Underground data wireless transmission system based on Internet of Things technology

Abstract

The present invention discloses an underground data wireless transmission system based on Internet of Things technology, which relates to the technical field of wireless signal transmission control. The system comprehensively monitors and analyzes the different channel performance data of LoRa, Wi-Fi, 5G, Bluetooth, and NB-IoT through a channel monitoring module, facilitating the switch to a channel with qualified quality. It combines lidar technology to scan the underground environment and establish a three-dimensional model, identify the impact of underground obstacles and changes in their moisture content on signal propagation, and generate targeted optimization strategies, such as adjusting the signal transmission power and antenna angle, to address environmental changes and signal attenuation problems. Further, through the analysis of underground multipath effects, after receiving the second warning instruction, the system can real-time identify the reflection surface and refraction surface in signal propagation, analyze the impact of multipath effects on the signal, and generate optimization solutions, such as measures like arranging relay devices and adjusting the antenna beam, significantly reducing interference and improving the transmission quality of wireless signals.

Description

Technical Field

[0001] The present invention relates to the technical field of wireless signal transmission control, and in particular to an underground data wireless transmission system based on Internet of Things technology. Background Art

[0002] With the rapid development of Internet of Things technology, more and more application scenarios have begun to rely on wireless communication technology for data transmission. Especially in underground operations, mining and other environments, due to the complex geographical structure, obstacles and harsh environmental conditions underground, traditional wireless communication technology has many problems in signal transmission and quality assurance. These problems not only affect the stability and reliability of data transmission, but also limit the efficiency and safety of underground operations to a certain extent. Therefore, how to improve the performance of underground wireless channels and enhance the reliability and stability of data transmission has become a key technical problem that needs to be solved in the field of wireless communications.

[0003] Traditional wireless communication technologies (such as LoRa, Wi-Fi, NB-IoT, etc.) usually do not perform well in such complex environments. Although these technologies perform well in open areas, once they enter underground or rocky areas, the signal attenuation increases significantly, resulting in a decrease in communication quality.

[0004] The most common obstacles in underground environments include rocks, metal pipes, concrete, etc. These obstacles will not only cause attenuation and distortion of wireless signal propagation, but may also cause signal reflection and refraction, further exacerbating the decline in signal quality. Natural materials such as rocks and soil have a high dielectric constant, especially in a humid environment, and their attenuation effect on wireless signals is more obvious. For example, wet materials such as mudstone, granite, and shale have a high attenuation factor, and their signal attenuation is much more serious than when dry. In addition, obstacles such as metal pipes and steel components, due to their high dielectric constant and reflectivity, will cause strong reflection and refraction of wireless signals when passing through, resulting in serious multipath effects, thereby affecting the transmission quality of wireless signals. Summary of the invention

[0005] In view of the deficiencies of the prior art, the present invention provides an underground data wireless transmission system based on the Internet of Things technology to solve the problems mentioned in the background technology.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: A downhole data wireless transmission system based on Internet of Things technology, comprising:

[0007] Channel monitoring module, which is used to monitor the transmission performance of several wireless channels underground in real time and establish a channel performance data set; wireless channels include LoRa, Wi-Fi, 5G, Bluetooth and NB-IoT;

[0008] A channel quality analysis module, which is used to establish and train a channel prediction model, analyze based on a channel performance dataset, so as to obtain the channel quality coefficient of the i-th wireless channel , and a quality threshold X is preset. If the channel quality coefficient of the i-th wireless channel is lower than the quality threshold, a first warning instruction is triggered;

[0009] A first channel interference analysis module, which is used to, after receiving the first warning instruction, adopt the laser scanning technology of lidar to scan the underground environment structure, establish a three-dimensional underground model, and identify obstacles, so as to construct the underground environment propagation obstacle coefficient of the i-th wireless channel , and a first influence threshold is preset , if , it indicates that the signal propagation path loss influence of this wireless channel is abnormal, a first strategy is generated, and after the first strategy is implemented, if the channel quality coefficient of the i-th wireless channel is still unqualified, a second warning instruction is triggered;

[0010] A second channel interference analysis module, which is used to, after receiving the second warning instruction, identify the reflection surface and refraction surface of the obstacle according to the three-dimensional underground model, so as to construct the multipath effect factor of the i-th wireless channel , and a second influence threshold is preset , when , a second strategy is generated and implemented.

[0011] Preferably, the channel monitoring module includes a first deployment unit and a first monitoring unit;

[0012] The first deployment unit is used to deploy and install several wireless communication devices in the underground or above-ground environment. The wireless communication devices include LoRa gateways, Wi-Fi routers, Bluetooth devices, NB-IoT base station devices, and 5G base station devices;

[0013] The first monitoring unit is used to collect the transmission performance data of the receiving end of each wireless channel by using a spectrum analyzer, a wireless network analyzer, a modulation analyzer, and a noise analyzer, and establish a channel performance dataset; the channel performance dataset includes: the bandwidth of the i-th wireless channel , signal-to-noise ratio , modulation error rate , noise component , and frequency utilization rate .

[0014] Preferably, the channel quality analysis module includes a channel prediction model establishment unit and a channel analysis unit;

[0015] The channel prediction model establishment unit is used to clean and normalize the channel performance data set, and convert the data into the interval [0,1];

[0016] And use the convolutional neural network CNN technology to establish a channel prediction model. After dividing the normalized channel performance data set and the second environment data set into an 80% training set and a 20% test set, the channel prediction model is trained and tested, and then the trained channel prediction model is exported;

[0017] The channel analysis unit is used to adopt the trained channel prediction model to extract the bandwidth of the i-th wireless channel in the channel performance data set And the signal-to-noise ratio , calculate and obtain the channel capacity of the i-th wireless channel :

[0018] ;

[0019] And extract the modulation error rate of the i-th wireless channel in the channel performance data set , noise component , frequency utilization rate , combined with the channel capacity of the i-th wireless channel , after dimensionless processing, calculate and obtain the channel quality coefficient of the i-th wireless channel through the following formula : ;

[0020] In the formula, respectively represent the modulation error rate of the i-th wireless channel , noise component , frequency utilization rate weight coefficients, the modulation error rate of the i-th wireless channel , noise component and frequency utilization rate values are all inversely proportional, the lower the better; the channel capacity of the i-th wireless channel is directly proportional, the larger the better.

[0021] Preferably, the channel quality analysis module further includes a first warning unit, and the first warning unit is used to preset a quality threshold X, and compare the channel quality coefficient of the i-th wireless channel with the quality threshold X to obtain a first evaluation result, including:

[0022] If , it means that the channel quality of the i-th wireless channel is qualified, and extract the maximum value of the channel quality coefficient of the i-th wireless channel among the qualified channel qualities, and preferentially switch this channel as the current wireless communication channel; ;

[0023] If , it indicates that the channel quality of the i-th wireless channel is unqualified. If the channel quality of all current wireless channels is unqualified, a first warning instruction is issued outward.

