face according to claim 3, characterized in that, Based on the LOS components, a line-of-sight channel model is established that is associated with the propagation distance, transmission direction, and reception direction. Based on the NLOS components and combined with the three-dimensional structural information of the roadway and equipment in the associated dataset, macroscopic reflector clusters are mapped to actual underground objects, and microscopic scatterer clusters are mapped to underground gas, dust, and water mist. Macroscopic object reflection channel models and microscopic particle scattering channel models are established respectively. Furthermore, a turbulent fading model is established by combining measured light intensity fluctuations, beam dispersion, and beam drift information, thus forming an ultraviolet light propagation channel model in the confined space of a coal mine.

5. The method for predicting the ultraviolet light channel in a confined space at a coal mine underground mining face according to claim 4, characterized in that, The line-of-sight channel model is represented as follows: ， ， ， in, This represents the impulse response of the line-of-sight path channel. Indicates the line-of-sight path amplitude gain. Represents the Dirac impulse function. Indicates the propagation delay along the line-of-sight path; Indicates the line-of-sight path scaling factor. This represents the Lambertian order of the i-th transmitting unit. This represents the effective receiving area of ​​the j-th receiving unit. This represents the propagation distance from the i-th transmitting unit to the j-th receiving unit. Indicates the emission irradiance angle. Indicates the angle of incidence received. Indicates the total extinction coefficient. This represents the indicator function under the receiving field of view limitation. Let represent the receiving field of view of the j-th receiving unit, and c represent the speed of light.

6. The method for predicting the ultraviolet light channel in a confined space at a coal mine underground mining face according to claim 5, characterized in that, The macroscopic object reflection path channel impulse response Represented as: ， ， ， in, This indicates the total number of resolvable reflection paths. Indicates the reflection path number. Indicates the first Amplitude gain of each reflection path Indicates the first The propagation delay of each reflection path; Indicates the reflection path scaling factor. Indicates the first The reflectivity of the reflecting surface corresponds to each reflection path. Indicates the proportion of specular reflection component. Indicates the angle of incidence. Indicates the deviation angle relative to the mirror surface. Indicates the specular reflectance index. This represents the distance from the transmitting unit to the reflecting point. This indicates the distance from the reflection point to the receiving unit.

7. The method for modeling the ultraviolet light channel in a confined space at an underground coal mine working face according to claim 6, characterized in that, The microscopic particle scattering path channel impulse response Represented as: ， ， in, Indicates the effective scattering volume. Indicates the scattering path scaling factor. Represents the equivalent scattering coefficient. Represents the scattering phase function. Indicates the scattering angle. This represents the distance from the emitting element to the scattering element. This represents the distance from the scattering element to the receiving element. Represents a volume element; Indicates the total extinction coefficient. Indicates the gas absorption coefficient. Represents the gas scattering coefficient. Indicates the dust absorption coefficient. Indicates the dust scattering coefficient; Working step Dust scattering coefficient at time With dust absorption coefficient They respectively satisfy: ， ， in, The wavelength represents the operating wavelength, and r represents the radius of the dust particle. and These represent the minimum and maximum radii of dust particles, respectively. This represents the scattering efficiency factor. This represents the absorption efficiency factor. The complex refractive index represents the dust particle's refractive index. This represents the particle size distribution function of dust.

8. The method for modeling the ultraviolet light channel in a confined space at an underground coal mine working face according to claim 7, characterized in that, Line-of-sight path channel impulse response Macroscopic object reflection path channel impulse response Impact response of the microscopic particle scattering path channel Superposition yields the total channel impulse response under turbulence-free conditions. : ， To further characterize the random disturbance effect of downhole air turbulence on the ultraviolet light propagation process, the turbulence fading factor was calculated based on the measured intensity fluctuations, beam dispersion, and beam drift information of the received signal. : ， ， in, This represents a random process with fluctuating logarithmic amplitude. This indicates that the mean is 0 and the variance is 0. Gaussian distribution; Introducing measured statistical characteristics of turbulence into the turbulence fading factor, the variance of the random process satisfies: ， in, The flicker index represents the flickering index, which satisfies the following: ， in, Let represent the random variable of received optical intensity in the link from the i-th transmitting unit to the j-th receiving unit; Combining the turbulence fading factor, the CIR predicted by the ultraviolet light propagation channel model is formed, representing the total channel impulse response from the i-th transmitting unit to the j-th receiving unit. Represented as: 。 9. The method for modeling the ultraviolet light channel in a confined space at an underground coal mine working face according to claim 8, characterized in that, The CIR predicted by the ultraviolet light propagation channel model is compared with the measured CIR in terms of total duration, energy distribution in different time delay intervals, and time delay spread parameters. The reflection parameters, scattering parameters, and turbulence parameters are iteratively updated based on the comparison results until the error between the CIR predicted by the ultraviolet light propagation channel model and the measured CIR is less than a preset threshold. Construct a parameter update model that targets the difference between the measured CIR and the model output CIR: ， in, This represents the parameter vector of the ultraviolet light propagation channel model to be optimized. This represents the parameter vector of the optimized ultraviolet light propagation channel model. This describes the process of finding the parameters that minimize the objective function. This represents the measured channel impulse response value. This represents the channel impulse response value output by the ultraviolet light propagation channel model. Represents the weighting coefficient of the nth time-delay sampling point; ultraviolet light propagation channel model parameter vector. This includes parameters such as reflectivity of the reflecting surface, surface roughness, reflection order, scattering order, scatterer distribution, and turbulence intensity. During the parameter update process, the CIR output of the ultraviolet light propagation channel model is compared with the measured CIR in three aspects: total duration, energy distribution in different time delay intervals, and root mean square time delay spread. Based on the comparison results, the reflection order, scattering order, and scatterer distribution parameters are iteratively updated until the difference between the model output and the measured CIR is less than a preset threshold.

10. A computer device, characterized in that, It includes a processor and a memory, the processor being electrically connected to the memory, the memory being used to store instructions and data, and the processor being used to execute the ultraviolet light channel modeling method for confined space in underground coal mine working faces as described in any one of claims 1-9.

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