Karst water dynamic monitoring and management method and system based on internet of things

Abstract

This invention relates to the field of wireless communication network technology, specifically to a method and system for dynamic monitoring and management of karst water based on the Internet of Things (IoT). The method includes: collecting structural data and environmental parameters of karst pipelines; establishing a propagation loss model and generating an initial signal attenuation distribution map; identifying areas affected by dynamic changes characterizing geological instability; optimizing the deployment of relay nodes based on the structural data of these areas, and adjusting their transmission frequency band, power, angle, and other communication parameters to achieve a target transmission efficiency threshold; evaluating the matching degree between communication parameters and geological structure, and generating an optimized signal attenuation distribution map through simulation; evaluating stability indicators such as signal loss rate and delay time based on this distribution map, and confirming the final deployment scheme. This invention effectively solves the technical problems of large attenuation and poor stability of wireless signal transmission in complex karst environments, significantly improving the reliability and real-time performance of monitoring data transmission.

Description

Technical Field

[0001] This invention relates to the field of wireless communication network technology, and more specifically to a method and system for dynamic monitoring and management of karst water based on the Internet of Things. Background Technology

[0002] Karst water monitoring is a core research area in the fields of environment and geological safety, and is of great significance for the scientific management of water resources and early warning of geological disasters. It can effectively support key tasks such as the prevention and control of karst cave collapses and the treatment of groundwater pollution. However, existing monitoring technologies have obvious limitations. They mostly rely on ground base stations to transmit electromagnetic wave signals and complete data transmission through fixed node deployments, which is not fully adapted to the unique geological conditions of karst areas. For example, the geometry of karst pipes is extremely irregular, with pipe diameters ranging from tens of centimeters to several meters, and the pipe environment has high humidity. These factors strongly interfere with signal propagation, resulting in rapid signal attenuation and limited coverage. Especially after penetrating deep underground, the signal strength drops sharply. Data from a karst monitoring project shows that in an irregular pipe system within a 10-square-kilometer area, the signal attenuates by more than 80% after penetrating 100 meters underground, which is simply insufficient to meet the needs of deep monitoring and even more difficult to adapt to the dynamic changes of karst runoff systems.

[0003] Although karst pipelines possess waveguide characteristics and have the potential to enhance signal propagation, the key to technological breakthroughs lies in how to rationally utilize this characteristic and adapt it to the dynamic changes in the karst pipeline environment. In summary, optimizing the deployment scheme of signal relay nodes, adjusting the transmission angle and frequency configuration based on the waveguide characteristics of karst pipelines, improving signal transmission efficiency in complex pipeline systems, and ensuring stable transmission of monitoring data are the core issues that urgently need to be addressed. Summary of the Invention

[0004] This invention provides a method and system for dynamic monitoring and management of karst water based on the Internet of Things, aiming to solve the technical problems mentioned in the background art.

[0005] In a first aspect, the present invention provides a method for dynamic monitoring and management of karst water based on the Internet of Things, including:

[0006] S1. Collect structural data and environmental parameters of the karst pipeline, establish a propagation loss model based on the structural data and environmental parameters, and generate an initial signal attenuation distribution map that reflects the propagation characteristics of electromagnetic wave signals in the pipeline;

[0007] S2. Analyze the initial signal attenuation distribution map, identify high-loss areas where the signal reflection intensity exceeds a preset threshold, and determine the high-loss areas as dynamic change influence areas characterizing geological instability.

[0008] S3. Based on the structural data of the dynamically changing affected area, an optimization algorithm is used to determine the location coordinates of the relay nodes and generate a relay node deployment scheme.

[0009] S4. Based on the relay node deployment scheme, iteratively adjust the transmission frequency band, transmission power and transmission angle of the relay nodes so that the network simulated transmission efficiency reaches the target threshold, and obtain the optimized frequency configuration combination, which includes the transmission frequency band, transmission power and transmission angle.

[0010] S5. Calculate the matching degree between the optimized frequency configuration combination and the structural data, and use the propagation loss model to perform simulation to generate the optimized signal attenuation distribution map;

[0011] S6. Evaluate the signal propagation stability based on the optimized signal attenuation distribution map. If the signal loss rate and communication delay time both meet the preset conditions, then confirm the relay node deployment scheme and the optimized frequency configuration combination as the final deployment scheme.

