5G mining wireless communication system and communication fault monitoring method

Abstract

The invention discloses a 5G mining wireless communication system and a communication fault monitoring method, and belongs to the technical field of mining wireless communication.The 5G mining wireless communication system comprises a dust sensor, a vibration sensor, a temperature sensor and a humidity sensor which are deployed underground, and underground data are monitored and collected in real time and uploaded to a dispatching control center deployed on the ground; the scheduling control center receives monitoring data and obtains wireless communication signal quality, extracts feature interference fingerprints and interference maps from the wireless communication signal quality, establishes an interference feature library, compares the feature interference fingerprints with the interference feature library for identification, obtains the type and strength of signal interference, and sends the interference data to the scheduling control center. According to the method, communication links are selected or combined to carry out primary adjustment processing on wireless communication signals, a comprehensive interference residual index is calculated, secondary adjustment processing is carried out on the wireless communication signals, and an evaluation report is generated, so that the type and strength of interference with a clear source in a mining scene can be identified, the accuracy and comprehensiveness of interference identification are improved, prospective anti-interference is realized, and the method is suitable for popularization and application. And the stability of a communication link is ensured.

Description

Technical Field

[0001] This application belongs to the field of mining wireless communication technology, specifically, it relates to a 5G mining wireless communication system and a communication fault monitoring method. Background Technology

[0002] With the widespread application of 5G technology in the mining industry, the demand for high-reliability, low-latency communication is becoming increasingly prominent. However, the underground environment is complex and variable, with numerous factors such as electromagnetic radiation from equipment, multipath reflections in tunnels, dynamic changes at the tunnel face, and environmental factors like dust, vibration, temperature, and humidity. These factors are interconnected and can easily cause complex interference to wireless communication signals, leading to a decline in communication quality or even interruption, seriously affecting mine production efficiency and operational safety. Therefore, achieving accurate monitoring and effective anti-interference adjustment of 5G mining wireless communication faults to ensure the stability and reliability of communication links has become a key technical problem that urgently needs to be solved in the process of intelligent development of mines.

[0003] Currently, most technologies for fault monitoring in mining wireless communication focus on interference identification in a single signal dimension. They judge interference by analyzing parameters such as the strength and frequency of communication signals, which is insufficient to cover multi-source interference such as transient electromagnetic fields, equipment crosstalk, and multipath reflections underground. This results in the inability to accurately extract interference patterns under specific environmental conditions, leading to insufficient accuracy and specificity in interference identification. At the same time, there is no adaptive adjustment strategy designed for dynamic scenarios such as mine roadway corners, changes in coal seam thickness, and equipment movement and obstruction. There is a lack of a predictive mechanism based on dynamic environmental changes, making it impossible to identify underground obstruction areas and future weak coverage areas in advance, which easily leads to coverage blind spots. Summary of the Invention

[0004] To address the aforementioned problems and technical deficiencies, this application adopts the following technical solution: a 5G mining wireless communication fault monitoring method, comprising the following steps:

[0005] Dust, vibration, temperature and humidity sensors are deployed underground to monitor and collect underground data in real time and upload it to the dispatch and control center deployed on the ground;

[0006] The dispatch and control center receives monitoring data and acquires the quality of wireless communication signals, performs signal separation, and extracts characteristic interference fingerprints and interference maps from the quality of wireless communication signals.

[0007] Establish an interference feature database, compare and identify the interference fingerprints with the interference feature database, and obtain the type and intensity of signal interference.

[0008] Based on the identification results, select or combine communication links to perform an adjustment process on the wireless communication signal;

[0009] Based on the processed signal, the comprehensive interference residual index is calculated. The wireless communication signal is then subjected to secondary adjustment processing based on the comprehensive interference residual index, and an evaluation report is generated.

[0010] Preferably, after receiving the data, the scheduling and control center will synchronize the environmental data and the wireless communication signal quality data of the corresponding link according to the timestamp;

[0011] An improved blind source separation algorithm is then used to establish an environmental parameter weight correction separation matrix to separate multi-source mixed interference data.

[0012] Preferably, the interference spectrum is generated by performing time-frequency domain analysis on signal quality data to produce a multi-dimensional interference spectrum including interference intensity, frequency point, bandwidth, and time domain.

[0013] Feature interference fingerprint extraction uses an attention mechanism network. First, an environmental data sequence is input, and the features of the interference pattern are used as the learning target to extract coupled feature fingerprints that can characterize the interference patterns expected to be generated under a specific combination of environmental states.

[0014] Furthermore, the interference feature library includes not only interference features at the signal dimension, but also environmental signal coupling feature fingerprints;

[0015] The real-time extracted coupling feature fingerprints are compared with the feature database, and the equipment operating condition labels that match the fingerprints are associated to identify the specific types and intensities of interference in mining scenarios.

[0016] Preferably, the adjustment process involves selecting or combining adjustment schemes from a preset strategy mapping table based on the interference type, adjusting the beamwidth, the relay node to which the device is pointing, and multi-beam coordination.

[0017] Preferably, the comprehensive interference residual index is calculated using a weighted summation formula:

[0018]

[0019] in, The normalized comprehensive interference residual index, To interfere with residual strength, This represents the signal-to-noise ratio loss value. α is the bit error rate, and β and γ are weighting coefficients;

[0020] When the overall residual interference index is less than the threshold, an evaluation report is generated directly; when the overall residual interference index is greater than or equal to the threshold, a secondary adjustment process is triggered.