[0024] Preferably, the first channel interference analysis module includes a three-dimensional modeling unit, a first recognition unit, and an Internet of Things sensing and acquisition unit;

[0025] The three-dimensional modeling unit is used to, after receiving the first warning instruction, adopt the laser scanning technology of lidar to scan the underground environment structure, obtain the underground point cloud data, and perform denoising, filtering, and fitting processing on the underground point cloud data and then conduct three-dimensional modeling to obtain an underground three-dimensional model;

[0026] The first recognition unit is used to identify obstacles in the underground environment based on the underground three-dimensional model. The obstacles include dry soil, mudstone, granite, metal pipes, concrete, glass, wood, silica, and shale; the metal pipes include steel and aluminum materials;

[0027] The Internet of Things sensing and acquisition unit is used to adopt a humidity sensor in the Internet of Things technology to monitor the moisture content S of the underground obstacles and establish an influence data set.

[0028] Preferably, the first channel interference analysis module further includes an obstacle obstruction calculation unit and a signal loss evaluation unit;

[0029] The obstacle obstruction calculation unit is used to, after marking the obstacles in the underground environment and extracting the moisture content S of the obstacles in the influence data set, calculate the relative permittivity of the obstacles : ;

[0030] In the formula, is the absolute permittivity of the obstacle in the dry state, including: dry soil: ϵ = 3 - 5; mudstone: ϵ = 3 - 10; granite: ϵ = 4 - 6; steel: ϵ = 1.5 - 3; concrete: ϵ = 4 - 7; glass: ϵ = 4 - 10; wood: ϵ = 2 - 3; silica: ϵ = 4 - 5; shale: ϵ = 8 - 15; is the permittivity of free space, set to: 8.854×10 −12  F / m; represents the humidity sensitivity coefficient, obtained through experiments, which describes the degree of influence of humidity change on the permittivity of materials; when the moisture content S of the obstacle increases, its permittivity will increase accordingly, and the propagation of wireless signals will be more attenuated, resulting in a decrease in the strength of wireless signals;

[0031] And based on the relative permittivity of the obstacle , combined with the physical thickness of the k-th obstacle , the propagation obstacle coefficient of the \(i\)-th wireless channel is calculated through the following formula ;

[0032] ;

[0033] In the formula, \(k\) represents the label of the obstacle, \(m\) represents the total number of obstacles from the signal transmitting end to the receiving end of the \(i\)-th wireless channel, represents the attenuation factor of the \(k\)-th obstacle, including: the attenuation factor of granite \(\beta = 0.5 - 1.5\ dB / m\); the attenuation factor of shale \(\beta = 3 - 4\ dB / m\); the attenuation factor of mudstone \(\beta = 2 - 5\ dB / m\); the attenuation factor of sandstone \(\beta = 1 - 2\ dB / m\); the attenuation factor of steel \(\beta = 20 - 40\ dB / m\); the attenuation factor of aluminum \(\beta = 15 - 25\ dB / m\); the attenuation factor of concrete \(\beta = 2 - 4\ dB / m\);

[0034] In the formula, represents the physical thickness of the \(k\)-th obstacle, represents the propagation path length of the \(k\)-th obstacle; represents the path loss attenuation exponent, which describes the rate at which the signal attenuates with increasing distance and is obtained by actually measuring the signal strength at different distances to obtain the relationship between path loss and distance.

[0035] Preferably, the signal loss evaluation unit is used to preset a first influence threshold , and compare and evaluate the propagation obstacle coefficient of the \(i\)-th wireless channel with the first influence threshold to evaluate whether the signal propagation path loss of the obstacle to the \(i\)-th wireless channel exceeds the threshold influence, including:

[0036] If , it means that the signal propagation path loss influence of the \(i\)-th wireless channel is abnormal, and a first strategy is generated, including: adjusting the angle of the wireless signal antenna to the direction with the least obstacles, gradually increasing the adjustment to increase the signal transmission power of the current wireless channel by 1% - 3%, and continuously adjusting 2 - 3 times until ; monitor the channel quality coefficient of the \(i\)-th wireless channel again. If the channel quality coefficient of the \(i\)-th wireless channel is still unqualified, a second warning instruction is sent out;

[0037] If , it means that the signal propagation path loss influence of the \(i\)-th wireless channel is normal, and continuous monitoring is carried out.

[0038] Preferably, the second channel interference analysis module includes a second identification unit, a reflection coefficient calculation unit, and a refraction coefficient calculation unit;

[0039] The second recognition unit is used to recognize the reflecting surface and refracting surface of obstacles in the underground environment according to the three-dimensional underground model after the implementation of the first strategy and when receiving the second warning instruction; the reflecting surface and refracting surface of the obstacles include rock formations, support structures, smooth metal surfaces, concrete walls, liquid surfaces, and smooth reflective material surfaces;

[0040] The refractive index calculation unit is used to mark the reflecting surface of the obstacles in the underground environment and calculate the wave impedance Z of each obstacle. The formula is as follows: ;

[0041] In the formula, is the magnetic permeability of the obstacle, is the absolute dielectric constant of the obstacle in the dry state. Different wave impedances will affect the reflection or refraction of electromagnetic waves;

[0042] When the electromagnetic wave of the wireless signal propagates to the reflecting surface of the obstacle, part of the electromagnetic wave of the wireless signal will be reflected back, causing signal loss. The reflection coefficient of the kth obstacle is calculated by the following formula :

[0043] ;

[0044] In the formula, is the wave impedance of the incident medium, represents the wave impedance of the obstacle where the reflecting surface is located; , indicating total reflection, when, indicating no reflection and all electromagnetic waves passing through the obstacle;

[0045] The refractive index calculation unit is used to calculate the influence of the kth obstacle on the electromagnetic wave of the wireless signal based on the marked refracting surface of the obstacle in the underground environment; when the electromagnetic wave of the wireless signal propagates to the refracting surface of the obstacle, part of the electromagnetic wave of the wireless signal will be transmitted into the new medium and refracted, and continue to propagate along the new propagation path. The refractive index of the kth obstacle is calculated by the following formula : ;

[0046] In the formula, is the refractive medium, that is, the wave impedance after refraction and penetration into the new medium, the wave impedance of the incident medium, when, indicating total transmission and no energy loss, when, indicating complete inability to penetrate the obstacle.

