A communication method and system for hydrogen sensor network of hydrogen storage cavity

Abstract

The application provides a communication method and system for a hydrogen sensor network of a hydrogen storage cavity, and relates to the technical field of wireless sensor communication. The method comprises the following steps: collecting medium wall interface physical parameters of the hydrogen storage cavity at different depths and positions; establishing an electromagnetic influence evaluation index system based on the interface physical parameters, which is used for quantifying the contribution of different interface characteristic parameters to the absorption reflection and scattering effect of wireless signals; arranging fixed surface topography monitoring devices at different depths of the hydrogen storage cavity, which are used for collecting time sequence change data of the medium wall roughness parameters; constructing a correlation analysis model of roughness evolution and signal attenuation trend based on the time sequence change data and communication quality data of the hydrogen sensor network, which is used for arranging the hydrogen sensor network nodes, setting communication parameters and determining a maintenance cycle. The application effectively improves the reliability and stability of wireless communication in a complex geological environment.

Description

Technical Field

[0001] This invention relates to the field of wireless sensor communication technology, and in particular to a communication method and system for a hydrogen sensor network in a hydrogen storage cavity. Background Technology

[0002] Underground hydrogen storage is a crucial technological approach for achieving large-scale hydrogen energy storage and stable operation of energy systems, holding key strategic significance in ensuring national energy security and promoting carbon neutrality. Salt cavern hydrogen storage chambers are widely used due to their excellent sealing and large capacity. To monitor safety parameters such as pressure, temperature, and leakage within the chamber in real time, a wireless sensor network needs to be deployed to transmit data. In a salt cavern hydrogen storage environment, wireless signals need to propagate over long distances within the chamber and penetrate the walls to communicate with the ground station. However, the chamber walls are affected by groundwater seepage, salt dissolution, and recrystallization, causing the wall roughness to continuously change over time. Furthermore, the wall surface moisture and mineral composition distribution are uneven. These interface physical parameters directly affect the absorption, reflection, and scattering of electromagnetic waves, resulting in significant differences in signal attenuation at different locations and times. In actual operation, the same sensor network initially communicates normally, but as the chamber is used for an extended period, the signals of some nodes gradually weaken or even cease, leading to an increased data packet loss rate at the ground station and a decrease in the reliability of the monitoring system.

[0003] The temporal evolution of wall roughness has become the most critical dynamic factor affecting the quality of wireless communication. When the wall becomes rougher due to long-term moisture or salt creep, electromagnetic wave scattering intensifies, and signal energy loss increases along the propagation path. Especially in the depths of the cavity or at corners, signal strength may rapidly drop below the threshold. At this point, even increasing the transmit power is difficult to fully compensate for, because the attenuation caused by roughness changes has non-linear characteristics and is correlated with the wall conditions at other locations, forming signal coverage blind spots or unstable areas throughout the cavity. Maintenance personnel cannot predict in advance which nodes are about to fail, nor can they accurately determine the specific impact of wall roughness changes on the communication link, resulting in frequent but limited maintenance of the sensor network and a shortened overall lifespan.

[0004] How to achieve reasonable configuration of sensor node layout and communication parameters in an environment where wall roughness continuously evolves and interacts with other interface physical parameters, while responding promptly to the signal attenuation trend caused by roughness changes, has become a key issue in ensuring the long-term reliable operation of wireless sensor networks for salt cavern hydrogen storage chambers. Summary of the Invention

[0005] This invention provides a communication method and system for a hydrogen sensor network in a hydrogen storage cavity, aiming to effectively improve the reliability and stability of wireless communication in complex geological environments, extend the lifespan of the sensor network, and thus support safe and efficient monitoring of the hydrogen storage cavity.

[0006] In a first aspect, the present invention provides a communication method for a hydrogen sensor network in a hydrogen storage cavity, mainly comprising: The physical parameters of the medium wall interface of the hydrogen storage cavity at different depths and locations are collected. The physical parameters of the interface include the roughness, humidity and mineral composition of the medium wall. An electromagnetic influence evaluation index system is established based on the aforementioned interface physical parameters. This system is used to quantify the contribution of different interface characteristic parameters to the absorption, reflection, and scattering effects of wireless signals. Fixed surface morphology monitoring devices are arranged at different depths in the hydrogen storage cavity. The fixed surface morphology monitoring devices are used to collect time-series variation data of the roughness parameters of the medium wall. Based on the time-series variation data and the communication quality data of the hydrogen sensor network, a correlation analysis model is constructed to determine the relationship between roughness evolution and signal attenuation trend. The correlation analysis model is used to arrange the nodes of the hydrogen sensor network, set communication parameters, and determine the maintenance cycle.

[0007] Furthermore, the acquisition of physical parameters of the medium wall interface at different depths and locations of the hydrogen storage cavity includes: The surface roughness parameters of the medium are obtained, and the values ​​of the surface roughness parameters of the medium are determined by surface scanning; The humidity parameter is obtained by measuring the humidity of the medium wall surface using a humidity sensor and obtaining the humidity parameter value. The mineral composition parameters are obtained, and the mineral composition parameter values ​​are determined by scanning the wall of the medium using a mineral composition analysis device. The interface physical parameters are formed based on the surface roughness parameter, the humidity parameter, and the mineral composition parameter.

[0008] Furthermore, the establishment of an electromagnetic influence evaluation index system based on the interface physical parameters includes: Obtain the physical parameters of the interface and the measured data of electromagnetic wave propagation; A multi-dimensional correlation analysis is performed based on the interface physical parameters and the measured electromagnetic wave propagation data. The multi-dimensional correlation analysis is used to determine the contribution of the medium wall roughness parameter to the wireless signal scattering effect, the contribution of the humidity parameter to the wireless signal absorption effect, and the contribution of the mineral composition parameter to the wireless signal reflection effect. A standardized evaluation model for the electromagnetic impact evaluation index system is formed based on the contribution rate. The evaluation results are output through the standardized evaluation model, and these results are used to deploy the hydrogen sensor network nodes and set the communication parameters.

[0009] Furthermore, the multi-dimensional correlation analysis based on the interface physical parameters and the measured electromagnetic wave propagation data includes: The scattering contribution index is calculated based on the surface roughness parameter of the medium, and the scattering contribution index is determined by a preset scattering formula; The absorption contribution index is calculated for the humidity parameter, and the absorption contribution index is determined by a preset absorption formula. The reflection contribution index is calculated based on the mineral composition parameters, and the reflection contribution index is determined by a preset reflection formula; The absorption contribution index and the reflection contribution index are calculated based on the scattering contribution index, and the total contribution index is obtained by weighted fusion.

[0010] Furthermore, the fixed surface morphology monitoring device is used to collect time-series variation data of the surface roughness parameters of the medium, including: The surface morphology of the medium wall is periodically scanned, and the current value of the medium wall roughness parameter is generated; The difference between the current roughness parameter value and the historical roughness parameter value of the medium wall is recorded to obtain the time-series variation data, which includes the evolution of hydrogen corrosion surface undulation, medium dissolution texture, and stress microcrack propagation parameters.

[0011] Furthermore, the construction of the correlation analysis model between roughness evolution and signal attenuation trend based on the time-series variation data and the communication quality data of the hydrogen sensor network includes: Real-time acquisition of communication quality data, including signal strength attenuation rate and bit error rate; Correlation analysis is performed on the time-series variation data and the communication quality data to determine the relationship between roughness evolution parameters and signal attenuation parameters; The correlation analysis model is generated based on the correlation relationship. The correlation analysis model is used to predict the signal attenuation trend and output the network maintenance cycle of the hydrogen sensor and dynamically adjust the communication parameters.

[0012] Furthermore, the correlation analysis based on the time-series change data and the communication quality data includes: Extract roughness evolution feature vectors from the time-varying data; Extract signal attenuation feature vectors from the communication quality data; The correlation coefficient between the roughness evolution feature vector and the signal attenuation feature vector is calculated by using a preset correlation coefficient. If the correlation coefficient is greater than a preset threshold, the association is confirmed and the association parameters are recorded.

