Underground data wireless transmission system based on Internet of Things technology

Abstract

The invention discloses an underground data wireless transmission system based on the Internet of Things technology, and relates to the technical field of wireless signal transmission control, and the system carries out the comprehensive monitoring and analysis of different channel performance data of LoRa, Wi-Fi, 5G, Bluetooth and NB-IoT through a channel monitoring module, and facilitates the switching to a qualified channel. In combination with a laser radar technology, an underground environment is scanned, a three-dimensional model is established, underground obstacles and influences of moisture content changes of the underground obstacles on signal propagation are identified, and targeted optimization strategies such as adjustment of signal transmitting power and antenna angles are generated so as to deal with the problems of environment changes and signal attenuation. Further, through underground multipath effect analysis, after a second early warning instruction is received, the system can identify a reflecting surface and a refracting surface in signal propagation in real time, analyze the influence of the multipath effect on the signal and generate an optimization scheme, such as measures of arranging relay equipment, adjusting antenna beams and the like, so that interference is remarkably reduced, and the transmission quality of wireless signals is improved.

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: 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; The channel quality analysis module is used to establish and train the channel prediction model and analyze the channel performance data set to obtain the channel quality coefficient of the i-th wireless channel. , and preset the quality threshold X, if the channel quality coefficient of the i-th wireless channel If the quality is lower than the threshold, the first warning instruction is triggered; The first channel interference analysis module is used to use the laser scanning technology of the laser radar to scan the underground environment structure, establish an underground three-dimensional model, and identify obstacles to construct the underground environment propagation obstacle coefficient of the i-th wireless channel after receiving the first warning instruction. , and preset the first impact threshold ,like , indicating that the signal propagation path loss of the wireless channel has an abnormal impact, generating a first strategy, and after the first strategy is implemented, if the channel quality coefficient of the i-th wireless channel If it still fails to meet the requirements, the second warning instruction is triggered; The second channel interference analysis module is used to identify the reflection surface and refraction surface of the obstacle according to the underground three-dimensional model after receiving the second warning instruction, so as to construct the multipath effect factor of the i-th wireless channel , and preset the second impact threshold ,when , generate the second strategy and implement it.

[0007] Preferably, the channel monitoring module includes a first deployment unit and a first monitoring unit; The first deployment unit is used to deploy and install a number of wireless communication devices in an underground or surface environment, and 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 each wireless channel receiving end 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 Bit Error Rate , noise component and frequency utilization .

[0008] Preferably, the channel quality analysis module includes a channel prediction model establishment unit and a channel analysis unit; A channel prediction model unit is established to clean and normalize the channel performance data set and convert the data into the [0,1] interval; A channel prediction model is established using convolutional neural network (CNN) technology. The normalized channel performance data set and the second environment data set are divided into 80% training set and 20% test set. After the channel prediction model is trained and tested, the trained channel prediction model is exported. The channel analysis unit is used to extract the bandwidth of the i-th wireless channel in the channel performance data set using the trained channel prediction model. and signal-to-noise ratio , calculate and obtain the channel capacity of the i-th wireless channel : ; And extract the modulation bit error rate of the i-th wireless channel in the channel performance dataset , noise component , frequency utilization , 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 by the following formula: : ; In the formula, They represent the modulation bit error rate of the i-th wireless channel respectively. , noise component , frequency utilization The weight coefficient of the modulation bit error rate of the i-th wireless channel , noise component and frequency utilization The values ​​of are inversely proportional, the lower the better; the channel capacity of the i-th wireless channel is It is directly proportional, the bigger the better.

[0009] Preferably, the channel quality analysis module further includes a first warning unit, which is used to preset a quality threshold X and set the channel quality coefficient of the i-th wireless channel to Compare with the quality threshold X to obtain the first evaluation result, including: like , indicating that the channel quality of the i-th wireless channel is qualified, and extracting the channel quality coefficient of the i-th wireless channel in the qualified channel quality Maximum value, the channel is switched to be the current wireless communication channel first; like , indicating 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 early warning instruction is issued.

[0010] Preferably, the first channel interference analysis module includes a three-dimensional modeling unit, a first identification unit and an Internet of Things sensor collection unit; The three-dimensional modeling unit is used to scan the underground environment structure by using the laser scanning technology of the laser radar after receiving the first warning instruction, obtain the underground point cloud data, and perform three-dimensional modeling after denoising, filtering and fitting the underground point cloud data to obtain the underground three-dimensional model; 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. The IoT sensing acquisition unit is used to adopt the humidity sensor in the IoT technology to monitor the moisture content S of underground obstacles and establish an impact data set.