[0012] Further, S1 specifically includes: acquiring three-dimensional point cloud data and wall moisture content data of the karst pipeline using a lidar and humidity probe mounted on an autonomous navigation robot; calculating the permeability based on Darcy's law through an injection experiment; performing registration and voxelization processing on the point cloud data to construct a three-dimensional karst pipeline model with centimeter-level accuracy; generating a humidity-permeability joint random field using geostatistical methods based on the moisture content and permeability data; mapping the moisture content values ​​to dielectric constant and conductivity values ​​using a pre-established rock physics model according to the moisture content distribution in the joint random field, thereby generating a spatial distribution of dielectric constant and conductivity; using the three-dimensional karst pipeline model and the spatial distribution of dielectric constant and conductivity as input, simulating electromagnetic wave propagation using the finite-difference time-domain method to generate an initial signal attenuation distribution map reflecting the propagation characteristics of electromagnetic waves within the pipeline.

[0013] Further, S2 specifically includes: extracting the reflected signal energy of each spatial point based on the initial signal attenuation distribution map, as the reflection intensity of each spatial point; clustering continuous spatial points with reflection intensity exceeding a preset threshold into high-loss regions, and calculating the geometric center coordinates and equivalent radius of each high-loss region; calculating the sum of the distance and equivalent radius between the geometric centers of any N high-loss regions, and if the distance is less than the sum of the equivalent radii, then determining that the high-loss regions are interconnected, where N≥3; and determining the connected region formed by the interconnected high-loss regions as a dynamically changing influence region.

[0014] Further, S3 includes: based on the structural data of the dynamically changing affected area, extracting the surface curvature of the inner surface of the karst pipeline, and marking areas with absolute curvature values ​​exceeding a preset curvature threshold as high-risk geological areas; taking the avoidance of the high-risk geological areas as the core constraint and maximizing network coverage as the objective, solving for the optimal solution of relay node coordinates through an optimization algorithm to generate a node spatial layout diagram; calculating the Euclidean distance between each node in the node spatial layout diagram, and if there is a node spacing less than the minimum value required by the communication protocol, symmetrically adjusting the node positions along the perpendicular bisector of the node connection line until all node spacings are greater than or equal to the minimum value, and finally generating a relay node deployment scheme.

[0015] Furthermore, the objective of maximizing network coverage specifically involves: discretizing the karst pipeline area into multiple evaluation units, and statistically analyzing whether each evaluation unit is covered by the signal of at least one relay node; the network coverage rate is the percentage of the total number of covered evaluation units to the total number of all evaluation units, and the objective of maximizing network coverage is to maximize this percentage.

[0016] Further, S4 specifically includes: configuring initial transmission frequency band, transmission power, and transmission angle for each relay node in the relay node deployment scheme; calculating network simulated transmission efficiency based on the transmission frequency band, transmission power, and transmission angle; determining whether the network simulated transmission efficiency reaches a target threshold; if not, iteratively adjusting the transmission frequency band, transmission power, and transmission angle of the relay node until the network simulated transmission efficiency is greater than or equal to the target threshold; and determining the transmission frequency band, transmission power, and transmission angle as the optimized frequency configuration combination.

[0017] Further, S5 specifically includes: quantizing the frequency band and power parameters in the optimized frequency configuration combination and the dielectric constant and conductivity distribution parameters in the structural data into feature vectors respectively; calculating the cosine similarity between the feature vector of the frequency configuration combination and the feature vector of the structural data; if the cosine similarity value is less than a preset matching degree threshold, adjusting the frequency configuration combination until the matching degree meets the requirements; using the final frequency configuration combination with the matching degree meeting the standard as input conditions, recalculating the electromagnetic wave propagation path using the propagation loss model, and generating the optimized signal attenuation distribution map.

[0018] Furthermore, the adjustment of the frequency configuration combination is specifically implemented by using a particle swarm optimization algorithm, wherein the particle swarm optimization algorithm uses the cosine similarity value as a fitness function to iteratively adjust the transmission frequency band and transmission power.