[0013] Furthermore, the correlation analysis model is used to deploy the hydrogen sensor network nodes, set communication parameters, and determine the maintenance cycle, including: The placement of the hydrogen sensor network nodes is determined based on the evaluation results of the electromagnetic influence evaluation index system. The communication parameters, including transmit power and multipath signal processing parameters, are dynamically adjusted based on the prediction results of the correlation analysis model.

[0014] Secondly, the present invention provides a communication system for a hydrogen sensor network in a hydrogen storage cavity, comprising: The acquisition module is used to acquire the physical parameters of the medium wall interface at different depths and positions of the hydrogen storage cavity. The interface physical parameters include the roughness, humidity and mineral composition of the medium wall. A module is established to create an electromagnetic influence evaluation index system based on the interface physical parameters. The electromagnetic influence evaluation index system is used to quantify the contribution of different interface characteristic parameters to the absorption, reflection and scattering effects of wireless signals. The module is used to arrange fixed surface morphology monitoring devices at different depths in the hydrogen storage cavity. The fixed surface morphology monitoring devices are used to collect time-series variation data of the roughness parameters of the medium wall. A construction module is used to construct a correlation analysis model between roughness evolution and signal attenuation trend based on the time-series variation data and the communication quality data of the hydrogen sensor network. The correlation analysis model is used to arrange the nodes of the hydrogen sensor network, set communication parameters, and determine the maintenance cycle.

[0015] Furthermore, the acquisition module is specifically used to: acquire the surface roughness parameter of the medium wall, and determine the surface roughness parameter value of the medium wall by surface scanning; acquire the humidity parameter, and measure the humidity of the medium wall by a humidity sensor to obtain the humidity parameter value; acquire the mineral composition parameter, and scan the medium wall by a mineral composition analysis device to determine the mineral composition parameter value; and form the interface physical parameters based on the surface roughness parameter value, the humidity parameter value, and the mineral composition parameter value.

[0016] The technical solutions provided by the embodiments of the present invention may include the following beneficial effects: This invention collects interfacial physical parameters such as wall roughness, humidity, and mineral composition at different depths and locations, establishes a standardized evaluation model that comprehensively considers the influence of each interfacial physical parameter on electromagnetic waves, quantifies the contribution of each parameter to signal propagation, and thus guides the optimal arrangement of sensor nodes and the configuration of initial communication parameters. Simultaneously, a fixed surface morphology monitoring device is deployed to continuously collect temporal variation data of wall roughness, and correlation analysis is performed with real-time communication quality data of the sensor network to construct a predictive model of roughness evolution and signal attenuation trends. This enables dynamic adjustment of communication parameters and determination of maintenance cycles, thereby effectively improving the reliability and stability of wireless communication in complex geological environments, extending the lifespan of the sensor network, and supporting the safe and efficient monitoring of hydrogen storage cavities. Attached Figure Description

[0017] Figure 1 This is a flowchart illustrating a communication method for a hydrogen sensor network in a hydrogen storage cavity, as provided in an embodiment of the present invention.

[0018] Figure 2 This is a flowchart illustrating a communication method for a hydrogen sensor network in a hydrogen storage cavity, as provided in an embodiment of the present invention.

[0019] Figure 3 This is a schematic diagram of the module structure of a communication system for a hydrogen sensor network in a hydrogen storage cavity, according to an embodiment of the present invention. Detailed Implementation

[0020] The technical solutions of the embodiments of the present invention will be clearly and thoroughly described below with reference to the accompanying drawings. The described embodiments are merely some embodiments of the present invention.

[0021] like Figures 1-3 The present invention provides a communication method for a hydrogen sensor network in a hydrogen storage cavity, which may specifically include: Step S100: Collect the physical parameters of the medium wall interface of the hydrogen storage cavity at different depths and positions.

[0022] The interface physical parameters include the surface roughness, humidity, and mineral composition of the medium. These parameters are used to analyze the propagation characteristics of wireless signals within the medium.

[0023] In one implementation, multi-point measurement devices deployed within the cavity are used to collect interface physical parameters in real time, ensuring coverage of key areas within the cavity. The collected data on wall roughness, humidity, and mineral composition are matched using a pre-established electromagnetic wave propagation influence database to determine the specific impact of each parameter on wireless signal propagation characteristics, forming a preliminary signal propagation characteristic distribution map. The characteristic change trends of key areas are extracted from the signal propagation characteristic distribution map, and combined with measured wireless signal strength data at different locations within the cavity, the relationship between interface physical parameter changes and signal attenuation is compared and analyzed to construct a correlation mapping table between parameters and signal propagation characteristics. This correlation mapping table is used to perform a multi-dimensional evaluation of the wireless signal propagation characteristics within the hydrogen storage cavity. For differences in medium wall characteristics at different depths and locations, corresponding signal propagation characteristic reports are generated for subsequent optimization and adjustment of sensor network communication quality.

[0024] In one possible implementation, the interfacial physical parameters of the medium wall at different depths and locations within the hydrogen storage cavity can be acquired in real time using a multi-point measurement device deployed within the cavity. This multi-point measurement device is a network system integrating multiple sensors, including a roughness scanner, a humidity probe, and a mineral composition spectrometer. These devices are fixed to the cavity wall and spaced apart to cover the upper, middle, and lower regions. For example, five data collection points could be deployed in the upper region to ensure that parameters such as the micron-level fluctuations in wall roughness, the percentage of humidity, and the salt-rock ratio of mineral composition are accurately recorded. This approach allows for precise capture of the heterogeneity of the cavity environment, as wall parameters in the salt cavern hydrogen storage environment vary with depth, leading to uneven wireless signal propagation. This acquisition method improves the accuracy of subsequent analysis.

[0025] For example, when matching the collected data on wall roughness, humidity, and mineral composition using a pre-established database of electromagnetic wave propagation effects, this database is a storage structure built based on historical experimental data. It contains signal absorption, reflection, and scattering coefficients corresponding to various parameter combinations. For instance, the database stores the signal attenuation rate when the roughness is at a medium level, the humidity is high, and the mineral composition is pure salt rock. The matching process compares the input parameters with the database entries using a query algorithm to determine the degree of influence, such as an absorption contribution of 30% and a reflection contribution of 40%, thereby forming a preliminary signal propagation characteristic distribution map. This distribution map is a two-dimensional or three-dimensional visualization that marks the characteristic values ​​at different locations in the cavity. This approach can bring the beneficial effect of quickly quantifying the influence, avoiding errors from manual calculations, and providing a basis for signal optimization.

[0026] In one possible implementation, when extracting the characteristic change trend of key areas from the signal propagation characteristic distribution map, comparative analysis is performed using measured wireless signal strength data at different locations within the cavity. For example, the upward trend of characteristics caused by the increase in roughness in the upper region is extracted and compared with the measured signal strength decrease data. Relationships are analyzed, such as a 2dB attenuation caused by each unit increase in roughness. A correlation mapping table between parameters and signal propagation characteristics is constructed. This table is in tabular form, with rows representing parameter types and columns representing propagation characteristic indicators. This approach can reveal the beneficial effect of dynamic relationships because parameter evolution in the salt cave environment affects communication stability. Constructing a correlation mapping table is beneficial for predicting potential problems.

[0027] For example, when using an association mapping table to evaluate the propagation characteristics of wireless signals within a hydrogen storage cavity in multiple dimensions, a corresponding signal propagation characteristic report is generated for the differences in the characteristics of the medium wall at different depths and locations. For instance, for the high humidity area at the bottom, the evaluation shows an enhanced scattering effect, and the report lists optimization suggestions such as adjusting the sensor spacing. This can bring about the beneficial effect of targeted decision-making and ensure the reliable adjustment of the communication quality of the sensor network in the salt cavern hydrogen storage environment.

[0028] Step S200: Establish an electromagnetic influence evaluation index system based on the interface physical parameters.

[0029] The electromagnetic influence evaluation index system forms a standardized evaluation model by quantitatively analyzing the contribution of different interface characteristic parameters to the absorption, reflection and scattering effects of wireless signals. The standardized evaluation model is used for decision-making on sensor node placement and communication parameter settings.