[0011] Preferably, the first channel interference analysis module further includes an obstacle obstruction calculation unit and a signal loss evaluation unit; The obstacle calculation unit is used to mark obstacles in the downhole environment, extract the moisture content S of the obstacles in the impact data set, and calculate the relative dielectric constant of the obstacles. : ; In the formula, is the absolute dielectric constant of the obstacle in dry state, including: Dry soil: ; Mudstone: ;granite: ;steel: ; Concrete: ;Glass: ;wood: Silica: ;shale: ; is the dielectric constant of free space, set to: 8.854×10 −12  F / m; It represents the humidity sensitivity coefficient, which is obtained through experiments and describes the degree of influence of humidity changes 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 strength of the wireless signal. And according to the relative dielectric constant of the obstacle , combined with the physical thickness of the kth obstacle , the propagation barrier factor of the i-th wireless channel is calculated by the following formula: : ; In the formula, k represents the number of obstacles, m represents the total number of obstacles from the signal transmitter to the i-th wireless channel receiver. Represents the attenuation factor of the kth obstacle, including: the attenuation factor of granite β=0.5-1.5dB / m; the attenuation factor of shale β=3-4dB / m; the attenuation factor of mudstone β=2-5dB / m; the attenuation factor of sandstone β=1-2dB / m; the attenuation factor of steel β=20-40dB / m; the attenuation factor of aluminum β=15-25dB / m; the attenuation factor of concrete β=2-4dB / m; In the formula, represents the physical thickness of the kth obstacle, represents the propagation path length of the kth obstacle; It represents the path loss attenuation exponent, which describes the rate at which the signal attenuates as the distance increases. It is obtained by actually measuring the signal strength at different distances and obtaining the relationship between path loss and distance.

[0012] Preferably, the signal loss evaluation unit is used to preset a first impact threshold , and the propagation barrier factor of the i-th wireless channel With the first impact threshold A comparative evaluation is performed to assess whether the obstacle's impact on the signal propagation path loss of the i-th wireless channel exceeds a threshold, including: like , indicating that the signal propagation path loss of the i-th wireless channel has an abnormal impact, generating the first strategy, including: adjusting the wireless signal antenna angle to the direction with the least obstacles, gradually increasing the adjustment to increase the current wireless channel signal transmission power by 1%-3%, and continuing to adjust 2-3 times until Until; monitor the channel quality coefficient of the i-th wireless channel again , if the channel quality coefficient of the i-th wireless channel If it still fails to meet the requirements, a second warning instruction will be issued; like , indicating that the signal propagation path loss of the i-th wireless channel has an abnormal impact and is continuously monitored.

[0013] Preferably, the second channel interference analysis module includes a second identification unit, a reflection coefficient calculation unit and a refraction coefficient calculation unit; The second recognition unit is used to identify the reflective surface and the refractive surface of obstacles in the underground environment according to the underground three-dimensional model after the first strategy is implemented and the second warning instruction is received; the reflective surface and the refractive surface of the obstacle include rock formations, support structures, smooth metal surfaces, concrete walls, liquid surfaces and smooth reflective material surfaces; The refractive index calculation unit is used to mark the reflection surface of obstacles in the underground environment and calculate the wave impedance Z of each obstacle. The formula is as follows: ; In the formula, is the magnetic permeability of the obstacle, is the absolute dielectric constant of the obstacle. The difference in wave impedance will affect the reflection or refraction of electromagnetic waves; When the wireless signal electromagnetic wave propagates to the reflective surface of the obstacle, part of the wireless signal electromagnetic wave will be reflected back, causing signal loss. The reflection coefficient of the kth obstacle is calculated by the following formula: : ; In the formula, The wave impedance when incident on the medium, Indicates the wave impedance of the obstacle where the reflecting surface is located; , indicating complete reflection, When , it means there is no reflection and all electromagnetic waves pass through the obstacle; The refractive index calculation unit is used to calculate the influence of the kth obstacle on the wireless signal electromagnetic wave according to the refractive surface of the obstacle in the downhole environment; when the wireless signal electromagnetic wave propagates to the refractive surface of the obstacle, part of the wireless signal electromagnetic wave will be transmitted to 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: : ; In the formula, is the refractive medium, that is, the impedance of the wave after refraction and penetration to the new medium. is the wave impedance when incident on the medium, When , it indicates complete transmission and no energy loss. , it means that the obstacle cannot be penetrated at all.

[0014] Preferably, the second channel interference analysis module further includes a multipath effect analysis unit and a multipath effect evaluation unit; The multipath effect analysis unit is used to analyze the reflection coefficient of the kth obstacle and the refractive index of the kth obstacle , to construct the multipath effect factor of the i-th wireless channel ; Since the signal propagates to the receiving end through multiple paths, it will be affected by the direct path, reflection path and refraction path, resulting in multipath effect. The multipath effect will cause the signal received by the wireless channel receiving end to be a combination of signals from multiple different paths. and refractive index , the multipath effect factor of the i-th wireless channel is calculated by the following formula : ; In the formula, k represents the number of obstacles, m represents the total number of obstacles from the signal transmitter to the i-th wireless channel receiver. represents the reflection coefficient of the kth obstacle, represents the phase difference of the reflection path of the kth obstacle, j is an imaginary unit, representing the phase of the signal, represents the phase difference of the refraction path of the kth obstacle, represents the propagation path length of the kth obstacle.