[0021] Furthermore, the secondary adjustment process includes:

[0022] Deploy spectrum sensing units at key underground locations to construct a real-time broadband electromagnetic environment map. Combine this with underground materials, equipment layout, and roadway routes to establish an underground roadway model. The underground roadway model is dynamically updated based on the tunneling progress and equipment movement.

[0023] Ray tracing simulation is performed based on the underground tunnel model to calculate the underground obstruction area and predict the weak coverage area within a preset time period in the future.

[0024] A tunnel signal propagation model is established, and by combining the interference spectrum, the comprehensive interference residual index, and the underground obstruction or weak coverage areas, an adjustment strategy is generated to perform secondary processing on the wireless communication signal.

[0025] Furthermore, the tunnel signal propagation model is constructed based on the ray tracing method, and the mining loss factor is calculated using the following formula:

[0026]

[0027]

[0028]

[0029]

[0030] in, For mining loss factor, Let d be the free space loss, d be the propagation distance, and f be the signal frequency. Let λ be the roughness loss of the tunnel wall, h be the root mean square roughness of the tunnel wall, λ be the signal wavelength, d1 and d2 be the distances from the transmitter and receiver to the tunnel wall, and z be the propagation path length. Here, k represents the dust scattering loss, k is the dust type coefficient, and C is the dust concentration.

[0031] Furthermore, the adjustment strategy includes:

[0032] To allocate cleaner frequency bands to links severely affected by residual interference;

[0033] In areas with obstructed or weak coverage, the deployment of mobile relay nodes or the establishment of direct links on the device side are triggered in advance.

[0034] The critical data stream is switched from a path with a high composite interference residual index to a predicted stable backup path.

[0035] A 5G wireless communication system for mining, comprising:

[0036] The data acquisition module is used to deploy dust, vibration, temperature and humidity sensors downhole to monitor and collect downhole data in real time and upload it to the dispatch and control center deployed on the ground.

[0037] The feature extraction module is used by the dispatch control center to receive monitoring data and obtain wireless communication signal quality, perform signal separation, and extract feature interference fingerprints and interference maps from the wireless communication signal quality.

[0038] The interference identification module establishes an interference feature library, compares the characteristic interference fingerprints with the interference feature library to identify the type and intensity of signal interference;

[0039] The primary processing module, based on the identification results, selects or combines communication links to perform a primary adjustment processing on the wireless communication signal;

[0040] The secondary processing module calculates the comprehensive interference residual index based on the processed signal, performs secondary adjustment processing on the wireless communication signal based on the comprehensive interference residual index, and generates an evaluation report.

[0041] Compared to existing technologies, the beneficial effects of this application are as follows:

[0042] (1) This application extracts the characteristic interference fingerprint of the coupling between environment and signal, constructs an interference feature library containing signal dimension interference features and environmental signal coupling feature fingerprints, realizes the accurate characterization of interference modes under specific environmental state combinations, compares the real-time extracted coupling feature fingerprints with the feature library and associates them with equipment operating condition labels, identifies the type and intensity of interference with clear roots in mining scenarios, and improves the accuracy and comprehensiveness of interference identification.

[0043] (2) This application adopts an improved blind source separation algorithm and establishes a weighted correction separation matrix in combination with environmental parameters to efficiently and accurately separate complex multi-source mixed interference data in the well, thereby improving the reliability of interference monitoring;

[0044] (3) This application constructs an electromagnetic environment map by deploying spectrum sensing units, establishes a dynamically updated underground roadway model, combines ray tracing simulation to predict weak coverage areas, generates targeted adjustment strategies, predicts future interference and weak coverage risks in advance, and achieves forward-looking anti-interference by deploying mobile relay nodes in advance and switching backup paths, thus ensuring the stability of communication links. Attached Figure Description

[0045] In the attached diagram:

[0046] Figure 1 This is a schematic diagram of the method steps in an embodiment of this application;

[0047] Figure 2 This is a schematic diagram of the system structure according to an embodiment of this application. Detailed Implementation

[0048] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are some embodiments of this application, but not all embodiments. Generally, the components of the embodiments of this application described and shown in the accompanying drawings can be arranged and designed in various different configurations.

[0049] Example 1

[0050] like Figure 1 As shown, a 5G mining wireless communication fault monitoring method includes the following steps:

[0051] Dust, vibration, temperature and humidity sensors are deployed underground to monitor and collect underground data in real time and upload it to the dispatch and control center deployed on the ground;

[0052] The dispatch and control center receives monitoring data and acquires the quality of wireless communication signals, performs signal separation, and extracts characteristic interference fingerprints and interference maps from the quality of wireless communication signals.

[0053] After receiving the data, the dispatch control center will synchronize the environmental data and the wireless communication signal quality data of the corresponding link according to the timestamp;

[0054] An improved blind source separation algorithm is then used to establish an environmental parameter weight correction separation matrix to separate multi-source mixed interference data.

[0055] Interference maps are generated by performing time-frequency domain analysis on signal quality data, producing a multi-dimensional interference map that includes interference intensity, frequency points, bandwidth, and time domain.