[0047] Preferably, the second channel interference analysis module further includes a multipath effect analysis unit and a multipath effect evaluation unit;

[0048] The multipath effect analysis unit is used to calculate based on the reflection coefficient of the kth obstacle and the refraction coefficient of the k-th obstacle , to construct the multipath effect factor of the i-th wireless channel ;

[0049] Since the signal propagates to the receiving end through multiple paths and is affected differently by the direct path, reflection path, and refraction path, multipath effect is caused; the multipath effect will result in the signal received at the receiving end of the wireless channel being a combination of signals from multiple different paths. According to the reflection coefficient and the refraction coefficient of the k-th obstacle, the multipath effect factor of the i-th wireless channel is calculated by the following formula :

[0050] ;

[0051] In the formula, k represents the label of the obstacle, m represents the total number of obstacles from the signal transmitting end to the receiving end of the i-th wireless channel, represents the reflection coefficient of the k-th obstacle, represents the phase difference of the reflection path of the k-th obstacle, j is the imaginary unit representing the phase of the signal, represents the phase difference of the refraction path of the k-th obstacle, represents the propagation path length of the k-th obstacle.

[0052] Preferably, the multipath effect evaluation unit is used to preset a second influence threshold , and compare and evaluate the multipath effect factor of the i-th wireless channel with the second influence threshold to evaluate whether the multipath effect interference of the obstacle on the i-th wireless channel is abnormal, including:

[0053] Including:

[0054] When , it means that the multipath effect interference in the signal propagation path of the i-th wireless channel is abnormal, and a second strategy is generated, including: arranging a relay device every 7 - 10 meters according to the length of the underground channel, and installing the relay device at the corners of the underground channel and near the obstacles; and adjusting the antenna beam coverage range of the current wireless channel from 90° to 45° to 60° to concentrate the signal transmission on the straight path until until

[0055] When , it means that the multipath effect interference in the signal propagation path of the i-th wireless channel is normal. Gradually increase and adjust the signal transmission power of the current wireless channel by 4% - 6%, and continuously adjust it 2 - 3 times until until, and continuously monitor.

[0056] The present invention provides an underground data wireless transmission system based on Internet of Things technology, having the following beneficial effects:

[0057] (1) The channel monitoring module can monitor the transmission performance of several underground wireless channels in real time, collect data and establish a channel performance data set, providing an accurate data basis for subsequent channel quality analysis. By monitoring and analyzing different wireless channels such as LoRa, Wi-Fi, 5G, Bluetooth, and NB-IoT, the signal quality in the underground environment can be comprehensively evaluated, and signal attenuation or distortion problems can be detected in a timely manner, thus avoiding data transmission failures during underground operations.

[0058] (2) After receiving the first warning instruction, the first channel interference analysis module scans the underground environment by using the laser scanning technology of lidar, establishes a three-dimensional model of the underground, and identifies obstacles in the underground environment. Based on the dielectric constant and attenuation factor of the obstacles, the humidity sensor in the Internet of Things technology can monitor the moisture content of the underground obstacles in real time. The change in the moisture content of the obstacles will directly affect its relative dielectric constant, and thus affect the propagation effect of the wireless signal. By monitoring the real-time change in humidity, the system can obtain more accurate environmental data, providing a timely basis for channel interference analysis. Construct the propagation obstacle coefficient of the i-th wireless channel , and then generate a targeted optimization strategy (such as adjusting the signal transmission power, antenna angle, etc.), and continue to monitor the channel quality after implementation. If the signal quality fails to be effectively restored, trigger the second warning instruction to further ensure the stability and reliability of underground data transmission.

[0059] (3) After receiving the second warning instruction, the second channel interference analysis module further identifies the reflection surface and refraction surface of the obstacle through the three-dimensional model of the underground, and analyzes the influence of the multipath effect on signal transmission. The system calculates and constructs the multipath effect factor of the i-th wireless channel according to this information and evaluates it, generating a second strategy to optimize the propagation path of the wireless signal. By setting relay devices, adjusting the antenna coverage range, etc., the interference of the multipath effect can be effectively reduced, and the transmission quality of the signal can be improved. Description of the Drawings

[0060] Figure 1 It is a schematic flow chart of the underground data wireless transmission system based on Internet of Things technology of the present invention. Detailed Embodiments

[0061] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0062] Embodiment 1

[0063] Please refer to Figure 1 , the present invention provides an underground data wireless transmission system based on Internet of Things technology, including:

[0064] A channel monitoring module for real-time monitoring of the transmission performance of several wireless channels underground and establishing a channel performance data set; the wireless channels include LoRa, Wi-Fi, 5G, Bluetooth, and NB-IoT;

[0065] A channel quality analysis module for establishing and training a channel prediction model, and analyzing based on the channel performance data set to obtain the channel quality coefficient of the i-th wireless channel , and presetting a quality threshold X. If the channel quality coefficient of the i-th wireless channel is lower than the quality threshold, a first warning instruction is triggered;

[0066] A first channel interference analysis module for, after receiving the first warning instruction, using the laser scanning technology of lidar to scan the underground environmental structure, establish an underground three-dimensional model, and identify obstacles to construct the underground environmental propagation obstacle coefficient of the i-th wireless channel , and presetting a first influence threshold. If , it indicates that the signal propagation path loss influence of this wireless channel is abnormal, generating a first strategy. After the first strategy is implemented, if the channel quality coefficient of the i-th wireless channel is still unqualified, a second warning instruction is triggered;

[0067] A second channel interference analysis module for, after receiving the second warning instruction, identifying the reflection surface and refraction surface of the obstacle based on the underground three-dimensional model to construct the multipath effect factor of the i-th wireless channel , and presetting a second influence threshold . When , a second strategy is generated and implemented.

[0068] In this embodiment, the channel monitoring module can monitor the transmission performance of several underground wireless channels in real time, collect data and establish a channel performance dataset, providing an accurate data basis for subsequent channel quality analysis. By monitoring and analyzing different wireless channels such as LoRa, Wi-Fi, 5G, Bluetooth, and NB-IoT, the signal quality in the underground environment can be comprehensively evaluated, and signal attenuation or distortion problems can be detected in a timely manner, thus avoiding data transmission failures during underground operations.

[0069] The channel quality analysis module can accurately analyze the channel quality coefficient of the wireless channel based on the real-time dataset by establishing and training a channel prediction model, and compare it with the preset quality threshold X to ensure that the signal quality meets the requirements. When the channel quality coefficient is lower than the preset quality threshold X, the system can trigger the first warning instruction in a timely manner to remind the operator to intervene and prevent the signal quality from deteriorating further.

[0070] After receiving the first warning instruction, the first channel interference analysis module scans the underground environment using the laser scanning technology of lidar, establishes a three-dimensional model of the underground, and identifies obstacles in the underground environment. Based on the dielectric constant and attenuation factor of the obstacles, the system constructs the propagation obstacle coefficient of the i-th wireless channel and then generates targeted optimization strategies (such as adjusting the signal transmission power, antenna angle, etc.), and continues to monitor the channel quality after implementation. If the signal quality fails to recover effectively, the second warning instruction is triggered to further ensure the stability and reliability of underground data transmission.