[0030] Surface roughness, humidity, and mineral composition parameters are collected at different depths and locations within the salt cavern to form an interface physical parameter dataset. This dataset includes roughness, humidity, and mineral composition distribution values ​​for each location. Roughness, humidity, and mineral composition parameters are extracted from this dataset, and the contribution of each parameter to the absorption, reflection, and scattering effects of the wireless signal is calculated, yielding the electromagnetic influence contribution value of each interface characteristic parameter. The electromagnetic influence contribution values ​​of each interface characteristic parameter are weighted and fused to form a comprehensive electromagnetic influence evaluation index. This index quantifies the overall impact of different interface characteristics on the wireless signal. Based on this comprehensive electromagnetic influence evaluation index, a standardized evaluation model is constructed. This model directly maps the interface physical parameters to sensor node placement locations and communication parameter settings, and is used for decision-making regarding sensor node placement and communication parameter settings.

[0031] In one possible implementation, the process of collecting surface roughness, humidity, and mineral composition parameters at different depths and locations within a salt cave to form an interface physical parameter dataset is accomplished by deploying fixed surface morphology monitoring devices and humidity sensors. For example, a monitoring device can be placed at the top of the salt cave to obtain roughness values ​​representing the degree of surface undulation, while a mineral analyzer can be used at the bottom to obtain mineral composition distribution values ​​representing the proportion of components such as sodium chloride in the salt rock. This ensures that the dataset comprehensively covers spatial variability, which is beneficial to the accuracy of subsequent analysis because the collected roughness, humidity, and mineral composition distribution values ​​directly reflect the potential impact of the interface on electromagnetic waves, thus providing a reliable basis for electromagnetic influence assessment.

[0032] The process of extracting roughness parameters, humidity parameters, and mineral composition parameters from the interface physical parameter dataset and calculating the contribution of each parameter to the wireless signal absorption, reflection, and scattering effects involves quantitative analysis. For example, the contribution of the roughness parameter to the scattering effect is calculated by assessing the degree of signal diffusion caused by surface undulations. The contribution of the humidity parameter to the absorption effect is determined by measuring the attenuation effect of water molecules on electromagnetic waves. The contribution of the mineral composition parameter to the reflection effect is evaluated based on the difference in dielectric constant of the components. This approach can accurately decompose the influence weight of each parameter, which is beneficial for identifying the dominant factors. The obtained electromagnetic influence contribution value can highlight the dominant role of roughness in scattering, thereby supporting the construction of comprehensive indicators.

[0033] In one possible implementation, the process of weightedly fusing the electromagnetic influence contribution values ​​of various interface characteristic parameters to form a comprehensive electromagnetic influence evaluation index is accomplished by summing the contribution values ​​using preset weight coefficients. For example, roughness is given a higher weight to reflect its significant impact on signal attenuation. The comprehensive electromagnetic influence evaluation index obtained after fusion quantifies the overall impact of different interface characteristics on wireless signals. This approach integrates multi-dimensional information and is beneficial for providing a unified evaluation standard, as the index can intuitively show the amplification effect of the overall impact when humidity increases, thus facilitating the standardization of subsequent models.

[0034] The process of constructing a standardized evaluation model based on the comprehensive electromagnetic influence evaluation index involves mapping the index to decision parameters. For example, a linear mapping function can be used to map high-impact indexes to dense sensor node placement and low-power communication parameter settings. The standardized evaluation model constructed in this way directly maps interface physical parameters to sensor node placement locations and communication parameter settings for decision-making on sensor node placement and communication parameter settings. This optimizes network performance and improves communication quality because the model can dynamically adjust node positions based on evaluation results to avoid high-roughness areas, thereby achieving reliable communication of the sensor network in the salt cavern hydrogen storage environment.

[0035] Step S300: Fixed surface morphology monitoring devices are arranged at different depths in the hydrogen storage cavity. The fixed surface morphology monitoring devices are used to collect time-series variation data of the roughness parameters of the medium wall.

[0036] Laser scanning devices are fixedly arranged at different depths within the hydrogen storage cavity to acquire initial three-dimensional point cloud data of the wall surface. The laser scanning devices continuously scan the wall surface to acquire subsequent three-dimensional point cloud data, from which surface undulation changes and texture evolution characteristics are obtained. From these surface undulation changes and texture evolution characteristics, parameters related to surface undulation changes caused by hydrogen corrosion and texture evolution caused by medium dissolution are obtained. From these parameters, parameters related to microcrack propagation caused by stress are obtained, forming temporal variation data of the wall roughness parameters.

[0037] Optical scanning devices are fixedly arranged at different depths within the hydrogen storage cavity to acquire initial three-dimensional point cloud data of the wall surface. The optical scanning devices continuously scan the wall surface to acquire subsequent three-dimensional point cloud data. The surface undulation changes and texture evolution characteristics are obtained by comparing the subsequent three-dimensional point cloud data with the initial three-dimensional point cloud data. Surface undulation change parameters caused by hydrogen corrosion and texture evolution parameters caused by medium dissolution are obtained from these surface undulation changes and texture evolution characteristics, forming temporal variation data of the wall roughness parameters.

[0038] In one embodiment, the optical scanning device uses a laser as a light source to emit light onto the wall surface and receives the reflected signals to generate three-dimensional point cloud data. This method reconstructs the fine structure of the wall surface through the dense distribution of the point cloud, thereby providing a high-resolution benchmark for subsequent comparisons. For example, the initial three-dimensional point cloud data records the original height distribution of the wall surface. Subsequent three-dimensional point cloud data are collected at the same location, and the surface undulation changes and texture evolution characteristics are calculated through point-to-point deviation. This continuous acquisition ensures that the changing characteristics directly reflect the influence of the hydrogen environment. From the surface undulation changes and texture evolution characteristics, parameters corresponding to the surface undulation changes caused by hydrogen corrosion in areas of increased local height are separated, while parameters corresponding to the texture evolution caused by medium dissolution in areas of decreased overall smoothness are separated. This separation is based on feature scale differences to quantify parameters, thereby accurately capturing wall deformation caused by different mechanisms.

[0039] It should be noted that the parameters of microcrack propagation caused by stress corresponding to edge discontinuities are further extracted from the surface undulation change parameters caused by hydrogen corrosion and the texture evolution parameters caused by medium dissolution. This extraction achieves parameter integration through feature coherence judgment, and finally forms the time series change data of wall roughness parameters. The continuous accumulation of this time series data supports roughness evolution tracking and improves monitoring reliability.

[0040] In one possible implementation, the 3D point cloud data processing prioritizes the removal of noise points to preserve the true wall details. This post-processing comparison more accurately reflects the undulations and helps distinguish the effects of hydrogen corrosion and medium dissolution, thereby improving the accuracy of parameter extraction and providing a reliable basis for microcrack propagation parameters.

[0041] Specifically, the temporal variation data, through the superposition of multiple point clouds, shows the gradual increase trend of roughness. This trend is directly derived from the accumulation and fusion of parameters such as surface undulation changes, texture evolution, and microcrack propagation. This is beneficial for comprehensively quantifying the dynamic process of wall roughness and supporting the assessment of wall condition under long-term hydrogen storage environment.

[0042] Step S400: Based on the time-series variation data and the communication quality data of the hydrogen sensor network, construct a correlation analysis model of roughness evolution and signal attenuation trend. The correlation analysis model is used to arrange the nodes of the hydrogen sensor network, set communication parameters, and determine the maintenance cycle.

[0043] Fixed surface morphology monitoring devices deployed at different depths within the salt cavern continuously acquire temporal variation data of the wall roughness. This temporal variation data includes parameters such as surface undulation changes caused by hydrogen corrosion, texture evolution caused by salt dissolution, and microcrack propagation caused by stress. The changes in these parameters at different time points are recorded for subsequent correlation analysis. The acquired temporal variation data of wall roughness is aligned with real-time communication quality data monitored in the sensor network, establishing a correspondence between the two and generating a dataset of roughness variation and communication quality fluctuations, providing a foundation for subsequent trend analysis. Roughness variation characteristics and communication quality fluctuation characteristics are extracted from the generated dataset. A correlation analysis model of wall roughness evolution and signal attenuation trend is constructed using a pre-established regression analysis method. This model quantifies the impact of roughness variation on signal attenuation and outputs prediction results. Based on the prediction results output by the correlation analysis model, the adjustment range of communication parameters and the planning basis for maintenance cycles in the sensor network are determined to ensure the stability of communication quality during the roughness evolution process, thus completing the dynamic management of sensor network communication quality in the salt cavern hydrogen storage environment.