[0056] Feature interference fingerprint extraction uses an attention mechanism network. First, an environmental data sequence is input, and the features of the interference pattern are used as the learning target to extract coupled feature fingerprints that can characterize the interference patterns expected to be generated under a specific combination of environmental states.

[0057] Establish an interference feature database, compare and identify the interference fingerprints with the interference feature database, and obtain the type and intensity of signal interference.

[0058] The interference feature library includes not only interference features at the signal level, but also environmental signal coupling feature fingerprints.

[0059] The real-time extracted coupling feature fingerprints are compared with the feature database, and the equipment operating condition labels that match the fingerprints are associated to identify the specific types and intensities of interference in mining scenarios.

[0060] Based on the identification results, select or combine communication links to perform an adjustment process on the wireless communication signal;

[0061] An adjustment process involves selecting or combining adjustment schemes from a preset strategy mapping table based on the type of interference, adjusting the beamwidth, the relay node to which the device is pointing, and multi-beam coordination.

[0062] Based on the processed signal, the comprehensive interference residual index is calculated. The wireless communication signal is then subjected to secondary adjustment processing based on the comprehensive interference residual index, and an evaluation report is generated.

[0063] The overall residual interference index is calculated using a weighted summation formula:

[0064]

[0065] in, The normalized comprehensive interference residual index, To interfere with residual strength, This represents the signal-to-noise ratio loss value. α is the bit error rate, and β and γ are weighting coefficients;

[0066] When the overall residual interference index is less than the threshold, an evaluation report is generated directly; when the overall residual interference index is greater than or equal to the threshold, a secondary adjustment process is triggered.

[0067] The secondary adjustment process includes:

[0068] Deploy spectrum sensing units at key underground locations to construct a real-time broadband electromagnetic environment map. Combine this with underground materials, equipment layout, and roadway routes to establish an underground roadway model. The underground roadway model is dynamically updated based on the tunneling progress and equipment movement.

[0069] Ray tracing simulation is performed based on the underground tunnel model to calculate the underground obstruction area and predict the weak coverage area within a preset time period in the future.

[0070] A tunnel signal propagation model is established, and by combining the interference spectrum, the comprehensive interference residual index, and the underground obstruction or weak coverage areas, an adjustment strategy is generated to perform secondary processing on the wireless communication signal.

[0071] The tunnel signal propagation model is constructed based on the ray tracing method. The mining loss factor is calculated using the following formula:

[0072]

[0073]

[0074]

[0075]

[0076] in, For mining loss factor, Let d be the free space loss, d be the propagation distance, and f be the signal frequency. Let λ be the roughness loss of the tunnel wall, h be the root mean square roughness of the tunnel wall, λ be the signal wavelength, d1 and d2 be the distances from the transmitter and receiver to the tunnel wall, and z be the propagation path length. Here, k represents the dust scattering loss, k is the dust type coefficient, and C is the dust concentration.

[0077] The adjustment strategies include:

[0078] To allocate cleaner frequency bands to links severely affected by residual interference;

[0079] In areas with obstructed or weak coverage, the deployment of mobile relay nodes or the establishment of direct links on the device side are triggered in advance.

[0080] The critical data stream is switched from a path with a high composite interference residual index to a predicted stable backup path.

[0081] Example 2

[0082] Dust, vibration, temperature, and humidity sensors are deployed in the downhole environment. These sensors are configured to monitor and collect downhole environmental data in real time, and data is transmitted via a wireless sensor network. The collected environmental data is sent wirelessly to an downhole aggregation node, which then uploads it to the dispatch and control center deployed on the surface via wired or wireless means.

[0083] Subsequently, the dispatch and control center deployed on the ground receives monitoring data from underground and simultaneously acquires wireless communication signal quality data. After receiving this data, the dispatch and control center performs signal separation processing on the wireless communication signals to distinguish between valid communication signals and interference signals.

[0084] Independent component analysis (ICA) algorithms can be used to separate multi-source mixed signals, thereby separating interference signals from different sources from the mixed signal.

[0085] From the separated wireless communication signal quality, characteristic interference fingerprints and interference spectra are extracted. The interference spectra can be generated by performing a Fourier transform on the signal quality data to obtain the signal's spectral distribution, thereby identifying the frequency and intensity of the interference. The characteristic interference fingerprint can be extracted by performing statistical analysis on the waveform of the interference signal, such as calculating its mean, variance, peak value, and other parameters, to form a digital feature sequence that can characterize the interference.

[0086] An interference feature library is established, which stores the feature fingerprints of various known interference modes and their corresponding interference types and intensity information. The library includes the spectral characteristics of typical electromagnetic interference generated by equipment such as motor starting, electric drill operation, and gas extraction pump operation. The real-time extracted feature interference fingerprints are compared and identified with the interference feature library. The comparison process uses a pattern matching algorithm to calculate the similarity between the real-time fingerprint and the fingerprints in the library, and selects the matching item with the highest similarity to obtain the type and intensity of signal interference.

[0087] Based on the identification results, select or combine communication links to adjust the wireless communication signal. When strong interference is detected in a specific frequency band, the current communication link can be manually switched to a preset backup frequency band. Alternatively, when the signal strength is detected to be below the threshold, the base station's transmission power can be increased to enhance signal coverage.