[0071] After receiving the second warning instruction, the second channel interference analysis module further identifies the reflection surface and refraction surface of the obstacle through the three-dimensional model of the underground, and analyzes the impact of the multipath effect on signal transmission. The system calculates the multipath effect factor based on this information and generates a second strategy to optimize the propagation path of the wireless signal through a preset influence threshold. By setting relay devices, adjusting the antenna coverage range, etc., the interference of the multipath effect can be effectively reduced, and the signal transmission quality can be improved.

[0072] By dynamically adjusting the transmission strategy of the wireless channel, this system not only solves the problems of signal attenuation and interference in the complex underground environment, but also can optimize the signal propagation path in real time according to actual needs to ensure stable data transmission. This is of great significance for safety monitoring, environmental monitoring, equipment fault diagnosis, etc. during underground operations, greatly improving the safety and efficiency of underground operations.

[0073] Embodiment 2

[0074] This embodiment is an explanatory description based on Embodiment 1. Please refer to Figure 1 Specifically, the channel monitoring module includes a first deployment unit and a first monitoring unit;

[0075] The first deployment unit is used to deploy and install several wireless communication devices in the downhole or surface environment. The wireless communication devices include LoRa gateways, Wi-Fi routers, Bluetooth devices, NB-IoT base station devices, and 5G base station devices;

[0076] The first monitoring unit is used to collect the transmission performance data of the receiving end of each wireless channel by using a spectrum analyzer, a wireless network analyzer, a modulation analyzer, and a noise analyzer, and establish a channel performance data set; the channel performance data set includes: the bandwidth of the i-th wireless channel , signal-to-noise ratio , modulation error rate , noise component and frequency utilization rate .

[0077] In this embodiment, through a spectrum analyzer, a wireless network analyzer, a modulation analyzer, and a noise analyzer, the system can collect and record in real time the detailed performance data of the receiving end of each wireless channel. The establishment of the channel performance data set not only provides rich input data for the channel quality analysis module, but also enables the system to accurately evaluate the actual performance of each wireless channel. According to key indicators such as bandwidth, signal-to-noise ratio, and error rate, the system can comprehensively understand the transmission capacity and stability of the channel, laying a solid foundation for predicting and optimizing the quality of the wireless channel.

[0078] Embodiment 3

[0079] This embodiment is an explanatory description based on Embodiment 2. Please refer to Figure 1 , specifically, the channel quality analysis module includes a channel prediction model establishment unit and a channel analysis unit;

[0080] The channel prediction model establishment unit is used to perform data cleaning and normalization processing on the channel performance data set, and convert the data into the interval [0, 1]; by performing cleaning and normalization processing on the channel performance data set, the consistency and accuracy of the data are ensured. Data cleaning can eliminate noise and irrelevant data, avoiding the influence of incorrect data on model training. Normalization processing converts the data into the interval [0, 1], enabling features with different dimensions to be compared on the same scale and avoiding bias during model training. This can ensure that the subsequent channel prediction model has higher training effectiveness and generalization ability.

[0081] A channel prediction model is established using Convolutional Neural Network (CNN) technology. After dividing the normalized channel performance dataset and the second environment dataset into an 80% training set and a 20% test set, the channel prediction model is trained and tested, and then the trained channel prediction model is exported. The Convolutional Neural Network (CNN) technology is used to establish the channel prediction model. CNN can automatically extract features from the channel performance dataset and perform effective pattern recognition. Compared with traditional linear models or shallow neural networks, CNN shows stronger capabilities in processing complex datasets and time-series data, and can more accurately predict channel performance, especially in complex environments. Training and testing with an 80% training set and a 20% test set ensure the robustness and accuracy of the model.

[0082] The channel analysis unit is used to extract the bandwidth of the i-th wireless channel in the channel performance dataset using the trained channel prediction model and the signal-to-noise ratio , and calculate the channel capacity of the i-th wireless channel : ;

[0083] Using the trained channel prediction model, the bandwidth and signal-to-noise ratio in the channel performance data are extracted to calculate the channel capacity. This calculation can accurately reflect the transmission capacity of each wireless channel, thus helping the system determine whether the current channel can meet the data transmission requirements. The larger the channel capacity, the more data traffic and higher transmission speed the channel can support, improving the efficiency of the entire communication system.

[0084] And extract the modulation error rate of the i-th wireless channel in the channel performance dataset , noise component , frequency utilization rate , combine with the channel capacity of the i-th wireless channel , after dimensionless processing, calculate and obtain the channel quality coefficient of the i-th wireless channel through the following formula : ;

[0085] In the formula, respectively represent the weight coefficients of the modulation error rate , noise component , frequency utilization rate of the i-th wireless channel, , the values of the modulation error rate , noise component and frequency utilization rate of the i-th wireless channel are all inversely proportional, the lower the better; the channel capacity It is directly proportional, and the larger the better. By processing the modulation error rate, noise components, and frequency utilization rate, and combining with channel capacity calculation, the channel quality coefficient of the i-th wireless channel is obtained. This coefficient not only considers the bandwidth and signal-to-noise ratio of the channel, but also comprehensively takes into account other influencing factors in the channel (such as error rate and noise, etc.), making the evaluation of channel quality more comprehensive and accurate. The design of the channel quality coefficient is conducive to reflecting the actual quality of the channel in real time, and then affecting the selection and adjustment of communication strategies.

[0086] The channel quality analysis module further includes a first warning unit, and the first warning unit is used to preset a quality threshold X, and compare the channel quality coefficient of the i-th wireless channel with the quality threshold X to obtain a first evaluation result, including:

[0087] If , it indicates that the channel quality of the i-th wireless channel is qualified, and extract the maximum value of the channel quality coefficient of the i-th wireless channel when the channel quality is qualified; by selecting the channel with the largest channel quality coefficient as the current wireless communication channel, the system can ensure the efficiency and stability of communication, and avoid communication interruption or low efficiency caused by selecting unqualified channels. The strategy of preferentially selecting the optimal channel improves the data transmission capacity of the entire system, especially in the case of complex environment and large signal fluctuations. If

[0088] , it indicates that the channel quality of the i-th wireless channel is unqualified. If the channel qualities of all current wireless channels are unqualified, a first warning instruction is sent outwards. This warning mechanism can effectively prevent data transmission interruption or performance degradation caused by channel quality problems, and enhance the intelligence and response ability of the system. If the qualities of all channels are unqualified, the system automatically issues a first warning instruction to ensure that potential problems can be discovered and processed at the earliest stage, prevent the expansion of faults, and ensure the safety of underground operations. If

[0089] Example 4

[0090] This example is an explanatory description based on Example 1. Please refer to Figure 1 , specifically, the first channel interference analysis module includes a three-dimensional modeling unit, a first identification unit, and an Internet of Things sensing and acquisition unit;

[0091] The 3D modeling unit is used to scan the underground environment structure using the laser scanning technology of lidar after receiving the first warning instruction, obtain the underground point cloud data, and perform 3D modeling after denoising, filtering, and fitting the underground point cloud data to obtain the underground 3D model; the laser scanning technology of lidar can accurately obtain the 3D point cloud data of the underground environment and construct the 3D model of the underground environment in real time. This technology effectively solves the problem of complex and diverse underground environments and ensures the accurate modeling of underground structures. Through denoising, filtering, and fitting processing, the interference and errors in the point cloud data are eliminated, making the subsequent obstacle recognition and signal propagation analysis more accurate and greatly improving the reliability of environmental modeling.