[0044] In salt cavern hydrogen storage environments, fixed surface morphology monitoring devices typically employ laser scanners or optical imaging sensors, which are permanently installed at different depths on the salt cavern wall. These devices measure wall roughness by periodically emitting beams or capturing images. For example, the device scans the wall once per hour to obtain data on changes in surface undulations caused by hydrogen corrosion, such as an increase in surface height difference from an initial 0.5 mm to 1.2 mm. This helps to capture temporal changes to support subsequent analysis, enabling timely identification of corrosion trends and thus optimizing maintenance strategies.

[0045] The alignment process for acquiring temporal variation data of wall roughness and communication quality data involves synchronizing their timestamps. In one embodiment, if the roughness data shows increased texture evolution at 10:00 AM, while the communication quality data records a 20% decrease in signal strength, a correspondence is established through timeline alignment to generate a dataset. This reveals the direct impact of roughness changes on signal fluctuations, providing a reliable basis for trend analysis and contributing to improved stability of the sensor network.

[0046] When extracting features from the generated dataset, the pre-established regression analysis method is a statistical modeling technique used to describe linear or nonlinear relationships between variables.

[0047] Specifically, this method first calculates the covariance between roughness variation characteristics, such as the average roughness value, and communication quality fluctuation characteristics, such as the signal attenuation rate, and then fits the model parameters using the least squares method.

[0048] For example, the model can be in the form of Y = aX + b, where Y represents the signal attenuation trend, X represents the roughness evolution value, and a and b are fitting coefficients. The correlation analysis model constructed in this way can quantify the degree of influence and output prediction results, which is helpful in predicting potential communication problems so as to intervene in advance.

[0049] The range of communication parameter adjustments is determined based on the prediction results output by the correlation analysis model. In one possible implementation, if the model predicts that an increase in roughness will cause signal attenuation to exceed a threshold, the transmit power is increased by 10% from the standard value, and a maintenance cycle of once a month is planned. This ensures the stability of communication quality during the roughness evolution process, which is beneficial for the dynamic management of the sensor network in the salt cavern hydrogen storage environment, thereby maintaining the reliable operation of the overall system.

[0050] The process of collecting physical parameters of the medium wall interface at different depths and positions of the hydrogen storage cavity, including the wall roughness, humidity, and mineral composition, includes obtaining the wall roughness parameters through the fixed surface morphology monitoring device, and obtaining the humidity and mineral composition in combination with a humidity sensor and a mineral composition analysis device.

[0051] Fixed surface morphology monitoring devices are deployed at different depths and locations within the hydrogen storage cavity to acquire time-series data of wall roughness parameters. This time-series data is combined with humidity data collected by a humidity sensor to obtain a joint distribution parameter of wall interface roughness and humidity. This joint distribution parameter is then combined with mineral composition data collected by a mineral composition analysis device to obtain a comprehensive dataset of physical parameters of the medium wall interface. This comprehensive dataset is used to quantify the influence of wall roughness, humidity, and mineral composition on electromagnetic wave propagation, and the physical parameters of the medium wall interface at different depths and locations within the hydrogen storage cavity are obtained from the quantification results.

[0052] For example, the process of arranging fixed surface morphology monitoring devices at different depths and locations within a hydrogen storage cavity involves selecting devices made of corrosion-resistant materials to adapt to the hydrogen environment within the salt cavern, ensuring that the devices can be permanently fixed to the wall surface to collect data. This arrangement allows for the accurate capture of wall changes, as the salt cavern wall surface will undulate due to hydrogen corrosion. The device acquires time-series data of roughness parameters via laser scanning, thus providing continuous basic information for subsequent analysis.

[0053] In one possible implementation, when acquiring time-series data of wall roughness parameters from the fixed surface morphology monitoring device, optical imaging technology can be used to record changes in surface texture at fixed time intervals. For example, the device can be deployed at shallow depths to capture the increase in roughness caused by rapid dissolution, while at deeper locations, stress-induced microcrack propagation can be monitored. This approach can provide a comprehensive understanding of wall evolution and support stable communication of the sensor network.

[0054] Specifically, the process of combining the time series data of wall roughness parameters with the humidity data collected by the humidity sensor includes a data fusion step. First, the time series of roughness data is aligned with the humidity readings to form a joint dataset. This combination can reveal how humidity amplifies the effect of roughness on signal scattering, bringing about the beneficial effect of optimizing communication parameters.

[0055] For example, in high humidity areas, increased roughness leads to enhanced signal reflection. By combining the roughness and humidity distribution parameters of the wall interface in this way, the evaluation can be supported from multiple directions. For example, the amplification effect of seasonal humidity changes on roughness can be analyzed from a time dimension, and the differences in distribution at different depths can be compared from a spatial dimension. These examples support each other and jointly demonstrate the importance of parameter combination.

[0056] In one possible implementation, when the joint distribution parameters of surface roughness and humidity are combined with mineral composition data collected by a mineral composition analysis device, the proportions of sodium chloride and sulfate in the salt rock are detected in real time using a spectral analysis device. These data are then incorporated into the joint parameters to form a comprehensive dataset of physical parameters of the medium surface. This approach can bring about the beneficial effect of comprehensively quantifying the influence of the interface on electromagnetic waves, because mineral composition affects signal absorption. For example, absorption is enhanced when the sodium chloride content is high. When combined with roughness and humidity, the influence mechanism can be deduced from the corrosion and dissolution aspects. These directions support each other and ensure the integrity of the dataset.

[0057] Specifically, the process of using a comprehensive dataset of physical parameters of the medium wall interface to quantify the influence of wall roughness, humidity, and mineral composition on electromagnetic wave propagation involves constructing a correlation model. First, the contribution of each parameter is calculated, such as the weight of roughness on scattering, the weight of humidity on absorption, and the weight of mineral composition on reflection. Then, the results are integrated into a quantification result. From the quantification result, the physical parameters of the medium wall interface at different depths and locations of the hydrogen storage cavity are obtained, which can bring about the beneficial effect of accurate decision support.

[0058] For example, in the upper part of the cavity, roughness dominates scattering, while in the lower part, mineral components dominate absorption. Through multi-faceted analysis, such as simulated signal attenuation scenarios and experimental verification, these examples support the feasibility of the method from both theoretical and practical perspectives, ensuring the logical coherence of the entire process.

[0059] The interface physical parameters are subjected to multi-dimensional correlation analysis with the measured electromagnetic wave propagation data. This multi-dimensional correlation analysis is used to verify the influence of the interface characteristic parameters on wireless signal propagation.

[0060] Interface physical parameters, including wall roughness, humidity, and mineral composition, were acquired from different depths and locations within the salt cavern. These parameters were recorded in real-time using pre-deployed monitoring devices, forming an initial interface characteristic dataset. This initial dataset was then paired with measured electromagnetic wave propagation data collected at the same depths and locations to establish a correspondence table between interface physical parameters and electromagnetic wave propagation characteristics. Multi-dimensional correlation features were extracted from this table, and statistical tools were used to quantify the influence of interface physical parameters on electromagnetic wave propagation characteristics, generating a distribution of the contribution of each parameter to wireless signal absorption, reflection, and scattering effects. Based on this contribution distribution, a verification basis for the influence of interface characteristic parameters on wireless signal propagation was constructed. By comparing signal change trends under different parameter combinations, key influencing factors were identified, providing data support for assessing the communication quality of sensor networks in salt cavern hydrogen storage environments.

[0061] In one possible implementation, the interface physical parameters are acquired using fixed monitoring devices positioned at different depths and locations within the salt cavern. These include recording surface roughness using a laser scanner to measure surface undulations, measuring moisture content using a resistive sensor, and identifying the main components of the salt rock using a spectrometer, thus forming an initial dataset of interface characteristics. This dataset acquisition ensures that parameters at the same location are recorded synchronously, facilitating subsequent direct correlation with measured electromagnetic wave propagation data.