[0015] Preferably, the multipath effect evaluation unit is used to preset a second impact threshold , and the multipath effect factor of the i-th wireless channel With the second impact threshold A comparative evaluation is performed to assess whether the multipath interference of obstacles on the i-th wireless channel is abnormal, including: when When , it indicates that the multipath interference in the signal propagation path of the i-th wireless channel is abnormal, and the second strategy is generated, including: adding a relay device every 7-10 meters according to the length of the underground channel, and the relay device is installed at the corner of the underground channel and near 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 a straight path until until; when When , it means that the multipath interference in the signal propagation path of the i-th wireless channel is normal. Gradually increase the adjustment to increase the current wireless channel signal transmission power by 4%-6%, and continue to adjust 2-3 times until and continue to monitor.

[0016] The present invention provides an underground data wireless transmission system based on Internet of Things technology. It has the following beneficial effects: (1) The channel monitoring module can monitor the transmission performance of several wireless channels underground 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 discovered in a timely manner, thereby avoiding data transmission failures during underground operations.

[0017] (2) After receiving the first warning instruction, the first channel interference analysis module scans the underground environment by using laser scanning technology of LiDAR, establishes an underground three-dimensional model, and identifies obstacles in the underground environment. The system is based on the dielectric constant and attenuation factor of the obstacle. The humidity sensor in the Internet of Things technology can monitor the moisture content of the underground obstacles in real time. The change of the moisture content of the obstacle will directly affect its relative dielectric constant, and then affect the propagation effect of the wireless signal. Through real-time monitoring of humidity changes, the system can obtain more accurate environmental data and provide timely basis for channel interference analysis. Construct the propagation obstacle factor of the i-th wireless channel , and then generate targeted optimization strategies (such as adjusting signal transmission power, antenna angle, etc.), and continue to monitor 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.

[0018] (3) After receiving the second warning instruction, the second channel interference analysis module further identifies the reflection and refraction surfaces of obstacles through the underground three-dimensional model and analyzes the impact of multipath effect on signal transmission. Based on this information, the system calculates the multipath effect factor of the i-th wireless channel And evaluate and generate a second strategy to optimize the propagation path of the wireless signal. By setting up relay equipment, adjusting the antenna coverage, etc., the interference of multipath effect can be effectively reduced and the transmission quality of the signal can be improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 The figure is a flow chart of the underground data wireless transmission system based on the Internet of Things technology of the present invention. DETAILED DESCRIPTION

[0020] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0021] Example 1 See also Figure 1 The present invention provides a downhole data wireless transmission system based on the Internet of Things technology, comprising: 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; The channel quality analysis module is used to establish and train the channel prediction model and analyze the channel performance data set to obtain the channel quality coefficient of the i-th wireless channel. , and preset the quality threshold X, if the channel quality coefficient of the i-th wireless channel If the quality is lower than the threshold, the first warning instruction is triggered; The first channel interference analysis module is used to use the laser scanning technology of the laser radar to scan the underground environment structure, establish an underground three-dimensional model, and identify obstacles to construct the underground environment propagation obstacle coefficient of the i-th wireless channel after receiving the first warning instruction. , and preset the first impact threshold ,like , indicating that the signal propagation path loss of the wireless channel has an abnormal impact, generating a first strategy, and after the first strategy is implemented, if the channel quality coefficient of the i-th wireless channel If it still fails to meet the requirements, the second warning instruction is triggered; The second channel interference analysis module is used to identify the reflection surface and refraction surface of the obstacle according to the underground three-dimensional model after receiving the second warning instruction, so as to construct the multipath effect factor of the i-th wireless channel , and preset the second impact threshold ,when , generate the second strategy and implement it.

[0022] In this embodiment, the channel monitoring module can monitor the transmission performance of several wireless channels underground 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 discovered in time, thereby avoiding data transmission failures in underground operations.

[0023] The channel quality analysis module can accurately analyze the channel quality coefficient of the wireless channel based on the real-time data set by establishing and training the 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 time to remind the operator to intervene to avoid further deterioration of signal quality.

[0024] After receiving the first warning instruction, the first channel interference analysis module scans the underground environment by using laser scanning technology of laser radar, establishes a three-dimensional model of the underground environment, and identifies obstacles in the underground environment. The system constructs the propagation obstacle factor of the i-th wireless channel based on the dielectric constant and attenuation factor of the obstacle. , and then generate targeted optimization strategies (such as adjusting signal transmission power, antenna angle, etc.), and continue to monitor 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.

[0025] After receiving the second warning instruction, the second channel interference analysis module further identifies the reflection and refraction surfaces of obstacles through the underground three-dimensional model and analyzes the impact of 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 the preset impact threshold. By setting up relay equipment and adjusting the antenna coverage, the interference of multipath effect can be effectively reduced and the transmission quality of the signal can be improved.

[0026] This system not only solves the problem of signal attenuation and interference in the complex environment of the underground mine by dynamically adjusting the transmission strategy of the wireless channel, but also optimizes 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 and other aspects in underground operations, and greatly improves the safety and efficiency of underground operations.

[0027] Example 2 This embodiment is explained in Example 1, please refer to Figure 1 ,Specifically, the channel monitoring module includes a first deployment unit and a first monitoring unit; The first deployment unit is used to deploy and install a number of wireless communication devices in an underground or surface environment, and 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 each wireless channel receiving end 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 Bit Error Rate , noise component and frequency utilization .