[0092] The first recognition unit is used to identify obstacles in the underground environment based on the underground 3D model. The obstacles include dry soil, mudstone, granite, metal pipes, concrete, glass, wood, silica, and shale; the metal pipes include steel and aluminum materials; this recognition process makes the subsequent interference analysis highly accurate and helps to evaluate the impact of different types of obstacles on wireless signal propagation. In this way, the system can quickly identify and locate potential interference sources, providing a basis for signal optimization.

[0093] The Internet of Things (IoT) sensing and acquisition unit is used to monitor the moisture content S of underground obstacles using a humidity sensor in IoT technology and establish an impact dataset. The humidity sensor in IoT technology can monitor the moisture content of underground obstacles in real time, which is crucial for subsequent signal propagation analysis. The change in the moisture content of obstacles will directly affect their relative permittivity and thus affect the propagation effect of wireless signals. By monitoring the humidity change in real time, the system can obtain more accurate environmental data, providing a timely basis for channel interference analysis and enhancing the adaptability and intelligence level of the system.

[0094] The first channel interference analysis module also includes an obstacle obstruction calculation unit and a signal loss assessment unit;

[0095] The obstacle obstruction calculation unit is used to mark the obstacles in the underground environment, extract the moisture content S of the obstacles in the impact dataset, and calculate the relative permittivity of the obstacles : ;

[0096] In the formula, is the absolute permittivity of the obstacle in the dry state, including: dry soil: ϵ = 3 - 5; mudstone: ϵ = 3 - 10; granite: ϵ = 4 - 6; steel: ϵ = 1.5 - 3; concrete: ϵ = 4 - 7; glass: ϵ = 4 - 10; wood: ϵ = 2 - 3; silica: ϵ = 4 - 5; shale: ϵ = 8 - 15; is the permittivity of free space, set to: 8.854×10−12  F / m; Denotes the humidity sensitivity coefficient, obtained through experiments, which describes the degree of influence of humidity change on the dielectric constant of the material; when the moisture content S of the obstacle increases, its dielectric constant will increase accordingly, and the propagation of the wireless signal will be more attenuated, resulting in a decrease in the wireless signal strength; according to the humidity data and the type of obstacle, the relative dielectric constant of each obstacle is calculated, which can more accurately evaluate the influence of different types of obstacles on the wireless signal. In particular, the influence of humidity change on the dielectric constant of the material is accurately modeled, making the signal attenuation analysis more in line with the actual situation. This calculation helps to better predict the signal propagation loss, take measures in advance to optimize the signal transmission, and improve the quality of underground wireless communication.

[0097] And based on the relative dielectric constant of the obstacle , combined with the physical thickness of the k-th obstacle, the propagation obstacle coefficient of the i-th wireless channel is calculated through the following formula ;

[0098] ;

[0099] In the formula, k represents the label of the obstacle, m represents the total number of obstacles from the signal transmitter to the receiver of the i-th wireless channel, denotes the attenuation factor of the k-th obstacle, including: the attenuation factor of granite β = 0.5 - 1.5 dB / m; the attenuation factor of shale β = 3 - 4 dB / m; the attenuation factor of mudstone β = 2 - 5 dB / m; the attenuation factor of sandstone β = 1 - 2 dB / m; the attenuation factor of steel β = 20 - 40 dB / m; the attenuation factor of aluminum β = 15 - 25 dB / m; the attenuation factor of concrete β = 2 - 4 dB / m;

[0100] In the formula, denotes the physical thickness of the k-th obstacle, denotes the propagation path length of the k-th obstacle; denotes the path loss attenuation exponent, which describes the rate of signal attenuation with increasing distance, obtained by measuring the signal strength at different distances and obtaining the relationship between path loss and distance. By combining the relative dielectric constant and physical thickness of the obstacle, the obstacle factor for the propagation of the wireless signal is calculated for each obstacle. This calculation not only considers the material and humidity of the obstacle, but also combines the attenuation factor and physical thickness of the obstacle, making the estimation of signal propagation loss more accurate. In this way, the system can quantitatively analyze the specific influence of various obstacles on the wireless signal, providing a scientific basis for subsequent signal adjustment strategies.

[0101] The signal loss evaluation unit is used to preset the first influence threshold , and compare the propagation obstacle coefficient of the i-th wireless channel with the first influence threshold for comparative evaluation to assess whether the signal propagation path loss of the i-th wireless channel caused by obstacles exceeds the threshold influence, including:

[0102] If , it indicates that the signal propagation path loss influence of the i-th wireless channel is abnormal, and a first strategy is generated, including: adjusting the angle of the wireless signal antenna to the direction with the fewest obstacles, gradually increasing the adjustment to increase the signal transmission power of the current wireless channel by 1%-3%, and continuously adjusting 2-3 times until it reaches; then monitor the channel quality coefficient of the i-th wireless channel again . If the channel quality coefficient of the i-th wireless channel is still unqualified, a second warning instruction is sent outwards;

[0103] If , it indicates that the signal propagation path loss influence of the i-th wireless channel is normal, and continuous monitoring is carried out.

[0104] In this embodiment, the signal loss evaluation unit can evaluate whether the signal propagation path loss of each wireless channel exceeds the threshold according to the calculated propagation obstacle coefficient and the preset influence threshold. This evaluation process can timely detect the situation where the wireless signal propagation is blocked and judge whether it will have an adverse impact on the communication quality. This mechanism enables the system to monitor the quality of underground wireless communication in real time and ensure that the signal transmission strategy can be adjusted in time when the signal is blocked. When the system detects that the signal propagation path loss of a certain wireless channel is abnormal, it will automatically generate an optimization strategy. This includes adjusting the angle of the wireless signal antenna to avoid the direction with more obstacles and gradually increasing the signal transmission power. This dynamic adjustment process can effectively reduce signal interference and attenuation, thereby improving the signal strength and communication quality. Through this adaptive optimization strategy, the system can ensure the stability of the wireless signal and achieve efficient communication even in a complex environment. If the channel quality is still unqualified after signal optimization, the system will automatically send out a second warning instruction. This mechanism ensures that the underground wireless communication system can respond to quality problems in time and avoid being in a poor signal state for a long time. The secondary warning mechanism provides more clear signal quality change information for the operator, which helps to speed up fault troubleshooting and ensure the stable operation of the system.