[0062] For example, given an initial dataset of interface characteristics, wireless sensor nodes are deployed at the same depth and location to emit electromagnetic wave signals of known frequencies. Simultaneously, the receiving end records propagation characteristics such as signal strength, phase shift, and time delay. These are then paired according to location and timestamp to establish a correspondence table between interface physical parameters and electromagnetic wave propagation characteristics. This pairing process directly associates each interface parameter sample with a set of measured propagation values, avoiding correlation bias caused by data misalignment.

[0063] Specifically, multi-dimensional correlation features are extracted from the correspondence table, and the linear relationship strength between each interface physical parameter and signal attenuation is calculated using correlation coefficients. Simultaneously, the contribution ratio of each parameter to the reflection coefficient and scattering loss is calculated, generating a distribution of the parameters' contribution to the absorption, reflection, and scattering effects of wireless signals. This quantification process highlights the trend that scattering effects increase with increasing roughness and absorption effects intensify with increasing humidity, providing objective evidence for verifying the impact.

[0064] In one embodiment, the measured signal variation trend under different parameter combinations is compared with the expected propagation path loss for the contribution distribution. If the parameter with a high contribution corresponds to significant signal attenuation, the influence of that parameter on wireless signal propagation is confirmed. Through cross-validation of multiple sets of location data, a verification basis for the influence of interface characteristic parameters on wireless signal propagation is formed. This verification basis directly supports the assessment of the impact of interface characteristics on sensor network communication quality in the salt cavern hydrogen storage environment, ensuring the targeted nature of parameter adjustments.

[0065] Based on the collected interface physical parameters, an electromagnetic influence evaluation index system is established that comprehensively considers the wall roughness, humidity, and mineral composition. This includes quantifying the contribution of the wall roughness to the signal scattering effect, the contribution of the humidity to the signal absorption effect, and the contribution of the mineral composition to the signal reflection effect.

[0066] Interfacial physical parameters, including surface roughness, humidity, and mineral composition data, were obtained from the walls of the salt cave at different depths and locations. These parameters were continuously monitored using fixed monitoring devices deployed within the salt cave, forming an initial interface feature dataset. For this dataset, the influence of surface roughness on signal scattering, humidity on signal absorption, and mineral composition on signal reflection were quantified. By comparing measured signal propagation data under different parameters, quantitative indicators of each parameter's influence were constructed. Based on these quantitative indicators, a comprehensive evaluation system was established, weighting and integrating the contributions of surface roughness, humidity, and mineral composition to the scattering, absorption, and reflection effects of electromagnetic signals to form a unified electromagnetic impact evaluation standard. This standard serves as the basis for subsequent adjustments to sensor network communication parameters. Based on this comprehensive evaluation system and combined with measured electromagnetic wave propagation data within the salt cave, the contribution of each parameter was dynamically calibrated to ensure the evaluation system adapts to the evolution of interface physical parameters over time, continuously optimizing the accuracy of the electromagnetic impact evaluation.

[0067] For example, when acquiring interface physical parameters in a salt cavern hydrogen storage environment, data can be continuously collected by deploying fixed monitoring devices such as laser scanners and humidity sensors. This allows for real-time capture of changes in wall roughness from smooth to undulating, thereby forming an initial interface feature dataset, which is beneficial for subsequent analysis of signal propagation stability.

[0068] In one possible implementation, the influence of wall roughness on signal scattering effect is quantified for the initial interface feature dataset. For example, the roughness parameter is divided into three levels: low, medium, and high. The percentage of scattering loss is calculated by comparing the corresponding measured signal propagation data. This approach can accurately assess the scattering contribution and is beneficial for optimizing the placement of sensor nodes to reduce communication interference.

[0069] The impact of humidity on signal absorption can be quantified by simulating absorption rate tests under different humidity conditions, such as 10% to 90%, to construct a quantitative index. This helps to identify the peak signal attenuation caused by humidity and is beneficial for dynamically adjusting communication power to maintain network reliability.

[0070] To quantify the effect of mineral composition on signal reflection, we can analyze the contribution of the ratio of sodium chloride and sulfate in salt rocks to the reflection coefficient. For example, the enhanced reflection effect in high sodium chloride regions leads to multipath propagation of signals. Doing so can construct accurate reflection indices, which is beneficial for selecting appropriate frequency bands to reduce reflection interference.

[0071] When establishing a comprehensive evaluation system based on quantitative indicators, the contribution of each parameter is weighted and integrated, such as assigning a weight of 30% to roughness, 40% to humidity, and 30% to mineral composition, forming a unified electromagnetic influence evaluation standard. This is beneficial for providing a basis for decision-making to adjust the communication parameters of the sensor network and ensure overall communication quality. The weighted integration process involves multiplying the quantitative values ​​of scattering, absorption, and reflection effects by their respective weights and then summing them to obtain a total evaluation score. For example, a score higher than a threshold indicates a high risk of interference. This approach comprehensively considers the interaction of multiple factors and is beneficial for predicting potential communication failures.

[0072] When performing dynamic calibration based on a comprehensive evaluation system, the contribution is adjusted by combining measured electromagnetic wave propagation data, such as signal strength attenuation curves. For example, if the measured humidity effect exceeds expectations, its weight is increased. This helps to adapt to the evolution of interface parameters over time and improves the accuracy of the evaluation.

[0073] For example, dynamic calibration can also be incorporated into time series analysis to track parameter change trends, such as an increase in roughness of 0.5 units per month leading to increased scattering. This ensures the adaptability of the system, which is beneficial for extending the maintenance cycle of the sensor network and optimizing resource allocation.

[0074] The application of the comprehensive evaluation system is examined from multiple perspectives. For example, when the parameters of deep salt caves change rapidly, humidity is prioritized for monitoring, while in shallower layers, the focus is on mineral composition. This multi-directional support can jointly enhance the robustness of the system and is beneficial for comprehensively addressing the diversity of electromagnetic influences at different depths.

[0075] In one possible implementation, an example illustrates the mutual support of the quantification process, such as using the quantification results of roughness to calibrate the humidity absorption model, and conversely, using humidity data to verify the mineral reflection hypothesis. This forms a closed-loop verification, which helps to enhance the reliability of the overall evaluation and reduce errors.

[0076] The standardized evaluation model outputs evaluation results, which indicate the optimal placement of sensor nodes and the initial communication parameter configuration.

[0077] Parameters of wall roughness, humidity, and mineral composition at different depths and locations within the salt cavern were collected. The contributions of these parameters to electromagnetic wave absorption, reflection, and scattering were then obtained. These contributions were quantified, weighted, and fused to obtain a comprehensive electromagnetic influence index (EMII), which characterizes the degree of wireless signal attenuation at each location. Based on the EMII, multi-dimensional correlation corrections were performed using measured electromagnetic wave propagation data to obtain a corrected EMII. The region with the lowest signal attenuation was determined from the corrected EMII as the optimal placement location for sensor nodes, and the initial communication parameter configuration for the corresponding location was determined, forming the evaluation results.

[0078] In one possible implementation, the process of collecting wall roughness parameters, humidity parameters, and mineral composition parameters at different depths and locations of salt caves involves scanning the wall with a laser scanner to obtain roughness data, for example, scanning at a salt cave location at a depth of 100 meters to obtain surface undulation height data. The process of obtaining the contribution of the parameters to electromagnetic wave absorption is to determine the absorption contribution by measuring the energy loss of the salt rock to electromagnetic waves of a specific frequency. This can bring the beneficial effect of accurately assessing signal attenuation, because increased roughness leads to more scattering, which in turn affects communication quality.

[0079] It should be noted that the process of obtaining the reflection contribution from the humidity parameter includes using a dielectric constant tester to detect the effect of humidity on wave reflection. For example, the reflection contribution increases in areas with higher humidity, which helps to understand how a humid environment enhances signal reflection and thus optimizes sensor placement.