[0028] In this embodiment, through the spectrum analyzer, wireless network analyzer, modulation analyzer and noise analyzer, the system can collect and record the detailed performance data of each wireless channel receiving end in real time. 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. Based on key indicators such as bandwidth, signal-to-noise ratio, bit error rate, etc., the system can fully understand the transmission capacity and stability of the channel, laying a solid foundation for predicting and optimizing the quality of the wireless channel.

[0029] Example 3 This embodiment is explained in Example 2. Please refer to Figure 1 ,Specifically, the channel quality analysis module includes a channel prediction model ,establishment unit and a channel analysis unit; The channel prediction model unit is established to clean and normalize the channel performance data set and convert the data into the [0,1] interval. The consistency and accuracy of the data are ensured by cleaning and normalizing the channel performance data set. Data cleaning can remove noise and irrelevant data to avoid the impact of erroneous data on model training. Normalization converts the data into the [0,1] interval so that features of different dimensions can be compared on the same scale to avoid deviations during model training. This ensures that the subsequent channel prediction model has higher training effects and generalization capabilities.

[0030] The convolutional neural network (CNN) technology is used to establish a channel prediction model. After the normalized channel performance data set and the second environment data set are divided into 80% training sets and 20% test sets, 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 data set and perform effective pattern recognition. Compared with traditional linear models or shallow neural networks, CNN has stronger capabilities in processing complex data sets and time series data, and can more accurately predict channel performance, especially in complex environments. The robustness and accuracy of the model are guaranteed by training and testing with 80% training sets and 20% test sets.

[0031] The channel analysis unit is used to extract the bandwidth of the i-th wireless channel in the channel performance data set using the trained channel prediction model. and signal-to-noise ratio , calculate and obtain the channel capacity of the i-th wireless channel : ; Through 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, thereby 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, which improves the efficiency of the entire communication system.

[0032] And extract the modulation bit error rate of the i-th wireless channel in the channel performance dataset , noise component , frequency utilization , 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 by the following formula: : ; In the formula, They represent the modulation bit error rate of the i-th wireless channel respectively. , noise component , frequency utilization The weight coefficient of , the modulation bit error rate of the i-th wireless channel , noise component and frequency utilization The values ​​of are inversely proportional, the lower the better; the channel capacity of the i-th wireless channel is The larger the better. By processing the modulation error rate, noise component and frequency utilization rate, combined with the channel capacity calculation, the channel quality coefficient of the i-th wireless channel is obtained. This coefficient not only takes into account the bandwidth and signal-to-noise ratio of the channel, but also integrates other influencing factors in the channel (such as bit error rate and noise), 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, thereby affecting the selection and adjustment of communication strategies.

[0033] The channel quality analysis module also includes a first warning unit, which is used to preset a quality threshold X and set the channel quality coefficient of the i-th wireless channel Compare with the quality threshold X to obtain the first evaluation result, including: like , indicating that the channel quality of the i-th wireless channel is qualified, and extracting the channel quality coefficient of the i-th wireless channel in the qualified channel quality The maximum value is selected, and the channel is switched to the current wireless communication channel first; by selecting the channel with the largest channel quality coefficient as the current wireless communication channel, the system can ensure efficient and stable communication and avoid communication interruption or inefficiency caused by selecting unqualified channels. The strategy of giving priority to the optimal channel improves the data transmission capacity of the entire system, especially in complex environments and when the signal fluctuates greatly.

[0034] like , indicating that the channel quality of the i-th wireless channel is unqualified. If the channel quality of all current wireless channels is unqualified, the first warning instruction is issued. This warning mechanism can effectively prevent data transmission interruption or performance degradation caused by channel quality problems, and enhance the intelligence and response capabilities of the system. If the quality of all channels is unqualified, the system automatically issues the first warning instruction to ensure that potential problems can be discovered and handled at the earliest stage, prevent the expansion of faults, and ensure the safety of underground operations.

[0035] Example 4 This embodiment is explained in 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 sensor acquisition unit; The 3D modeling unit is used to scan the underground environment structure using the laser scanning technology of the laser radar 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 the laser radar can obtain the 3D point cloud data of the underground environment with high precision and build the 3D model of the underground environment in real time. This technology effectively solves the problem of the complex and diverse underground environment and ensures the accurate modeling of the underground structure. Through denoising, filtering and fitting processing, the interference and errors in the point cloud data are eliminated, making the subsequent obstacle identification and signal propagation analysis more accurate, greatly improving the reliability of environmental modeling.

[0036] The first recognition unit is used to identify obstacles in the underground environment based on the underground three-dimensional model. Obstacles include dry soil, mudstone, granite, metal pipes, concrete, glass, wood, silica and shale; metal pipes include steel and aluminum. This recognition process makes the subsequent interference analysis more 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, thereby providing a basis for signal optimization.