[0105] Embodiment 5

[0106] This embodiment is an explanatory description based on Embodiment 1. Please refer to Figure 1 . Specifically, the second channel interference analysis module includes a second recognition unit, a reflection coefficient calculation unit, and a refraction coefficient calculation unit;

[0107] The second recognition unit is used to recognize the reflecting surface and refracting surface of obstacles in the underground environment according to the underground three-dimensional model after the implementation of the first strategy and when receiving the second warning instruction; the reflecting surface and refracting surface of obstacles include rock formations, support structures, smooth metal surfaces, concrete walls, liquid surfaces, and smooth reflective material surfaces; after the implementation of the first strategy, the second recognition unit recognizes the reflecting surface and refracting surface in the underground environment based on the underground three-dimensional model. By accurately recognizing rock formations, support structures, smooth metal surfaces, concrete walls, liquid surfaces, and smooth reflective material surfaces, the system can timely master the key factors affecting wireless signals. This recognition process helps to understand in detail the reflection and refraction phenomena of signals during propagation, thereby providing a more accurate basis for subsequent signal interference analysis.

[0108] The refractive index calculation unit is used to mark the reflecting surface of obstacles in the underground environment and calculate the wave impedance Z of each obstacle. The formula is as follows: ;

[0109] In the formula, is the magnetic permeability of the obstacle, is the absolute dielectric constant of the obstacle in the dry state. Different wave impedances will affect the reflection or refraction phenomenon of electromagnetic waves; by calculating the wave impedance Z of the obstacle, the present invention can quantitatively analyze the influence of different obstacles on the propagation of electromagnetic waves.

[0110] When the electromagnetic wave of the wireless signal propagates to the reflecting surface of the obstacle, part of the electromagnetic wave of the wireless signal will be reflected back, causing signal loss. The reflection coefficient of the k-th obstacle is calculated by the following formula :

[0111] ;

[0112] In the formula, is the wave impedance of the incident medium, represents the wave impedance of the obstacle where the reflecting surface is located; , indicating total reflection, when, it means no reflection and all electromagnetic waves pass through the obstacle;

[0113] The refractive index calculation unit is used to calculate the influence of the k-th obstacle on the electromagnetic wave of the wireless signal based on the marked refracting surface of the obstacle in the underground environment; when the electromagnetic wave of the wireless signal propagates to the refracting surface of the obstacle, part of the electromagnetic wave of the wireless signal will be transmitted into the new medium and refracted, and continue to propagate along the new propagation path. The refractive index of the k-th obstacle is calculated by the following formula : ;

[0114] In the formula, is the refractive medium, that is, the wave impedance of the wave after refraction and penetration into the new medium. is the wave impedance of the incident medium. When, it indicates complete transmission with no energy loss. When, it indicates that the obstacle cannot be penetrated at all.

[0115] In this embodiment, accurately calculating the reflection coefficient of the k-th obstacle can help the system evaluate the loss degree of the wireless signal, thereby more accurately predicting signal attenuation, and taking measures for optimization to reduce the decrease in signal strength. By calculating the refraction coefficient of the k-th obstacle , the system can predict the propagation path of the signal, and adjust the propagation strategy according to the wave impedance difference of different media to minimize the interference and signal loss caused by refraction and enhance the penetration ability of the signal.

[0116] Embodiment 6

[0117] This embodiment is an explanatory description carried out in Embodiment 1. Please refer to Figure 1 , specifically, the second channel interference analysis module further includes a multipath effect analysis unit and a multipath effect evaluation unit;

[0118] The multipath effect analysis unit is used to construct the multipath effect factor of the i-th wireless channel based on the reflection coefficient of the k-th obstacle and the refraction coefficient of the k-th obstacle. ;

[0119] Since the signal propagates to the receiving end through multiple paths and is affected differently by the direct path, reflection path, and refraction path, causing multipath effects; the multipath effects will result in the signal received at the receiving end of the wireless channel being a combination of signals from multiple different paths. Based on the reflection coefficient and the refraction coefficient of the k-th obstacle, the multipath effect factor of the i-th wireless channel is calculated through the following formula : ;

[0120] In the formula, k represents the label of the obstacle, m represents the total number of obstacles in the path from the signal transmitting end to the receiving end of the i-th wireless channel, represents the reflection coefficient of the k-th obstacle, represents the phase difference of the reflection path of the k-th obstacle, j is the imaginary unit representing the phase of the signal, represents the phase difference of the refraction path of the k-th obstacle, represents the propagation path length of the k-th obstacle.

[0121] The multipath effect evaluation unit is used to preset the second influence threshold and the multipath effect factor of the i-th wireless channel is compared with the second influence threshold for comparative evaluation to evaluate whether the multipath effect interference in the i-th wireless channel by the obstacle is abnormal, including:

[0122] Including:

[0123] When , it indicates that the multipath effect interference in the signal propagation path of the i-th wireless channel is abnormal, and a second strategy is generated, including: arranging a relay device every 7 - 10 meters according to the length of the underground channel, and the relay device is installed at the corners of the underground channel and near the obstacles; and adjusting the antenna beam coverage range of the current wireless channel from 90° to 45° to 60° to concentrate the signal transmission on the straight path until it ends;

[0124] When , it indicates that the multipath effect interference in the signal propagation path of the i-th wireless channel is normal, and gradually increase and adjust the signal transmission power of the current wireless channel by 4% - 6%, and continuously adjust it 2 - 3 times until it ends, and continue to monitor.

[0125] In this embodiment, by analyzing the signal propagation through different paths based on the reflection coefficient and refraction coefficient of the obstacle, the multipath effect factor of the wireless channel is constructed. Since the signal is affected by multiple reflection and refraction paths during propagation, the received signal is a combination of signals from multiple paths, and traditional methods cannot accurately evaluate these effects. The multipath effect analysis unit can accurately evaluate the multipath propagation of the signal, help the system finely adjust the propagation strategy, and reduce the mutual interference of signals. The multipath effect factor of each signal path is calculated through a formula, which comprehensively considers factors such as the phase difference, path length, and reflection coefficient of the reflection path and refraction path, and can quantify the influence of each path on signal propagation. This calculation result can provide reliable data support for subsequent interference evaluation and optimization. Especially in complex environments, it can accurately measure the influence of different paths on signal strength and quality. The multipath effect evaluation unit can evaluate in real time whether the multipath effect interference in the wireless channel is abnormal by comparing with the preset second influence threshold. This evaluation mechanism can quickly trigger interference remedial measures when the multipath effect exceeds the preset threshold. Timely identification of abnormal multipath effect interference enables the system to take effective countermeasures before the signal quality deteriorates, ensuring the stability of communication.