[0080] For example, the process of obtaining scattering contribution from mineral composition parameters involves identifying mineral types using a spectrometer and calculating their contribution to wave scattering. For instance, locations with higher sodium chloride content have lower scattering contributions, which is supported by the absorption contribution, as mineral composition influences overall electromagnetic behavior, thus providing a comprehensive parameter basis. The process of quantifying and weighting the collected absorption, reflection, and scattering contributions involves multiplying these contributions by preset weights and then summing them to obtain a comprehensive electromagnetic influence index. For example, the absorption contribution weight is 0.4, reflection is 0.3, and scattering is 0.3. This approach provides a valuable benefit by offering a more accurate indicator of wireless signal attenuation, as the fused index reflects the interaction of multiple factors, thus supporting location optimization.

[0081] In one possible implementation, the process of characterizing the degree of wireless signal attenuation at each location by the comprehensive electromagnetic influence index is to identify high attenuation areas by comparing index values ​​at different locations. For example, locations with high index values ​​have severe attenuation. This is supported by the aforementioned parameter acquisition to ensure the accuracy of the evaluation.

[0082] It should be noted that the process of multi-dimensional correlation correction based on the comprehensive electromagnetic influence index and the measured data of electromagnetic wave propagation involves adjusting the index using correlation analysis methods. For example, when the measured data shows that the actual attenuation is higher than the index, a correction factor is added. This can bring about a more realistic and beneficial effect to the corrected comprehensive electromagnetic influence index, because the correction takes into account dynamic environmental factors, thereby improving the reliability of the model.

[0083] For example, the process of obtaining the corrected comprehensive electromagnetic influence index is achieved through an iterative correction loop, such as multiple adjustments when the initial index deviates significantly from the measured data. This facilitates the transition from theory to practice and connects with the fusion process. The process of determining the region with the lowest signal attenuation from the corrected comprehensive electromagnetic influence index as the optimal placement location for sensor nodes involves selecting the region with the lowest index value, such as a low-index region at a depth of 200 meters. This complements the correction process to ensure the scientific validity of the location selection.

[0084] It should be noted that the process of determining the initial communication parameter configuration for the corresponding location includes setting the transmission power and frequency according to the low attenuation area. For example, reducing the power in the low attenuation area can save energy. Doing so can have a beneficial effect on the evaluation results because the configuration optimizes the communication efficiency, thereby enabling the stable operation of the sensor network.

[0085] The fixed surface morphology monitoring device continuously collects time-series change data of the wall roughness parameters, including periodically scanning the wall surface morphology and generating roughness quantification indicators.

[0086] A fixed surface morphology monitoring device operates continuously at a predetermined position on the salt cave wall, periodically acquiring high-resolution surface morphology data of local areas of the wall using laser scanning to obtain three-dimensional point cloud data. Height fluctuation information is extracted from the 3D point cloud data, the root mean square value of the surface height deviation is calculated, and a quantitative index of wall roughness is generated. The corresponding acquisition time is recorded to form a time series. Linear regression fitting is performed on the roughness quantitative index in the time series to determine the evolution trend of the roughness parameters over time, obtaining the time-series variation data of the wall roughness parameters. The time-series variation data of the roughness parameters is correlated with the synchronous sensor network communication signal intensity data to quantify the dynamic impact of roughness evolution on signal attenuation, used for continuous acquisition of the time-series variation data of the wall roughness parameters.

[0087] In one possible implementation, a fixed surface morphology monitoring device is deployed at key locations on the salt cave wall. A laser scanner performs high-resolution scans on local areas of the wall at fixed time intervals. This scanning utilizes the principle of laser beam reflection to capture the surface microstructure, thereby obtaining three-dimensional point cloud data of the wall. This is beneficial for capturing surface changes caused by hydrogen corrosion in real time, ensuring the continuity and accuracy of monitoring, and thus providing reliable basic data for subsequent roughness analysis.

[0088] For example, when extracting surface height undulation information from the acquired 3D point cloud data, the point cloud is first denoised to remove noise interference. Then, the height deviation of each point relative to the reference plane is calculated, and the root mean square value of these deviations is obtained as a roughness quantification index. At the same time, the acquisition time points are marked to form a time series. This method helps to quantify the wall texture evolution process, improve the sensitivity to the salt rock dissolution effect, and thus improve the accuracy of the overall evaluation system.

[0089] In one possible implementation, linear regression is applied to the roughness quantification index in the time series, and a trend line is fitted using the least squares method to describe the linear change of the roughness parameter over time. This fitting process considers the time variable as the independent variable and the roughness value as the dependent variable to obtain the evolution trend parameter, which is helpful in predicting future roughness changes and optimizing the maintenance decisions of the sensor network.

[0090] For example, the Pearson correlation coefficient can be calculated by comparing the obtained roughness parameter time-series variation data with the sensor network communication signal strength data collected at the same time to quantify the dynamic impact of roughness evolution on signal attenuation. This correlation analysis reveals how the increase in roughness leads to enhanced signal scattering, thus providing a basis for dynamically adjusting communication parameters, ensuring stable network operation and extending equipment life.

[0091] Specifically, when multiple fixed surface morphology monitoring devices are deployed at different depths in the salt cavern, each device independently collects data and transmits it wirelessly to the central processor. This deployment method, combined with the monitoring of the temporal changes in wall roughness, is beneficial for building a comprehensive quantitative evaluation system for roughness, supporting the dynamic impact assessment of electromagnetic properties, thereby improving the operation and maintenance efficiency of the hydrogen storage environment.

[0092] In one possible implementation, when the laser scanning acquisition of 3D point cloud data encounters wall humidity interference, the device can integrate a humidity compensation module to adjust the scanning parameters. This compensation maintains data accuracy by correcting the laser reflection intensity, which is beneficial for continuously acquiring reliable surface morphology information in humid environments and further enhances the temporal variation analysis of roughness parameters.

[0093] For example, the time series formed by calculating the root mean square value of surface height deviation can be used to visualize roughness evolution charts. These charts show index fluctuations on the time axis, which helps to intuitively identify microcrack propagation trends and thus correlate them with communication quality monitoring data to provide decision support to avoid signal attenuation problems.

[0094] Specifically, the evolution trend determined by linear regression fitting can be extended to nonlinear models such as polynomial regression, which can better fit the data in complex corrosion scenarios, help capture non-uniform changes, improve the prediction accuracy of the dynamic impact of salt cave walls, and thus contribute to the optimization of sensor networks.

[0095] The time-series change data is stored in a monitoring database, which supports time-series queries of the roughness evolution trend.

[0096] A fixed surface morphology monitoring device continuously collects time-series variation data of surface roughness parameters at different depths within a salt cavern. This time-series variation data includes surface undulation parameters caused by hydrogen corrosion and texture evolution parameters caused by salt dissolution. The time-series variation data, after being appended with a collection timestamp, is transmitted to a monitoring database. This database uses a columnar storage structure to persistently store the roughness evolution trend. The monitoring database establishes a time-series index for the roughness evolution trend, which supports grouped time-series queries by depth and location. The time-series query retrieves the roughness evolution trend from the monitoring database and is used for correlation with real-time monitoring data on sensor network communication quality.

[0097] Specifically, the acquisition of surface roughness parameters in salt cavern environments can be achieved using fixed surface morphology monitoring devices. These devices are typically installed at different depths inside the salt cavern and use high-precision scanning probes to periodically scan the surface walls, acquiring data on the microscopic undulations and changes in the surface.

[0098] For example, a monitoring device is installed at the top and bottom of the salt cave to record surface irregularities caused by hydrogen corrosion and changes in the dissolution texture of the salt rock under the influence of humidity. This data acquisition method ensures the comprehensiveness of the data, provides a reliable basis for subsequent analysis, and helps to promptly detect the potential impact of changes in the wall condition on communication signals.

[0099] In one possible implementation, the collected time-series variation data is timestamped before being transmitted to the monitoring database. The addition of timestamps ensures that each data set corresponds to a specific collection time, facilitating subsequent tracing of trends in chronological order. The monitoring database employs a columnar storage structure, which is particularly suitable for processing time-series data because it efficiently compresses and queries roughness parameter changes over continuous time periods. The advantage of this storage structure is that when it's necessary to extract roughness data at a specific depth within a certain timeframe, the query speed is significantly improved, providing technical support for real-time monitoring and rapid response.