[0037] The IoT sensing acquisition unit is used to use the humidity sensor in the IoT technology to monitor the moisture content S of the underground obstacles and establish an impact data set. The humidity sensor in the IoT technology can monitor the moisture content of underground obstacles in real time, which is crucial for subsequent signal propagation analysis. Changes in the moisture content of obstacles will directly affect their relative dielectric constant, and thus affect the propagation effect of wireless signals. Through real-time monitoring of humidity changes, the system can obtain more accurate environmental data, provide timely basis for channel interference analysis, and enhance the adaptability and intelligence level of the system.

[0038] The first channel interference analysis module also includes an obstacle obstruction calculation unit and a signal loss evaluation unit; The obstacle calculation unit is used to mark obstacles in the downhole environment, extract the moisture content S of the obstacles in the impact data set, and calculate the relative dielectric constant of the obstacles. : ; In the formula, is the absolute dielectric constant of the obstacle in dry state, including: Dry soil: ; Mudstone: ;granite: ;steel: ; Concrete: ;Glass: ;wood: Silica: ;shale: ; is the dielectric constant of free space, set to: 8.854×10 −12  F / m; represents the humidity sensitivity coefficient, which is obtained through experiments and describes the degree of influence of humidity changes 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 strength of the wireless signal. 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 impact of different types of obstacles on the wireless signal. In particular, the impact of humidity changes 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 signal propagation losses, take measures in advance to optimize signal transmission, and improve the quality of underground wireless communication.

[0039] And according to the relative dielectric constant of the obstacle , combined with the physical thickness of the kth obstacle , the propagation barrier factor of the i-th wireless channel is calculated by the following formula: : ; In the formula, k represents the number of obstacles, m represents the total number of obstacles from the signal transmitter to the i-th wireless channel receiver. Represents the attenuation factor of the kth obstacle, including: the attenuation factor of granite β=0.5-1.5dB / m; the attenuation factor of shale β=3-4dB / m; the attenuation factor of mudstone β=2-5dB / m; the attenuation factor of sandstone β=1-2dB / m; the attenuation factor of steel β=20-40dB / m; the attenuation factor of aluminum β=15-25dB / m; the attenuation factor of concrete β=2-4dB / m; In the formula, represents the physical thickness of the kth obstacle, represents the propagation path length of the kth obstacle; Represents the path loss attenuation index, which describes the rate at which the signal attenuates as the distance increases. It is obtained by actually measuring the signal strength at different distances to obtain the relationship between path loss and distance. By combining the relative dielectric constant and physical thickness of the obstacle, the barrier factor of each obstacle to the propagation of wireless signals is calculated. This calculation not only takes into account 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 impact of various obstacles on wireless signals, providing a scientific basis for subsequent signal adjustment strategies.

[0040] The signal loss evaluation unit is used to preset a first impact threshold , and the propagation barrier factor of the i-th wireless channel With the first impact threshold A comparative evaluation is performed to assess whether the signal propagation path loss of the ith wireless channel caused by the obstacle exceeds a threshold, including: like , indicating that the signal propagation path loss of the i-th wireless channel has an abnormal impact, generating the first strategy, including: adjusting the wireless signal antenna angle to the direction with the least obstacles, gradually increasing the adjustment to increase the current wireless channel signal transmission power by 1%-3%, and continuing to adjust 2-3 times until Until; monitor the channel quality coefficient of the i-th wireless channel again , if the channel quality coefficient of the i-th wireless channel If it still fails to meet the requirements, a second warning instruction will be issued; like , indicating that the signal propagation path loss of the i-th wireless channel has an abnormal impact and is continuously monitored.

[0041] In this embodiment, the signal loss evaluation unit can evaluate whether the signal propagation path loss of each wireless channel exceeds the threshold value according to the calculated propagation obstacle factor and the preset impact threshold value. The evaluation process can timely discover the situation where the wireless signal propagation is blocked and determine whether it will have an adverse effect 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 finds 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 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 the signal is optimized, the system will automatically issue a second warning instruction. This mechanism ensures that the underground wireless communication system can respond to quality problems in a timely manner and avoid being in a poor signal state for a long time. The secondary warning mechanism provides operators with clearer information on signal quality changes, which helps to speed up troubleshooting and ensure the stable operation of the system.

[0042] Example 5 This embodiment is explained in Example 1, please refer to Figure 1 ,Specifically, 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 identify the reflective and refractive surfaces of obstacles in the underground environment based on the underground three-dimensional model after the first strategy is implemented and the second early warning instruction is received; the reflective and refractive surfaces of obstacles include rock formations, supporting structures, smooth metal surfaces, concrete walls, liquid surfaces, and smooth reflective material surfaces; the second identification unit identifies the reflective and refractive surfaces in the underground environment based on the underground three-dimensional model after the first strategy is implemented. Through the accurate identification of rock formations, supporting structures, smooth metal surfaces, concrete walls, liquid surfaces, and smooth reflective material surfaces, the system can timely grasp the key factors affecting wireless signals. This identification 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.

[0043] The refractive index calculation unit is used to mark the reflection surface of obstacles in the underground environment and calculate the wave impedance Z of each obstacle. The formula is as follows: ; In the formula, is the magnetic permeability of the obstacle, is the absolute dielectric constant of the obstacle. The difference in wave impedance will affect the reflection or refraction 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.