[0088] Finally, based on the signal after the first adjustment, the comprehensive residual interference index is calculated. This index can be obtained by weighted averaging of parameters such as the signal-to-noise ratio and bit error rate of the adjusted signal, and is used to quantify the degree of interference that still exists after the first adjustment. Based on this comprehensive residual interference index, the wireless communication signal is subjected to a second adjustment. If the comprehensive residual interference index is still high, further adjustments to the antenna azimuth angle or manual adjustments to the communication protocol parameters can be attempted.

[0089] After processing, an evaluation report is generated, which includes a comparison of signal strength before and after adjustment, the results of interference type identification, and a brief text description to record the fault monitoring and adjustment process.

[0090] By comprehensively utilizing downhole multi-source environmental sensor data and wireless communication signal quality data, signal separation is performed and characteristic interference fingerprints and interference spectra are extracted. Combined with the interference feature database, accurate identification of complex downhole interference types and intensities is achieved.

[0091] Through primary adjustment and secondary adjustment based on the comprehensive interference residual index, the wireless communication signal can be optimized in a multi-stage, adaptive manner, effectively addressing multi-source interference such as transient electromagnetic fields, equipment crosstalk, and multipath reflections underground, thereby improving the reliability and stability of 5G wireless communication in mining and ensuring mine production efficiency and operational safety.

[0092] Example 3

[0093] After receiving monitoring data and obtaining the quality of wireless communication signals, the dispatch control center needs to perform signal separation to extract characteristic interference fingerprints and interference maps from the wireless communication signal quality.

[0094] After receiving the data, the dispatch control center will synchronize the environmental data and the wireless communication signal quality data of the corresponding link according to the timestamp.

[0095] When the dust, vibration, temperature and humidity sensors deployed downhole monitor and collect downhole data in real time, and when the wireless communication equipment acquires the quality of the wireless communication signal, they will add a precise timestamp to their respective data.

[0096] After receiving these heterogeneous data, the dispatch and control center uses these timestamps as alignment benchmarks to perform precise temporal matching between environmental data and wireless communication signal quality data. Synchronous processing ensures that the associated environmental parameters and wireless communication signal quality data are highly consistent in time when performing subsequent signal separation and interference analysis.

[0097] An improved blind source separation algorithm is used to establish an environmental parameter weight correction separation matrix to separate multi-source mixed interference data. In the complex electromagnetic environment of mines, wireless communication signals are often affected by the superposition of multiple interference sources, forming mixed interference data.

[0098] The improved blind source separation algorithm not only relies on the statistical characteristics of the signal itself for separation, but also introduces synchronized environmental data as important auxiliary information into the separation process. It dynamically adjusts and optimizes the weights in the separation matrix according to environmental parameters. When environmental data shows that a certain environmental factor may cause a certain type of mechanical interference, the algorithm will adjust the separation matrix accordingly to enhance the separation ability of this type of interference source. This allows for more accurate decomposition of complex mixed interference signals into independent and identifiable interference components, providing a cleaner input for subsequent interference type identification.

[0099] Through the above technical solution, the dispatch control center can accurately synchronize environmental data and wireless communication signal quality data according to the timestamp, effectively solving the problem of inconsistent time for heterogeneous data and ensuring the accuracy and reliability of data analysis.

[0100] By employing an improved blind source separation algorithm and combining it with environmental parameter weights to correct the separation matrix, the signal separation process can be intelligently optimized by fully utilizing environmental information. This effectively addresses the complex and ever-changing mixed interference situation in mines, achieving accurate separation of multi-source interference signals. This not only significantly improves the accuracy of interference identification and avoids misjudgments caused by inconsistent data or incomplete separation, but also provides cleaner and more reliable feature data for subsequent interference type and intensity identification.

[0101] Example 4

[0102] The dispatch control center receives monitoring data and acquires the quality of wireless communication signals. It then performs signal separation and extracts characteristic interference fingerprints and interference maps from the wireless communication signal quality. In the complex mine environment, the sources of interference for wireless communication signals are diverse and dynamically changing. Simply extracting general interference maps and characteristic interference fingerprints may not be enough to comprehensively and accurately characterize the deep-seated characteristics of the interference and its correlation with environmental factors, thus affecting the accurate identification of the type and intensity of interference, which may lead to insufficient effectiveness of subsequent adjustment and processing.

[0103] Interference mapping involves performing time-frequency domain analysis on signal quality data to generate a multi-dimensional interference map that includes interference intensity, frequency, bandwidth, and time domain. Feature interference fingerprint extraction uses an attention mechanism network. It first inputs an environmental data sequence and uses the features of the interference map as the learning target to extract coupled feature fingerprints that can characterize the expected interference patterns under specific environmental state combinations.

[0104] In fault monitoring of wireless communication in mines, time-frequency domain analysis of signal quality data can capture the transient characteristics and frequency distribution of interference signals more precisely. The original time-domain signal can be converted into a time-frequency representation by methods such as short-time Fourier transform, wavelet transform, or Wigner-Ville distribution.

[0105] Interference intensity, frequency point, bandwidth, and time domain information are extracted from the time-frequency analysis results to construct a multidimensional interference spectrum. Interference intensity represents the magnitude of interference energy at a specific time and frequency point; frequency point indicates the center frequency of the interference signal; bandwidth describes the frequency range occupied by the interference signal; and time domain information reflects the duration or occurrence time of the interference signal.