[0126] A relay device is arranged every 7 - 10 meters along the underground passage length. In particular, relay devices are arranged at corners and near obstacles, which can significantly improve the signal transmission efficiency and reduce signal attenuation and delay. Adjust the antenna beam coverage range of the wireless channel from 90° to 45° - 60°, so that the signal is concentrated on the straight - line path for transmission, avoiding the influence of excessive reflection and refraction paths on the signal quality. Gradually increase the signal transmission power by 4% - 6%. Through multiple adjustments, the signal can overcome the signal attenuation caused by the multipath effect and restore the communication quality. Through accurate multipath effect evaluation and optimization, this system can maintain clear and stable wireless communication during underground operations, avoiding safety risks caused by signal loss or quality degradation. The stability of signal transmission directly affects the coordination and safety of underground operations. The optimized signal quality improves the operation efficiency and also provides a more secure communication guarantee for underground workers.

[0127] The setting of the threshold value is for the convenience of comparison. Regarding the size of the threshold value, it depends on the amount of sample data and the base quantity set by those skilled in the art for each group of sample data; as long as it does not affect the proportional relationship between the parameter and the quantized value.

[0128] The above - mentioned formulas are all obtained through software simulation by collecting a large amount of data and selecting a formula close to the true value. The coefficients in the formula are set by those skilled in the art according to the actual situation. The above is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention, according to the technical solution and inventive concept of the present invention, makes equivalent substitutions or changes, and should be covered within the protection scope of the present invention.

Claims

1. A wireless downhole data transmission system based on Internet of Things technology, characterized in that, Including: A channel monitoring module for real-time monitoring of the transmission performance of several wireless channels underground and establishing a channel performance data set; The wireless channels include LoRa, Wi-Fi, 5G, Bluetooth, and NB-IoT; A channel quality analysis module, which is used to establish and train a channel prediction model, and perform analysis based on a channel performance data set to obtain the channel quality coefficient of the i-th wireless channel , and a quality threshold X is preset. If the channel quality coefficient of the i-th wireless channel is lower than the quality threshold, a first warning instruction is triggered; The first channel interference analysis module is used to, after receiving the first warning instruction, adopt the laser scanning technology of lidar to scan the underground environment structure, establish a three-dimensional underground model, and identify obstacles, so as to construct the underground environment propagation obstacle coefficient of the i-th wireless channel , and preset the first influence threshold . If , it indicates that the signal propagation path loss influence of this wireless channel is abnormal, generate the first strategy, and after the first strategy is implemented, if the channel quality coefficient of the i-th wireless channel is still unqualified, trigger the second warning instruction; The second channel interference analysis module is used to receive the second early warning instruction and identify the reflection surface and refraction surface of the obstacle according to the three-dimensional underground model to construct the multipath effect factor of the i-th wireless channel , and preset the second influence threshold , when , generate the second strategy and implement it.

2. The downhole data wireless transmission system based on Internet of Things technology according to claim 1, characterized in that, The channel monitoring module includes a first deployment unit and a first monitoring unit; The first deployment unit is used to deploy and install several wireless communication devices in the underground or above-ground environment. The wireless communication devices include LoRa gateways, Wi-Fi routers, Bluetooth devices, NB-IoT base station devices, and 5G base station devices; The first monitoring unit is used to collect the transmission performance data of the receiving end of each wireless channel using a spectrum analyzer, a wireless network analyzer, a modulation analyzer, and a noise analyzer, and establish a channel performance data set; The described channel performance data set includes: the bandwidth of the i-th wireless channel , signal-to-noise ratio , modulation error rate , noise component and frequency utilization rate .

3. The underground data wireless transmission system based on Internet of Things technology according to claim 1, characterized in that, The channel quality analysis module includes a channel prediction model establishment unit and a channel analysis unit; The channel prediction model establishment unit is used to perform data cleaning and normalization processing on the channel performance data set, and convert the data into the [0,1] interval; And use the convolutional neural network CNN technology to establish a channel prediction model. After dividing the normalized channel performance data set and the second environment data set into a training set of 80% and a test set of 20%, after training and testing the channel prediction model, export the trained channel prediction model; The channel analysis unit is used to extract the bandwidth of the i-th wireless channel in the channel performance dataset by using the trained channel prediction model and the signal-to-noise ratio , and calculate and obtain the channel capacity of the i-th wireless channel : ; And extract the modulation error rate of the i-th wireless channel in the channel performance dataset , noise component , frequency utilization rate , combined with the channel capacity of the i-th wireless channel , after dimensionless processing, the channel quality coefficient of the i-th wireless channel is calculated through the following formula : ; In the formula, respectively represent the modulation error rate of the i-th wireless channel , the noise component , the frequency utilization rate of the weight coefficient, the modulation error rate of the i-th wireless channel , the noise component and the frequency utilization rate are all inversely proportional, the lower the better; the channel capacity of the i-th wireless channel is directly proportional, the larger the better.

4. The downhole data wireless transmission system based on the Internet of Things technology according to claim 1, wherein The channel quality analysis module further includes a first warning unit, and the first warning unit is configured to preset a quality threshold X and compare the channel quality coefficient of the i-th wireless channel with the quality threshold X to obtain a first evaluation result, including: If , it indicates that the channel quality of the i-th wireless channel is qualified, and the channel quality coefficient of the i-th wireless channel with qualified channel quality is extracted the maximum value, and preferentially switch this channel as the current wireless communication channel; If , it indicates that the channel quality of the i-th wireless channel is unqualified. If the channel quality of all current wireless channels is unqualified, a first warning instruction is sent outwards.

5. The downhole data wireless transmission system based on the Internet of Things technology according to claim 1, characterized in that The first channel interference analysis module includes a three-dimensional modeling unit, a first identification unit, and an Internet of Things sensing and acquisition unit; The three-dimensional modeling unit is used to, after receiving the first warning instruction, use the laser scanning technology of lidar to scan the underground environment structure, obtain the underground point cloud data, and perform denoising, filtering, and fitting processing on the underground point cloud data and then perform three-dimensional modeling to obtain an underground three-dimensional model; The first identification unit is used to identify the obstacles in the underground environment based on the underground three-dimensional model. The obstacles include dry soil, mudstone, granite, metal pipes, concrete, glass, wood, silica, and shale; the metal pipes include steel and aluminum materials; The Internet of Things sensing and acquisition unit is used to monitor the moisture content S of the underground obstacles using a humidity sensor in the Internet of Things technology and establish an influence data set.