[0044] When the wireless signal electromagnetic wave propagates to the reflective surface of the obstacle, part of the wireless signal electromagnetic wave will be reflected back, causing signal loss. The reflection coefficient of the kth obstacle is calculated by the following formula: : ; In the formula, is the wave impedance when incident on the medium, Indicates the wave impedance of the obstacle where the reflecting surface is located; , indicating complete reflection, When , it means there is no reflection and all electromagnetic waves pass through the obstacle; The refractive index calculation unit is used to calculate the influence of the kth obstacle on the wireless signal electromagnetic wave according to the refractive surface of the obstacle in the downhole environment; when the wireless signal electromagnetic wave propagates to the refractive surface of the obstacle, part of the wireless signal electromagnetic wave will be transmitted to 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: : ; In the formula, is the refractive medium, that is, the impedance of the wave after refraction and penetration to the new medium. is the wave impedance when incident on the medium, When , it indicates complete transmission and no energy loss. , it means that the obstacle cannot be penetrated at all.

[0045] In this embodiment, the reflection coefficient of the kth obstacle is accurately calculated. It can help the system evaluate the degree of wireless signal loss, thereby more accurately predicting signal attenuation and taking measures to optimize and reduce the decline in signal strength. The system can predict the propagation path of the signal and adjust the propagation strategy according to the difference in wave impedance of different media to minimize the interference and signal loss caused by refraction and improve the signal's penetration ability.

[0046] Example 6 This embodiment is explained in Example 1, please refer to Figure 1 ,Specifically, 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 analyze the reflection coefficient of the kth obstacle and the refractive index of the kth obstacle , to construct the multipath effect factor of the i-th wireless channel ; Since the signal propagates to the receiving end through multiple paths, it will be affected by the direct path, reflection path and refraction path, resulting in multipath effect. The multipath effect will cause the signal received by the wireless channel receiving end to be a combination of signals from multiple different paths. and refractive index , the multipath effect factor of the i-th wireless channel is calculated by the following formula: : ; In the formula, k represents the number of obstacles, m represents the total number of obstacles from the signal transmitter to the i-th wireless channel receiver. represents the reflection coefficient of the kth obstacle, represents the phase difference of the reflection path of the kth obstacle, j is an imaginary unit, representing the phase of the signal, represents the phase difference of the refraction path of the kth obstacle, represents the propagation path length of the kth obstacle.

[0047] The multipath effect evaluation unit is used to preset a second impact threshold , and the multipath effect factor of the i-th wireless channel With the second impact threshold A comparative evaluation is performed to evaluate whether the multipath effect interference of obstacles on the i-th wireless channel is abnormal, including: when When , it indicates that the multipath interference in the signal propagation path of the i-th wireless channel is abnormal, and the second strategy is generated, including: adding a relay device every 7-10 meters according to the length of the underground channel, and the relay device is installed at the corner of the underground channel and near 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 a straight path until for the Lord; when When , it means that the multipath interference in the signal propagation path of the i-th wireless channel is normal. Gradually increase the adjustment to increase the current wireless channel signal transmission power by 4%-6%, and continue to adjust 2-3 times until and continue to monitor.

[0048] In this embodiment, the propagation of the signal through different paths is analyzed according to the reflection coefficient and refraction coefficient of the obstacle, and the multipath effect factor of the wireless channel is constructed. Since the signal is affected by multiple reflection and refraction paths during the propagation process, the received signal is a combination of multiple path signals, and the traditional method cannot accurately evaluate these effects. The multipath effect analysis unit can accurately evaluate the multipath propagation of the signal, help the system to fine-tune the propagation strategy, and reduce the mutual interference of the signal. The multipath effect factor of each signal path is calculated by a formula. The factor combines the phase difference, path length, and reflection coefficient of the reflection path and the refraction path, and can quantify the impact of each path on the signal propagation. This calculation result can provide reliable data support for subsequent interference evaluation and optimization, especially in complex environments, and can accurately measure the impact of different paths on signal strength and quality. The multipath effect evaluation unit can evaluate in real time whether the interference of the multipath effect in the wireless channel is abnormal by comparing with the preset second impact threshold. This evaluation mechanism can quickly trigger interference remediation 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 decreases to ensure the stability of communication.

[0049] Arranging a relay device every 7-10 meters in the length of the underground channel, especially at corners and near obstacles, can significantly improve signal transmission efficiency and reduce signal attenuation and delay. Adjust the antenna beam coverage of the wireless channel from 90° to 45° to 60°, so that the signal is concentrated on the straight path for transmission, avoiding excessive reflection and refraction paths that affect 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 safer communication guarantee for underground workers.

[0050] The threshold is set to facilitate comparison. The size of the threshold depends on the amount of sample data and the number of bases set by technicians in this field for each group of sample data; as long as it does not affect the proportional relationship between the parameter and the quantized value.

[0051] The above formulas are obtained by collecting a large amount of data for software simulation and selecting a formula that is close to the actual value. The coefficients in the formula are set by technical personnel in this field according to actual conditions. The above is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited to this. Any technical personnel familiar with the technical field within the technical scope disclosed by the present invention, according to the technical solution and inventive concept of the present invention, make equivalent replacement or change, which should be covered within the protection scope of the present invention.