[0106] By using a multi-dimensional approach, we can provide richer and more comprehensive information about interference than with a single dimension, which helps us to understand the nature of interference more deeply.

[0107] Feature interference fingerprint extraction uses an attention mechanism network. First, it receives real-time environmental data sequences from sensors monitoring dust, vibration, temperature, and humidity inside the mine, reflecting the real-time operating conditions. Simultaneously, interference features extracted from a multi-dimensional interference map are used as the learning target.

[0108] Attention mechanism networks can identify specific interference patterns expected to occur under certain combinations of environmental states by learning the complex mapping relationship between environmental data sequences and interference map features.

[0109] Ultimately, the coupling feature fingerprint output by the network is an abstract representation of the correlation between this environmental state and the interference pattern. This fingerprint not only contains the characteristics of the interference itself, but also reveals the environmental causes of the interference, thus enabling more accurate location and identification of the root cause of the interference.

[0110] Time-frequency domain analysis of wireless communication signal quality data generates a multi-dimensional interference spectrum including interference intensity, frequency point, bandwidth, and time domain. This provides more comprehensive and detailed interference information than traditional single-dimensional analysis, allowing the transient characteristics and frequency distribution of interference to be clearly presented.

[0111] By utilizing an attention mechanism network, deep correlation learning is performed on the features of underground environmental data sequences and multidimensional interference maps to extract coupled feature fingerprints that can characterize the expected interference patterns under specific environmental state combinations. These coupled feature fingerprints not only describe the appearance of interference but also reveal the intrinsic connection between interference and complex environmental factors in the mine, providing a more accurate basis for subsequent communication link adjustment and thus effectively improving the stability and reliability of the 5G mining wireless communication system.

[0112] Example 5

[0113] By analyzing the quality of wireless communication signals and extracting coupling feature fingerprints that characterize the expected interference patterns under specific environmental conditions, and comparing these fingerprints with an interference feature database, the type and intensity of interference can be identified. However, simply identifying the interference pattern and intensity may not be sufficient to directly pinpoint the specific interference source or the operating condition of the equipment causing the interference. This makes subsequent adjustments and handling less targeted, affecting the efficiency and accuracy of troubleshooting.

[0114] The interference feature library includes not only interference features at the signal level, but also environmental signal coupling feature fingerprints. By comparing the real-time extracted coupling feature fingerprints with the feature library and associating them with the equipment condition tags that match the fingerprints, the types and intensities of interference with clear origins in mining scenarios can be identified.

[0115] The construction of the interference feature library can be carried out in a laboratory environment or an actual mine by simulating or recording the interference generated by different devices under different operating conditions, and simultaneously collecting environmental data and wireless signal data, thereby establishing a mapping relationship between interference patterns, environmental conditions and equipment operating conditions.

[0116] Interference characteristics in the signal dimension refer to the interference features obtained from the analysis of the wireless communication signal itself, such as the center frequency, bandwidth, power spectral density, modulation type, duration, angle of arrival, and other parameters of the interference signal. These characteristics directly describe the physical properties of the interference signal.

[0117] Environmental signal coupling feature fingerprint refers to a unique identifier that is learned through machine learning models by combining environmental data such as downhole dust, vibration, temperature, and humidity, as well as wireless communication signal quality data. It can reflect the correlation between one or more environmental factors and a specific interference mode under specific environmental conditions. It is not only a feature of the signal itself, but also a comprehensive reflection of the interaction between signal features and environmental factors.

[0118] Equipment status tags refer to information such as the operating status, location, power, and operating mode of a specific device that generates interference, which is associated with each known coupling feature fingerprint in the interference feature library. The tags are stored together with the corresponding coupling feature fingerprints when the interference feature library is established.

[0119] In practical applications, the dispatch control center continuously receives environmental data and wireless communication signal quality data uploaded by downhole sensors. Based on this real-time data, it uses a pre-trained attention mechanism network to generate the current coupling feature fingerprint.

[0120] The generated coupling feature fingerprints are then matched with known coupling feature fingerprints stored in the interference feature library for pattern matching or similarity calculation. This is achieved through various pattern recognition algorithms, such as distance-based classifiers, support vector machines, or neural networks, to find the best-matching known interference pattern.

[0121] When the real-time extracted coupled feature fingerprint successfully matches a known fingerprint in the feature library, the system automatically extracts the device condition label associated with the matching fingerprint.

[0122] Through the above correlation process, not only can the type and intensity of the interference be determined, but more importantly, the specific equipment causing the interference and its current operating conditions can be clearly identified.

[0123] Through the above technical solution, the interference feature library not only includes interference features at the signal dimension, but also further incorporates environmental signal coupling feature fingerprints, greatly enriching the dimensions and context of interference information. When the coupling feature fingerprint extracted in real time is compared with this enhanced feature library, it can be directly associated with the equipment condition label that matches the fingerprint. This enables the system to not only identify the type and intensity of interference, but also significantly improve the accuracy and efficiency of fault location, avoid blind troubleshooting, improve the stability and reliability of the communication system, and ensure the safety and efficiency of downhole operations.

[0124] Example 6

[0125] Based on the identification results, the wireless communication signal is adjusted by selecting or combining communication links. The adjustment process involves selecting or combining adjustment schemes from a preset strategy mapping table according to the interference type, adjusting the beamwidth, the relay node to which the device is pointing, and multi-beam coordination.