[0019] Further, S6 specifically includes: based on the optimized signal attenuation distribution map, calculating the proportion of regions where the signal strength is lower than the receiver sensitivity, as the signal loss rate; calculating the electromagnetic wave propagation speed based on the dielectric constant in the structural data, and calculating the total topological length of the communication path in combination with the node coordinates in the relay node deployment scheme; calculating the communication delay time based on the propagation speed and the total topological path length; if the signal loss rate is higher than a preset loss rate threshold or the communication delay time is higher than a preset delay threshold, adjusting the frequency configuration combination or node layout and re-verifying until both the signal loss rate and the communication delay time meet the preset conditions; determining the frequency configuration relay node deployment scheme and frequency configuration combination that both meet the preset conditions for signal loss rate and communication delay time as the final deployment scheme.

[0020] Secondly, the present invention provides a system for dynamic monitoring and management of karst water based on the Internet of Things (IoT), used to implement the above-mentioned dynamic monitoring and management method for karst water based on the IoT, including:

[0021] The environmental perception and modeling module is used to collect structural data and environmental parameters of karst pipelines, establish a propagation loss model based on the structural data and environmental parameters, and generate an initial signal attenuation distribution map that reflects the propagation characteristics of electromagnetic wave signals in the pipeline.

[0022] The risk area identification module is used to analyze the initial signal attenuation distribution map, identify high-loss areas where the signal reflection intensity exceeds a preset threshold, and determine the high-loss areas as dynamic change influence areas characterizing geological instability.

[0023] The node layout module is used to determine the location coordinates of relay nodes based on the structural data of the dynamically changing affected area and to generate a relay node deployment scheme.

[0024] The communication parameter optimization module is used to iteratively adjust the transmission frequency band, transmission power and transmission angle of the relay nodes based on the relay node deployment scheme so that the network simulated transmission efficiency reaches the target threshold, and obtain the optimized frequency configuration combination, which includes the transmission frequency band, transmission power and transmission angle.

[0025] The scheme matching and verification module is used to calculate the matching degree between the optimized frequency configuration combination and the structural data, and to perform simulation using the propagation loss model to generate the optimized signal attenuation distribution map.

[0026] The final confirmation module is used to evaluate the signal propagation stability based on the optimized signal attenuation distribution map. If the signal loss rate and communication delay time both meet the preset conditions, the relay node deployment scheme and the optimized frequency configuration combination are confirmed as the final deployment scheme.

[0027] This invention offers the following advantages: It utilizes an autonomous navigation robot equipped with a lidar and humidity probe to acquire high-precision structural data of karst pipelines. Combined with humidity and permeability data, an accurate karst pipeline model is generated. The finite-difference time-domain (FDTD) method is used to simulate electromagnetic wave propagation, establishing a propagation loss model to reflect the propagation characteristics of electromagnetic waves within the pipeline, thereby improving the accuracy and reliability of monitoring signals. Simultaneously, by analyzing the initial signal attenuation distribution map, high-loss areas are identified and marked as dynamically changing influence areas. Then, an optimization algorithm is used to deploy relay nodes, ensuring maximum signal coverage and improving network coverage and communication efficiency. Furthermore, by iteratively adjusting the transmission frequency band, power, and transmission angle of the relay nodes, the network's simulated transmission efficiency reaches a preset target threshold, further optimizing signal transmission. By calculating the matching degree between frequency configuration and geological data, the signal propagation path is optimized, ultimately ensuring stable signal transmission and low latency. This method can dynamically adapt to changes in the karst pipeline environment, reducing signal loss rate and communication latency, improving the stability and flexibility of the monitoring system under complex geological conditions, meeting the needs of deep karst water monitoring, and providing reliable technical support for karst water resource management and geological disaster early warning. Attached Figure Description

[0028] Figure 1 This is a flowchart of the IoT-based dynamic monitoring and management method for karst water according to the present invention;

[0029] Figure 2 This is a signal attenuation distribution diagram of the karst conduit detection path of the present invention;

[0030] Figure 3 This is a structural diagram of the Internet of Things-based dynamic monitoring and management system for karst water according to the present invention. Detailed Implementation

[0031] To further understand the content of this invention, a detailed description of the invention is provided in conjunction with the accompanying drawings and embodiments. The specific embodiments described herein are for illustrative purposes only and are not intended to limit the invention. It should also be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings.