Claims

1. The underground data wireless transmission system based on Internet of Things technology is characterized by: include: The channel monitoring module 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; The channel quality analysis module is used to establish and train the channel prediction model and analyze the channel performance data set to obtain the channel quality coefficient of the i-th wireless channel. , and preset the quality threshold X, if the channel quality coefficient of the i-th wireless channel If the quality is lower than the threshold, the first warning instruction is triggered; The first channel interference analysis module is used to use the laser scanning technology of the laser radar to scan the underground environment structure, establish an underground three-dimensional model, and identify obstacles to construct the underground environment propagation obstacle coefficient of the i-th wireless channel after receiving the first warning instruction. , and preset the first impact threshold ,like , indicating that the signal propagation path loss of the wireless channel has an abnormal impact, generating a first strategy, and after the first strategy is implemented, if the channel quality coefficient of the i-th wireless channel If it still fails to meet the requirements, the second warning instruction is triggered; The second channel interference analysis module is used to identify the reflection surface and refraction surface of the obstacle according to the underground three-dimensional model after receiving the second warning instruction, so as to construct the multipath effect factor of the i-th wireless channel , and preset the second impact threshold ,when , generate the second strategy and implement it.

2. The underground data wireless transmission system based on Internet of Things technology according to claim 1 is 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 a number of wireless communication devices in an underground or above-ground environment, the wireless communication devices including a LoRa gateway, a Wi-Fi router, a Bluetooth device, an NB-IoT base station device, and a 5G base station device; The first monitoring unit is used to collect transmission performance data of each wireless channel receiving end using a spectrum analyzer, a wireless network analyzer, a modulation analyzer and a noise analyzer to 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 Bit Error Rate , noise component and frequency utilization .

3. The underground data wireless transmission system based on Internet of Things technology according to claim 1 is 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 a [0,1] interval; A channel prediction model is established using convolutional neural network (CNN) technology. The normalized channel performance data set and the second environment data set are divided into 80% training set and 20% test set. After the channel prediction model is trained and tested, the trained channel prediction model is exported. The channel analysis unit is used to extract the bandwidth of the i-th wireless channel in the channel performance data set by using the trained channel prediction model. and signal-to-noise ratio , calculate and obtain the channel capacity of the i-th wireless channel : ; And extract the modulation bit error rate of the i-th wireless channel in the channel performance dataset , noise component , frequency utilization , 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 by the following formula: : ; In the formula, They represent the modulation bit error rate of the i-th wireless channel respectively. , noise component , frequency utilization The weight coefficient of the modulation bit error rate of the i-th wireless channel , noise component and frequency utilization The values ​​of are inversely proportional, the lower the better; the channel capacity of the i-th wireless channel is It is directly proportional, the bigger the better.

4. The underground data wireless transmission system based on Internet of Things technology according to claim 1 is characterized in that: The channel quality analysis module also includes a first warning unit, which is used to preset a quality threshold X and set the channel quality coefficient of the i-th wireless channel Compare with the quality threshold X to obtain the first evaluation result, including: like , indicating that the channel quality of the i-th wireless channel is qualified, and extracting the channel quality coefficient of the i-th wireless channel in the qualified channel quality Maximum value, the channel is switched to be the current wireless communication channel first; like , indicating 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 early warning instruction is issued.

5. The underground data wireless transmission system based on Internet of Things technology according to claim 1 is characterized in that: The first channel interference analysis module includes a three-dimensional modeling unit, a first identification unit and an Internet of Things sensor acquisition unit; The three-dimensional modeling unit is used to scan the underground environment structure by using the laser scanning technology of the laser radar after receiving the first warning instruction, obtain the underground point cloud data, and perform three-dimensional modeling after denoising, filtering and fitting the underground point cloud data to obtain the underground three-dimensional model; 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; The Internet of Things sensing and collecting unit is used to use the humidity sensor in the Internet of Things technology to monitor the moisture content S of the underground obstacles and establish an impact data set.