[0126] The dispatch control center receives monitoring data and acquires wireless communication signal quality, performs signal separation, extracts characteristic interference fingerprints and interference maps from the wireless communication signal quality, and establishes an interference feature database. After comparing and identifying the characteristic interference fingerprints with the interference feature database, the system will perform an adjustment process based on the specific interference type identified.

[0127] Interference types can be diverse, such as narrowband interference, broadband interference, pulse interference, co-channel interference, and adjacent-channel interference. By adjusting according to the interference type, the system can identify the specific interference mode and take the most effective suppression measures for that mode.

[0128] The pre-defined strategy mapping table is a database that stores the relationship between different interference types and their corresponding adjustment schemes. It is pre-built through simulation, experimentation, or expert experience and includes optimized adjustment strategies for various known interference modes.

[0129] For example, for narrowband interference at a specific frequency, the mapping table may indicate adjustment to a spare frequency band; for strong interference in a specific direction, it may indicate adjustment of beam direction or power.

[0130] The system can select the best-matching single adjustment scheme from the policy mapping table based on the identified interference type, or select multiple adjustment schemes to combine when the interference is complex, in order to achieve the best interference suppression effect.

[0131] Adjusting the beamwidth refers to the angular range of the area where the antenna's radiated energy is concentrated. By adjusting the beamwidth, the range and directionality of signal coverage can be changed. When an interference source is located in a specific direction, the beamwidth can be narrowed to avoid the direction of the interference source, thereby improving the signal-to-noise ratio. Alternatively, when wider coverage is required, the beamwidth can be appropriately increased by adjusting the phase and amplitude of the antenna array.

[0132] In the complex environment of a mine, where signals may be attenuated due to obstruction, relay nodes are used to receive and forward signals, extending coverage or bypassing obstacles. Adjusting the device's orientation allows the system to switch the communication path to a relay node with better signal quality and less interference when the current communication link is interfered with or has poor signal quality. Alternatively, the device's antenna can be adjusted to align with a more suitable relay node, establishing a more stable communication link.

[0133] Multi-beam coordination refers to the simultaneous operation of multiple antenna beams to jointly optimize communication performance. It includes technologies such as beamforming, beam switching, and beam tracking. When there are multiple interference sources or multiple users need to be served simultaneously, multi-beam coordination can be used to allocate independent beams to signals from different users or different directions. Alternatively, intelligent beamforming can be used to effectively suppress interference from specific directions while maintaining communication quality.

[0134] Through the above technical solution, this application can intelligently select or combine the most suitable adjustment scheme from the preset strategy mapping table according to the specific interference type identified, thereby achieving precise one-time adjustment processing of wireless communication signals, improving the ability of 5G wireless communication systems to cope with diverse interference in complex mining environments, ensuring the stability and reliability of communication, avoiding the negative impact of blind adjustment, and thus effectively solving the problem of poor adjustment effect of traditional methods in complex interference scenarios.

[0135] Example 7

[0136] The wireless communication signal undergoes secondary adjustment processing, which includes: deploying spectrum sensing units at key underground locations to construct a real-time broadband electromagnetic environment map; establishing an underground roadway model based on underground materials, equipment layout, and roadway routes; dynamically updating the underground roadway model according to the tunneling progress and equipment movement; performing ray tracing simulation based on the underground roadway model to calculate the underground obstruction area and predict the weak coverage area within a preset time period; and establishing a roadway signal propagation model, combining interference spectrum, comprehensive interference residual index, and underground obstruction or weak coverage areas to generate adjustment strategies for secondary processing of the wireless communication signal.

[0137] Deploying spectrum sensing units at key locations underground enables real-time monitoring and analysis of the complex electromagnetic spectrum environment. These spectrum sensing units can be independent sensor nodes or integrated into existing mining wireless base stations or relay equipment. They can continuously scan a preset wide frequency range and collect spectrum data including signal strength, noise level, interference source frequency points, and bandwidth.

[0138] By processing and fusing these real-time collected data, a real-time broadband electromagnetic environment map can be constructed. This map intuitively reflects the electromagnetic field distribution and interference status in different areas and frequency bands underground, providing basic data support for subsequent signal adjustments.

[0139] Simultaneously, an underground roadway model is established by combining underground materials, equipment layout, and roadway alignment. This model is a digital representation of the underground physical environment.

[0140] Downhole material information can be obtained from geological exploration data or construction drawings, including material parameters of rocks, coal seams, support structures, etc., which have different attenuation and reflection characteristics for wireless signal propagation.

[0141] Equipment layout and roadway routes can be obtained through mine CAD drawings, BIM models, laser scanning, or inertial navigation systems to accurately describe the geometry of the underground space.

[0142] To cope with the dynamic changes in the mine environment, the underground roadway model is dynamically updated according to the tunneling progress and equipment movement. When the tunneling machine advances, the roadway model will extend accordingly; when large mining equipment moves, the model will update its position and its impact on signal propagation, ensuring that the model always maintains a high degree of consistency with the actual environment.

[0143] Ray tracing simulation is performed based on a dynamically updated underground tunnel model. By simulating the reflection, refraction, diffraction, and scattering paths of radio waves in a complex three-dimensional environment, the propagation loss and arrival intensity of signals at various locations underground are accurately calculated.