6. The underground data wireless transmission system based on Internet of Things technology according to claim 1, characterized in that The first channel interference analysis module also includes an obstacle obstruction calculation unit and a signal loss evaluation unit; The obstacle hindrance calculation unit is used to mark obstacles in the underground environment, extract the moisture content S of the obstacles in the influence dataset, and calculate the relative dielectric constant of the obstacles : ； wherein, is the absolute dielectric constant of the obstacle in the dry state, including: dry soil: ϵ = 3 - 5; mudstone: ϵ = 3 - 10; granite: ϵ = 4 - 6; steel: ϵ = 1.5 - 3; concrete: ϵ = 4 - 7; glass: ϵ = 4 - 10; wood: ϵ = 2 - 3; silica: ϵ = 4 - 5; shale: ϵ = 8 - 15; is the dielectric constant of free space, set to: 8.854×10 −12  F / m; is the humidity sensitivity coefficient, obtained through experiments, which describes the degree of influence of humidity change on the dielectric constant of the material; when the moisture content S of the obstacle increases, its dielectric constant will increase accordingly, and the propagation of the wireless signal will be more attenuated, resulting in a decrease in the wireless signal strength; and based on the relative permittivity of the obstacle , combined with the physical thickness of the k-th obstacle , the propagation obstacle coefficient of the i-th wireless channel is calculated and obtained through the following formula ; ; Wherein, k represents the label of the obstacle, and m represents the total number of obstacles from the signal transmitting end to the receiving end of the i-th wireless channel. represents the attenuation factor of the k-th obstacle, including: the attenuation factor β of granite is 0.5 - 1.5 dB / m; the attenuation factor β of shale is 3 - 4 dB / m; the attenuation factor β of mudstone is 2 - 5 dB / m; the attenuation factor β of sandstone is 1 - 2 dB / m; the attenuation factor β of steel is 20 - 40 dB / m; the attenuation factor β of aluminum is 15 - 25 dB / m; the attenuation factor β of concrete is 2 - 4 dB / m; In the formula, represents the physical thickness of the k-th obstacle, represents the propagation path length of the k-th obstacle; represents the path loss attenuation exponent, which describes the rate of signal attenuation with increasing distance and is obtained by measuring the signal strength at different distances and obtaining the relationship between path loss and distance.

7. The underground data wireless transmission system based on Internet of Things technology according to claim 6, characterized in that, The signal loss evaluation unit is used to preset a first influence threshold , and compare and evaluate the propagation obstacle coefficient of the i-th wireless channel with the first influence threshold to evaluate whether the signal propagation path loss of the obstacle to the i-th wireless channel exceeds the threshold influence, including: If , it indicates that the signal propagation path loss of the i-th wireless channel is abnormally affected, and a first strategy is generated, including: adjusting the angle of the wireless signal antenna to the direction with the least obstacles, gradually increasing the adjustment to increase the signal transmission power of the current wireless channel by 1%-3%, continuously adjusting 2-3 times until ; monitoring the channel quality coefficient of the i-th wireless channel again . If the channel quality coefficient of the i-th wireless channel is still unqualified, a second warning instruction is sent out; If , it means that the signal propagation path loss of the i-th wireless channel is not abnormal, and continuous monitoring is carried out.

8. The wireless downhole data transmission system based on Internet of Things technology according to claim 7, characterized in that, The second channel interference analysis module includes a second identification unit, a reflection coefficient calculation unit, and a refraction coefficient calculation unit; The second identification unit is used to, after the implementation of the first strategy and when receiving the second warning instruction, identify the reflection surface and refraction surface of the obstacles in the underground environment based on the underground three-dimensional model; the reflection surface and refraction surface of the obstacles include rock formations, support structures, smooth metal surfaces, concrete walls, liquid surfaces, and smooth reflective material surfaces; The refraction coefficient calculation unit is used to mark the reflection surface of obstacles in the downhole environment and calculate the wave impedance Z of each obstacle, and the formula is as follows: ; In the formula, is the magnetic permeability of the obstacle, is the absolute dielectric constant of the obstacle in the dry state. Different wave impedances will affect the reflection or refraction of electromagnetic waves; When the electromagnetic wave of the wireless signal propagates to the reflection surface of the obstacle, part of the electromagnetic wave of the wireless signal will be reflected back, resulting in signal loss. The reflection coefficient of the k-th obstacle is calculated by the following formula : ； wherein, is the wave impedance of the incident medium, represents the wave impedance of the obstacle where the reflection surface is located; , indicating total reflection, when, indicating no reflection and all electromagnetic waves passing through the obstacle; The refractive index calculation unit is used to calculate the influence of the k-th obstacle on the electromagnetic wave of the wireless signal based on the refractive surface that marks the obstacles in the underground environment; when the electromagnetic wave of the wireless signal propagates to the refractive surface of the obstacle, part of the electromagnetic wave of the wireless signal will be transmitted into the new medium and refracted, and continue to propagate along the new propagation path. The refractive index of the k-th obstacle is calculated by the following formula : ; In the formula, is the refractive medium, that is, the wave impedance of the wave after refraction and penetration into the new medium, is the wave impedance of the incident medium, When, it means complete transmission with no energy loss, When, it means complete inability to penetrate the obstacle.

9. The underground data wireless transmission system based on Internet of Things technology according to claim 8, characterized in that, The second channel interference analysis module also includes a multipath effect analysis unit and a multipath effect evaluation unit; The multipath effect analysis unit is used to construct the multipath effect factor of the ith wireless channel according to the reflection coefficient of the kth obstacle and the refraction coefficient of the kth obstacle ; ; Since the signal propagates to the receiving end through multiple paths and is affected differently by the direct path, reflection path, and refraction path, multipath effects are caused; the multipath effects will cause the signal received by the receiving end of the wireless channel to be a combination of signals from multiple different paths. According to the reflection coefficient and the refraction coefficient of the k-th obstacle, the multipath effect factor of the i-th wireless channel is calculated by the following formula : ； Where k represents the label of the obstacle, and m represents the total number of obstacles from the signal transmitter to the receiver of the i-th wireless channel. represents the reflection coefficient of the k-th obstacle. represents the phase difference of the reflected path of the k-th obstacle. j is the imaginary unit, representing the phase of the signal. represents the phase difference of the refracted path of the k-th obstacle. represents the propagation path length of the k-th obstacle.

10. The underground data wireless transmission system based on Internet of Things technology according to claim 9, characterized in that, The multipath effect evaluation unit is used to preset a second influence threshold , and compare the multipath effect factor of the i-th wireless channel with the second influence threshold for comparative evaluation, so as to evaluate whether the multipath effect interference of the obstacle on the i-th wireless channel is abnormal Including: When it indicates that the multipath effect interference in the signal propagation path of the i-th wireless channel is abnormal, and a second strategy is generated, including: arranging a relay device every 7-10 meters according to the length of the underground channel, and installing the relay device at the corners and near obstacles of the underground channel; and adjusting the antenna beam coverage range of the current wireless channel from 90° to 45° to 60° to concentrate the signal transmission on the straight path until it ends; When it indicates that the multipath effect interference in the signal propagation path of the i-th wireless channel is normal. Gradually increase the adjustment to improve the signal transmission power of the current wireless channel by 4% - 6%, and continuously adjust 2 - 3 times until it reaches the limit, and continuously monitor it.

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