6. The underground data wireless transmission system based on Internet of Things technology according to claim 1 is characterized in that: The first channel interference analysis module also includes an obstacle obstruction calculation unit and a signal loss assessment unit; The obstacle calculation unit is used to mark obstacles in the downhole environment, extract the moisture content S of the obstacles in the impact data set, and calculate the relative dielectric constant of the obstacles. : ; In the formula, is the absolute dielectric constant of the obstacle in dry state, including: Dry soil: ; Mudstone: ;granite: ;steel: ; Concrete: ;Glass: ;wood: Silica: ;shale: ; is the dielectric constant of free space, set to: 8.854×10 −12  F / m; It represents the humidity sensitivity coefficient, which is obtained through experiments and describes the degree of influence of humidity changes 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 strength of the wireless signal. And according to the relative dielectric constant of the obstacle , combined with the physical thickness of the kth obstacle , the propagation barrier factor of the i-th wireless channel is calculated by the following formula: : ; In the formula, k represents the number of obstacles, m represents the total number of obstacles from the signal transmitter to the i-th wireless channel receiver. The attenuation factor of the kth obstacle is: the attenuation factor of granite is β=0.5-1.5dB / m; the attenuation factor of shale is β=3-4dB / m; the attenuation factor of mudstone is β=2-5dB / m; the attenuation factor of sandstone is β=1-2dB / m; the attenuation factor of steel is β=20-40dB / m; the attenuation factor of aluminum is β=15-25dB / m; the attenuation factor of concrete is β=2-4dB / m; In the formula, represents the physical thickness of the kth obstacle, represents the propagation path length of the kth obstacle; It represents the path loss attenuation exponent, which describes the rate at which the signal attenuates as the distance increases. It is obtained by actually 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 is characterized in that: The signal loss evaluation unit is used to preset a first impact threshold , and the propagation barrier factor of the i-th wireless channel With the first impact threshold A comparative evaluation is performed to assess whether the signal propagation path loss of the ith wireless channel caused by the obstacle exceeds a threshold, including: like , indicating that the signal propagation path loss of the i-th wireless channel has an abnormal impact, generating the first strategy, including: adjusting the wireless signal antenna angle to the direction with the least obstacles, gradually increasing the adjustment to increase the current wireless channel signal transmission power by 1%-3%, and continuing to adjust 2-3 times until Until; monitor the channel quality coefficient of the i-th wireless channel again , if the channel quality coefficient of the i-th wireless channel If it still fails to meet the requirements, a second warning instruction will be issued; like , indicating that the signal propagation path loss of the i-th wireless channel has an abnormal impact and is continuously monitored.

8. The underground data wireless transmission system based on Internet of Things technology according to claim 7 is 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 recognition unit is used to identify the reflective surface and the refractive surface of obstacles in the underground environment according to the underground three-dimensional model after the first strategy is implemented and the second warning instruction is received; the reflective surface and the refractive surface of the obstacle include rock formations, support structures, smooth metal surfaces, concrete walls, liquid surfaces and smooth reflective material surfaces; The refractive index calculation unit is used to mark the reflection surface of obstacles in the underground environment and calculate the wave impedance Z of each obstacle. The formula is as follows: ; In the formula, is the magnetic permeability of the obstacle, is the absolute dielectric constant of the obstacle. The difference in wave impedance will affect the reflection or refraction of electromagnetic waves; When the wireless signal electromagnetic wave propagates to the reflective surface of the obstacle, part of the wireless signal electromagnetic wave will be reflected back, causing signal loss. The reflection coefficient of the kth obstacle is calculated by the following formula: : ; In the formula, is the wave impedance when incident on the medium, Indicates the wave impedance of the obstacle where the reflecting surface is located; , indicating complete reflection, When , it means there is no reflection and all electromagnetic waves pass through the obstacle; The refractive index calculation unit is used to calculate the influence of the kth obstacle on the wireless signal electromagnetic wave according to the refractive surface of the obstacle in the downhole environment; when the wireless signal electromagnetic wave propagates to the refractive surface of the obstacle, part of the wireless signal electromagnetic wave will be transmitted to 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: : ; In the formula, is the refractive medium, that is, the impedance of the wave after refraction and penetration to the new medium. is the wave impedance when incident on the medium, When , it indicates complete transmission and no energy loss. , it means that the obstacle cannot be penetrated at all.

9. The underground data wireless transmission system based on Internet of Things technology according to claim 8 is 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 analyze the reflection coefficient of the kth obstacle and the refractive index of the kth obstacle , to construct the multipath effect factor of the i-th wireless channel ; Since the signal propagates to the receiving end through multiple paths, it will be affected by the direct path, reflection path and refraction path, resulting in multipath effect. The multipath effect will cause the signal received by the wireless channel receiving end to be a combination of signals from multiple different paths. and refractive index , the multipath effect factor of the i-th wireless channel is calculated by the following formula : ; In the formula, k represents the number of obstacles, m represents the total number of obstacles from the signal transmitter to the i-th wireless channel receiver. represents the reflection coefficient of the kth obstacle, represents the phase difference of the reflection path of the kth obstacle, j is an imaginary unit, representing the phase of the signal, represents the phase difference of the refraction path of the kth obstacle, represents the propagation path length of the kth obstacle.

10. The underground data wireless transmission system based on Internet of Things technology according to claim 9 is characterized in that: The multipath effect evaluation unit is used to preset a second impact threshold , and the multipath effect factor of the i-th wireless channel With the second impact threshold A comparative evaluation is performed to evaluate whether the multipath effect interference of obstacles on the i-th wireless channel is abnormal. include: when When , it indicates that the multipath interference in the signal propagation path of the i-th wireless channel is abnormal, and the second strategy is generated, including: adding a relay device every 7-10 meters according to the length of the underground channel, and the relay device is installed at the corner of the underground channel and near 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 a straight path until until; when When , it means that the multipath interference in the signal propagation path of the i-th wireless channel is normal. Gradually increase the adjustment to increase the current wireless channel signal transmission power by 4%-6%, and continue to adjust 2-3 times until and continue to monitor.

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