[0144] Based on the simulation results, the obstruction zone underground, that is, the area where the signal strength is lower than the communication threshold, is calculated.

[0145] By combining the dynamic update information of the underground tunnel model, the system can predict weak coverage areas within a preset time period and anticipate potential signal blind spots based on the tunneling plan for the next few hours or days.

[0146] Establishing a tunnel signal propagation model is a mathematical description of the propagation law of wireless signals in the special environment of underground tunnels. Based on empirical formulas, semi-empirical corrections or physical models, it takes into account the characteristics of the underground environment such as narrowness, multiple reflections and high attenuation, and can quickly assess signal coverage and interference.

[0147] Finally, the interference spectrum, comprehensive interference residual index, and various information such as the downhole obstruction or weak coverage areas obtained through simulation are comprehensively analyzed to generate a comprehensive adjustment strategy. The wireless communication signal is then processed in a secondary manner. This adjustment strategy may include, but is not limited to, adjusting the frequency band, transmission power, beamforming, deploying mobile relay nodes, or establishing direct links on the device side, in order to achieve the best communication effect.

[0148] By deploying spectrum sensing units at key underground locations and constructing a real-time broadband electromagnetic environment map, the real-time status of the underground electromagnetic environment can be fully grasped. This secondary adjustment processing scheme overcomes the limitations of relying solely on the interference residual index for adjustment, effectively copes with the impact of the complex and ever-changing underground physical environment on wireless communication signals, significantly improves the coverage, signal quality, and system stability of 5G mining wireless communication, and ensures the continuity and reliability of critical services.

[0149] Example 8

[0150] By establishing an underground tunnel model, conducting ray tracing simulations, and combining interference maps and comprehensive residual interference indices, an adjustment strategy was generated to perform secondary processing on wireless communication signals, allocating cleaner frequency bands to links severely affected by residual interference; in advance, mobile relay node deployment or equipment-side direct link establishment was triggered in obstructed or weakly covered areas; and critical data streams were switched from paths with high comprehensive residual interference indices to predicted stable backup paths.

[0151] When the system identifies that certain communication links still have a high residual interference index even after one adjustment, indicating that the link is still under severe interference, it will allocate cleaner frequency bands to these links with severe residual interference. By monitoring the spectrum occupancy in real time, it will identify frequency bands with low interference levels or that are not being fully utilized. Then, through the network management system, it will instruct the relevant communication devices to switch to these cleaner frequency bands for communication, thereby fundamentally avoiding the source of interference and improving communication quality.

[0152] Simultaneously, in response to the dynamic changes in the underground environment, when a blocked or weak coverage area is predicted, the deployment of mobile relay nodes or the establishment of direct links on the device side will be triggered in advance. Blocked or weak coverage areas refer to areas predicted by underground roadway models and ray tracing simulations where the signal strength may be insufficient to support reliable communication. When the system predicts that such areas will appear within a preset time period in the future, it can schedule mobile communication relay devices to move automatically or remotely to the predicted weak coverage area for deployment to expand the signal coverage range, or instruct devices near or inside the predicted area to establish direct links on the device side, that is, to form a local network by utilizing the direct communication capabilities between devices, bypassing the central base station, thereby maintaining communication in the weak coverage area.

[0153] To ensure the transmission of critical business operations, critical data streams are switched from paths with high composite interference residual index to predicted stable backup paths. Critical data streams refer to real-time data that is crucial to the safety and production efficiency of downhole operations, such as remote control commands, safety monitoring data, or emergency call information. When the system detects that the composite interference residual index of the current communication path carrying these critical data streams is too high and may affect the reliability of data transmission, it will identify one or more backup communication paths with low current interference levels and expected to remain stable in the future, based on the roadway signal propagation model, interference spectrum, and real-time assessment of the downhole environment.

[0154] The system will then seamlessly switch critical data streams to these predicted stable backup paths to ensure timely and accurate transmission of critical information and avoid business interruptions or data loss due to interference.

[0155] Through the above adjustment strategies, this application can specifically solve a variety of complex problems in mining wireless communication, ensure the continuity of communication, and switch the critical data stream from a path with a high comprehensive interference residual index to a predictable and stable backup path, which greatly ensures the transmission security and real-time performance of important data.

[0156] Example 9

[0157] like Figure 2 As shown, a 5G wireless communication system for mining includes:

[0158] The data acquisition module is used to deploy dust, vibration, temperature and humidity sensors downhole to monitor and collect downhole data in real time and upload it to the dispatch and control center deployed on the ground.

[0159] The feature extraction module is used by the dispatch control center to receive monitoring data and obtain wireless communication signal quality, perform signal separation, and extract feature interference fingerprints and interference maps from the wireless communication signal quality.

[0160] The interference identification module establishes an interference feature library, compares the characteristic interference fingerprints with the interference feature library to identify the type and intensity of signal interference;

[0161] The primary processing module, based on the identification results, selects or combines communication links to perform a primary adjustment processing on the wireless communication signal;

[0162] The secondary processing module calculates the comprehensive interference residual index based on the processed signal, performs secondary adjustment processing on the wireless communication signal based on the comprehensive interference residual index, and generates an evaluation report.

[0163] The embodiments described above are merely preferred embodiments of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this application. It should be noted that those skilled in the art can make various modifications, improvements, and substitutions without departing from the concept of this application, and these all fall within the protection scope of this application.