[0100] It should be noted that building a time-series index based on the roughness evolution trend is to further optimize data query efficiency. The time-series index can be grouped according to the depth and location of salt caves.

[0101] For example, monitoring point data from the top, middle, and bottom can be categorized and stored separately. This way, when analyzing changes in wall roughness in a specific area, the relevant data group can be directly located via the index, avoiding the time consumption of a full table scan. This grouped indexing method significantly improves data access efficiency, especially when dealing with large amounts of data, providing a convenient data foundation for subsequent communication quality monitoring.

[0102] Furthermore, the roughness evolution trend data obtained based on time-series queries can be correlated with the communication quality monitoring data of sensor networks for analysis.

[0103] For example, if a significant increase in surface roughness is observed at a certain depth in a salt cave over a period of time, it might be inferred that this increases the reflection and scattering effects on wireless signals, leading to increased signal attenuation. By comparing this data with actual signal strength changes, the impact of the surface condition on communication can be determined more accurately. This correlation analysis provides crucial reference for the maintenance and parameter adjustment of sensor networks, thereby ensuring communication stability.

[0104] The sensor network monitors communication quality data in real time, including signal strength, attenuation rate, and bit error rate. The communication quality data is correlated with the time-series change data through correlation analysis.

[0105] Time-series data on roughness variations were acquired from the walls of salt caves at different depths and locations. This data included surface undulations caused by hydrogen corrosion and texture evolution parameters resulting from salt dissolution. Changes in these parameters were continuously recorded using a fixed surface morphology monitoring device, forming a wall roughness variation dataset. For this dataset, real-time communication quality data from a sensor network (including signal strength and attenuation rate) was analyzed. A correlation mapping between these two metrics was established through data comparison, generating a table showing the relationship between roughness variation and communication quality impact. Specific parameters affecting signal attenuation due to roughness variation were extracted from this table, and a correlation analysis model was constructed to quantify the impact of different roughness variations on signal strength, generating a queryable impact metric. For this impact metric, the bit error rate (BER) in the communication quality data was monitored in real-time. The BER data was compared with the impact metric for roughness variation to determine the correlation characteristics between the communication quality data and the time-series variation data, completing the real-time monitoring and correlation analysis of the sensor network's communication quality data.

[0106] In one possible implementation, the process of acquiring temporal variation data of wall roughness from different depths and locations within the salt cave can be achieved by deploying multiple fixed surface morphology monitoring devices inside the salt cave. These devices can continuously capture surface undulation changes caused by hydrogen corrosion and texture evolution features resulting from salt dissolution.

[0107] For example, several monitoring points can be set at the top and bottom of the salt cave to record the changes in the microstructure of the wall surface under different environmental conditions, forming a comprehensive dataset of roughness variations. The advantage of this approach is that it comprehensively reflects the dynamic changes in the internal environment of the salt cave, providing reliable basic data for subsequent analysis.

[0108] Specifically, the process of combining roughness variation datasets with sensor network communication quality data can be achieved by acquiring signal strength and attenuation rate data in real time.

[0109] For example, sensor nodes can be deployed at different locations within a salt cave to record the intensity changes of wireless signals during transmission. These changes can then be time-aligned with surface roughness data at the same locations to establish a correlation mapping between the two. The advantage of this approach is that it can intuitively reveal how roughness changes affect signal propagation, laying the data foundation for subsequent mapping table generation.

[0110] In one possible implementation, the process of extracting parameters on the impact of roughness variation on signal attenuation from the correspondence table and constructing a correlation analysis model can be accomplished by performing multi-dimensional comparisons of the data.

[0111] For example, analyzing the decrease in signal strength as surface roughness increases allows for the quantification of the impact of different roughness levels on the signal, generating queryable impact metrics. This provides a scientific basis for predicting and optimizing communication quality.

[0112] Specifically, the process of monitoring changes in bit error rate in real time and performing correlation comparisons using influence metrics can be achieved by embedding a real-time monitoring module in the sensor network.

[0113] For example, continuously recording the bit error rate data during communication and matching it with the impact metric of roughness changes can determine the correlation characteristics between the two. The advantage of this method is that it can promptly identify potential causes of communication quality degradation, providing data support for the dynamic adjustment of the sensor network. Through these various implementation methods, the correlation analysis between communication quality data and time-series variation data can be effectively achieved, improving the operational reliability of the sensor network in the salt cavern hydrogen storage environment.

[0114] The correlation analysis model predicts the signal attenuation trend based on the roughness evolution, and the prediction result triggers the dynamic update of adaptive power control and multipath signal processing parameters.

[0115] Temporal variation data of salt cavern wall roughness parameters are continuously collected from a fixed surface morphology monitoring device. The surface undulation changes caused by hydrogen corrosion, texture evolution caused by salt dissolution, and microcrack propagation caused by stress are quantified to obtain a roughness evolution sequence. This sequence is then jointly processed with real-time monitoring data of sensor network communication quality to establish a correlation between roughness parameter changes and signal attenuation, determining the signal attenuation trend corresponding to the roughness evolution sequence. The signal attenuation trend is predicted based on the roughness evolution sequence, resulting in a predicted signal attenuation curve. The required transmit power adjustment for adaptive power control is determined based on this predicted curve. The predicted signal attenuation curve is then used to directly update multipath signal processing parameters and adaptive power control parameters, achieving dynamic adjustment of transmit power and signal interference suppression.

[0116] For example, time-series variation data of salt cave wall roughness parameters are continuously collected from a fixed surface topography monitoring device. This device is typically installed at different depths in the salt cave and captures surface details through optical scanning or laser ranging. It quantifies the surface undulation changes caused by hydrogen corrosion. For instance, in areas with high hydrogen concentration, surface undulations may evolve from an initial smooth state to an uneven surface, thus affecting signal reflection. This quantification helps to identify potential communication attenuation risks early, bringing the beneficial effect of improving the stability of sensor networks.

[0117] In one possible implementation, the texture evolution caused by salt rock dissolution is recorded as changes in texture density indices. For example, the dissolution process leads to the formation of fine grooves on the wall, which increases signal scattering. This allows the evolutionary patterns to be revealed through time-series data analysis. This approach provides accurate input data for subsequent correlation models and is beneficial for optimizing maintenance strategies.

[0118] Specifically, the propagation of microcracks caused by stress is quantified by measuring crack length and density. For example, under high pressure, microcracks can expand from millimeters to centimeters, amplifying signal interference. Continuously acquiring roughness evolution sequences lays the foundation for communication quality assessment. This method helps capture dynamic changes in real time, preventing sudden signal interruptions. Joint processing of the roughness evolution sequence with real-time monitoring data of sensor network communication quality—for example, combining fluctuation data in the sequence with signal strength logs—establishes a correlation between roughness parameter changes and signal attenuation. This correlation is reflected through correlation calculations; for instance, as roughness increases, signal attenuation increases accordingly. Determining the signal attenuation trend corresponding to the roughness evolution sequence reveals the impact of environmental factors on communication and is beneficial for predicting potential problems.

[0119] In one possible implementation, the signal attenuation trend is predicted based on the roughness evolution sequence to obtain a predicted signal attenuation change curve. For example, time series analysis methods are used to simulate future trends. This curve shows the attenuation changing from gradual to abrupt. The amount of transmit power adjustment required for adaptive power control is determined based on the predicted signal attenuation change curve. This ensures that the power adjustment accurately matches environmental changes, resulting in the beneficial effects of reducing energy consumption and improving reliability.

[0120] For example, the multipath signal processing parameters and adaptive power control parameters are directly updated using the predicted signal attenuation curve. For instance, the filter settings are adjusted according to the curve to suppress reflection interference, thereby achieving dynamic adjustment of transmit power and signal interference suppression. The role of this update mechanism is to adapt to the real-time evolution of the salt cave environment, thereby maintaining continuous communication of the sensor network.