Claims

1. A method for fault monitoring of 5G wireless communication in mining, characterized in that, Includes the following steps: Dust, vibration, temperature and humidity sensors are deployed underground to monitor and collect underground data in real time and upload it to the dispatch and control center deployed on the ground; The dispatch and control center receives monitoring data and acquires the quality of wireless communication signals, performs signal separation, and extracts characteristic interference fingerprints and interference maps from the quality of wireless communication signals. Establish an interference feature database, compare and identify the interference fingerprints with the interference feature database, and obtain the type and intensity of signal interference. Based on the identification results, select or combine communication links to perform an adjustment process on the wireless communication signal; Based on the processed signal, the comprehensive interference residual index is calculated. The wireless communication signal is then subjected to secondary adjustment processing based on the comprehensive interference residual index, and an evaluation report is generated.

2. The 5G mining wireless communication fault monitoring method according to claim 1, characterized in that, After receiving the data, the scheduling and control center will synchronize the environmental data and the wireless communication signal quality data of the corresponding link according to the timestamp. An improved blind source separation algorithm is then used to establish an environmental parameter weight correction separation matrix to separate multi-source mixed interference data.

3. The 5G mining wireless communication fault monitoring method according to claim 1, characterized in that, The interference spectrum is generated by performing time-frequency domain analysis on signal quality data, resulting in a multi-dimensional interference spectrum including interference intensity, frequency point, bandwidth, and time domain. Feature interference fingerprint extraction uses an attention mechanism network. First, an environmental data sequence is input, and the features of the interference pattern are used as the learning target to extract coupled feature fingerprints that can characterize the interference patterns expected to be generated under a specific combination of environmental states.

4. The 5G mining wireless communication fault monitoring method according to claim 3, characterized in that, The interference feature library includes not only interference features at the signal dimension, but also environmental signal coupling feature fingerprints. The real-time extracted coupling feature fingerprints are compared with the feature database, and the equipment operating condition labels that match the fingerprints are associated to identify the specific types and intensities of interference in mining scenarios.

5. A 5G mining wireless communication fault monitoring method according to claim 1, characterized in that, The first adjustment process involves selecting or combining adjustment schemes from a preset strategy mapping table based on the interference type, adjusting the beamwidth, the relay node to which the device is pointing, and multi-beam coordination.

6. The 5G mining wireless communication fault monitoring method according to claim 1, characterized in that, The comprehensive interference residual index is calculated using a weighted summation formula: in, The normalized composite interference residual index, To interfere with residual strength, This represents the signal-to-noise ratio loss value. α is the bit error rate, and β and γ are weighting coefficients; When the overall residual interference index is less than the threshold, an evaluation report is generated directly; when the overall residual interference index is greater than or equal to the threshold, a secondary adjustment process is triggered.

7. A 5G mining wireless communication fault monitoring method according to claim 6, characterized in that, The secondary adjustment process includes: Deploy spectrum sensing units at key underground locations to construct a real-time broadband electromagnetic environment map. Combine this with underground materials, equipment layout, and roadway routes to establish an underground roadway model. The underground roadway model is dynamically updated based on the tunneling progress and equipment movement. Ray tracing simulation is performed based on the underground tunnel model to calculate the underground obstruction area and predict the weak coverage area within a preset time period in the future. A tunnel signal propagation model is established, and by combining the interference spectrum, the comprehensive interference residual index, and the underground obstruction or weak coverage areas, an adjustment strategy is generated to perform secondary processing on the wireless communication signal.

8. A 5G mining wireless communication fault monitoring method according to claim 7, characterized in that, The tunnel signal propagation model is constructed based on the ray tracing method, and the mining loss factor is calculated using the following formula: in, For mining loss factor, Let d be the free space loss, d be the propagation distance, and f be the signal frequency. Let λ be the roughness loss of the tunnel wall, h be the root mean square roughness of the tunnel wall, λ be the signal wavelength, d1 and d2 be the distances from the transmitter and receiver to the tunnel wall, and z be the propagation path length. Here, k represents the dust scattering loss, k is the dust type coefficient, and C is the dust concentration.

9. A 5G mining wireless communication fault monitoring method according to claim 7, characterized in that, The adjustment strategy includes: To allocate cleaner frequency bands to links severely affected by residual interference; In areas with obstructed or weak coverage, the deployment of mobile relay nodes or the establishment of direct links on the device side are triggered in advance. The critical data stream is switched from a path with a high composite interference residual index to a predicted stable backup path.

10. A 5G wireless communication system for mining, characterized in that, include: The data acquisition module is used to deploy dust, vibration, temperature and humidity sensors downhole to monitor and collect downhole data in real time and upload it to the dispatch and control center deployed on the ground. The feature extraction module is used by the dispatch control center to receive monitoring data and obtain wireless communication signal quality, perform signal separation, and extract feature interference fingerprints and interference maps from the wireless communication signal quality. The interference identification module establishes an interference feature library, compares the characteristic interference fingerprints with the interference feature library to identify the type and intensity of signal interference; The primary processing module, based on the identification results, selects or combines communication links to perform a primary adjustment processing on the wireless communication signal; The secondary processing module calculates the comprehensive interference residual index based on the processed signal, performs secondary adjustment processing on the wireless communication signal based on the comprehensive interference residual index, and generates an evaluation report.

Images