[0121] This invention provides a communication system for a hydrogen sensor network in a hydrogen storage cavity, comprising: The acquisition module is used to acquire the physical parameters of the medium wall interface at different depths and positions of the hydrogen storage cavity. The interface physical parameters include the roughness, humidity and mineral composition of the medium wall. A module is established to create an electromagnetic influence evaluation index system based on the interface physical parameters. The electromagnetic influence evaluation index system is used to quantify the contribution of different interface characteristic parameters to the absorption, reflection and scattering effects of wireless signals. The module is used to arrange fixed surface morphology monitoring devices at different depths in the hydrogen storage cavity. The fixed surface morphology monitoring devices are used to collect time-series variation data of the roughness parameters of the medium wall. A construction module is used to construct a correlation analysis model between roughness evolution and signal attenuation trend based on the time-series variation data and the communication quality data of the hydrogen sensor network. The correlation analysis model is used to arrange the nodes of the hydrogen sensor network, set communication parameters, and determine the maintenance cycle.

[0122] Furthermore, the acquisition module is specifically used to: acquire the surface roughness parameter of the medium wall, and determine the surface roughness parameter value of the medium wall by surface scanning; acquire the humidity parameter, and measure the humidity of the medium wall by a humidity sensor to obtain the humidity parameter value; acquire the mineral composition parameter, and scan the medium wall by a mineral composition analysis device to determine the mineral composition parameter value; and form the interface physical parameters based on the surface roughness parameter value, the humidity parameter value, and the mineral composition parameter value.

[0123] It should be noted that the modules or units provided in the embodiments of the present invention have the same implementation principle and technical effects as those in the aforementioned method embodiments. For the sake of brevity, the specific working process of the modules described above can be referred to the corresponding process in the aforementioned method embodiments, and will not be repeated here.

[0124] The above description is merely a preferred embodiment of one or more embodiments of this specification and is not intended to limit the scope of one or more embodiments of this specification. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of one or more embodiments of this specification should be included within the protection scope of one or more embodiments of this specification.

Claims

1. A communication method for a hydrogen sensor network in a hydrogen storage cavity, characterized in that, include: The physical parameters of the medium wall interface of the hydrogen storage cavity at different depths and locations are collected. The physical parameters of the interface include the roughness, humidity and mineral composition of the medium wall. An electromagnetic influence evaluation index system is established based on the aforementioned interface physical parameters. This system is used to quantify the contribution of different interface characteristic parameters to the absorption, reflection, and scattering effects of wireless signals. Fixed surface morphology monitoring devices are arranged at different depths in the hydrogen storage cavity. The fixed surface morphology monitoring devices are used to collect time-series variation data of the roughness parameters of the medium wall. Based on the time-series variation data and the communication quality data of the hydrogen sensor network, a correlation analysis model is constructed between roughness evolution and signal attenuation trend. The correlation analysis model is used to arrange the nodes of the hydrogen sensor network, set communication parameters, and determine the maintenance cycle. The establishment of an electromagnetic influence evaluation index system based on the interface physical parameters includes: Obtain the physical parameters of the interface and the measured data of electromagnetic wave propagation; A multi-dimensional correlation analysis is performed based on the interface physical parameters and the measured electromagnetic wave propagation data. The multi-dimensional correlation analysis is used to determine the contribution of the medium wall roughness parameter to the wireless signal scattering effect, the contribution of the humidity parameter to the wireless signal absorption effect, and the contribution of the mineral composition parameter to the wireless signal reflection effect. A standardized evaluation model for the electromagnetic impact evaluation index system is formed based on the contribution rate. The evaluation results are output through the standardized evaluation model, and the evaluation results are used to deploy the hydrogen sensor network nodes and set the communication parameters. The correlation analysis model for roughness evolution and signal attenuation trend, constructed based on the time-series variation data and the communication quality data of the hydrogen sensor network, includes: Real-time acquisition of communication quality data, including signal strength attenuation rate and bit error rate; Correlation analysis is performed on the time-series variation data and the communication quality data to determine the relationship between roughness evolution parameters and signal attenuation parameters; The correlation analysis model is generated based on the correlation relationship. The correlation analysis model is used to predict the signal attenuation trend and output the network maintenance cycle of the hydrogen sensor and dynamically adjust the communication parameters.

2. The method as described in claim 1, characterized in that, The collection of physical parameters of the medium wall interface at different depths and locations of the hydrogen storage cavity includes: The surface roughness parameters of the medium are obtained, and the values ​​of the surface roughness parameters of the medium are determined by surface scanning; The humidity parameter is obtained by measuring the humidity of the medium wall surface using a humidity sensor and obtaining the humidity parameter value. The mineral composition parameters are obtained, and the mineral composition parameter values ​​are determined by scanning the wall of the medium using a mineral composition analysis device. The interface physical parameters are formed based on the surface roughness parameter, the humidity parameter, and the mineral composition parameter.

3. The method as described in claim 1, characterized in that, The multi-dimensional correlation analysis based on the interface physical parameters and the measured electromagnetic wave propagation data includes: The scattering contribution index is calculated based on the surface roughness parameter of the medium, and the scattering contribution index is determined by a preset scattering formula; The absorption contribution index is calculated for the humidity parameter, and the absorption contribution index is determined by a preset absorption formula. The reflection contribution index is calculated based on the mineral composition parameters, and the reflection contribution index is determined by a preset reflection formula; The absorption contribution index and the reflection contribution index are calculated based on the scattering contribution index, and the total contribution index is obtained by weighted fusion.

4. The method as described in claim 1, characterized in that, The fixed surface morphology monitoring device is used to collect time-series variation data of the surface roughness parameters of the medium, including: The surface morphology of the medium wall is periodically scanned, and the current value of the medium wall roughness parameter is generated; The difference between the current roughness parameter value and the historical roughness parameter value of the medium wall is recorded to obtain the time-series variation data, which includes the evolution of hydrogen corrosion surface undulation, medium dissolution texture, and stress microcrack propagation parameters.

5. The method as described in claim 1, characterized in that, The correlation analysis based on the time-series change data and the communication quality data includes: Extract roughness evolution feature vectors from the time-varying data; Extract signal attenuation feature vectors from the communication quality data; The correlation coefficient between the roughness evolution feature vector and the signal attenuation feature vector is calculated by using a preset correlation coefficient. If the correlation coefficient is greater than a preset threshold, the association is confirmed and the association parameters are recorded.

6. The method as described in claim 1, characterized in that, The correlation analysis model is used to deploy the hydrogen sensor network nodes, set communication parameters, and determine the maintenance cycle, including: The placement of the hydrogen sensor network nodes is determined based on the evaluation results of the electromagnetic influence evaluation index system. The communication parameters, including transmit power and multipath signal processing parameters, are dynamically adjusted based on the prediction results of the correlation analysis model.

7. A communication system employing the communication method for a hydrogen sensor network in a hydrogen storage cavity according to any one of claims 1-6, characterized in that, include: The acquisition module is used to acquire the physical parameters of the medium wall interface at different depths and positions of the hydrogen storage cavity. The interface physical parameters include the roughness, humidity and mineral composition of the medium wall. A module is established to create an electromagnetic influence evaluation index system based on the interface physical parameters. The electromagnetic influence evaluation index system is used to quantify the contribution of different interface characteristic parameters to the absorption, reflection and scattering effects of wireless signals. The module is used to arrange fixed surface morphology monitoring devices at different depths in the hydrogen storage cavity. The fixed surface morphology monitoring devices are used to collect time-series variation data of the roughness parameters of the medium wall. A construction module is used to construct a correlation analysis model of roughness evolution and signal attenuation trend based on the time-series variation data and the communication quality data of the hydrogen sensor network. The correlation analysis model is used to arrange the nodes of the hydrogen sensor network, set communication parameters, and determine the maintenance cycle.

8. The system as described in claim 7, characterized in that, The acquisition module is specifically used to: acquire the surface roughness parameter of the medium wall and determine the surface roughness parameter value of the medium wall through surface scanning; acquire the humidity parameter by measuring the humidity of the medium wall through a humidity sensor and obtaining the humidity parameter value; and acquire the mineral composition parameter by scanning the medium wall through a mineral composition analysis device and determining the mineral composition parameter value. The interface physical parameters are formed based on the surface roughness parameter, the humidity parameter, and the mineral composition parameter.

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