[0032] like Figure 1 As shown in this embodiment, the IoT-based dynamic monitoring and management method for karst water includes:

[0033] S1. Collect structural data and environmental parameters of the karst pipeline, establish a propagation loss model based on the structural data and environmental parameters, and generate an initial signal attenuation distribution map that reflects the propagation characteristics of electromagnetic wave signals in the pipeline.

[0034] Specifically, structural data around the karst conduits, including rock type, porosity, fracture distribution, and bedding thickness, were collected using geological exploration equipment and sensors. Environmental parameters, including temperature, humidity, and pressure, were also collected. Based on a rock physics model, the structural data and environmental parameters were converted into spatial dielectric constant and conductivity distributions, as shown in Table 1. A propagation loss model was then established using the finite-difference time-domain (FDTD) method. This model discretizes the solution space using a Yee grid, with the grid size set to one-tenth of the wavelength to ensure computational accuracy. The time step was automatically determined based on the Courant stability condition to guarantee numerical stability. During the model calculation, the parameters interact in the following ways: the dielectric constant is mainly affected by both porosity and water saturation, and a linear weighted model is used for calculation; the conductivity is determined by the rock matrix conductivity, pore water conductivity, and fracture density, with the contribution of fracture density to conductivity reflected through an additional term. Based on the above parameter settings and interaction relationships, the energy loss during propagation is quantified by calculating the field strength attenuation of the electromagnetic wave at each grid point, ultimately generating an initial signal attenuation distribution map in decibels per meter. For example... Figure 2 As shown in the figure, the distribution map clearly shows the distribution of signal attenuation along the pipeline. The high attenuation areas (such as P004 and P008) have a significant correspondence with the geologically unstable areas with high porosity, high fracture density, and high humidity.

[0035] Table 1

[0036]

[0037] S2. Analyze the initial signal attenuation distribution map, identify high-loss areas where the signal reflection intensity exceeds a preset threshold, and determine the high-loss areas as dynamic change influence areas characterizing geological instability.

[0038] Specifically, data analysis is performed on the initial signal attenuation distribution map to identify areas with significant signal attenuation. By setting a preset signal reflection intensity threshold, the signal intensity is judged to identify areas where the reflection intensity exceeds the threshold; these areas typically represent regions with significant signal loss during propagation. These identified high-loss areas are defined as regions characterizing the dynamic changes in geological instability, implying that the increased signal attenuation may be closely related to changes in the geological structure surrounding the pipeline. Therefore, by monitoring changes in these high-loss areas, real-time tracking and assessment of geological instability within the pipeline can be achieved.

[0039] S3. Based on the structural data of the dynamically changing affected area, an optimization algorithm is used to determine the location coordinates of the relay nodes and generate a relay node deployment scheme.

[0040] Specifically, in the node layout design process, an optimization algorithm is used to process the structural data of dynamically changing areas and determine the optimal location of relay nodes based on geological instability and signal attenuation distribution. The goal of the optimization algorithm is to find node locations that effectively improve signal quality and ensure stable system operation by considering multiple factors such as signal coverage, distance between nodes, signal enhancement requirements, and the complexity of the geological environment. The algorithm considers not only the physical characteristics of signal propagation but also the impact of geological changes on signal transmission, dynamically adjusting the node layout to adapt to different geological conditions and environmental conditions. For example, in some areas where the geological structure is relatively unstable, resulting in significant signal attenuation, the optimization algorithm will automatically deploy relay nodes near these areas to enhance signal coverage and propagation. In areas with more stable geological conditions, fewer relay nodes may be selected to optimize resource allocation and reduce costs. In this way, the node layout scheme can dynamically adapt to the complex geological environment within karst pipelines, effectively improving the propagation efficiency of electromagnetic wave signals. This technical feature not only solves the problem of signal attenuation within karst pipelines but also automatically adjusts the relay node deployment scheme according to actual geological environment changes, ensuring the system's flexibility and adaptability. By combining optimized algorithms and structured data, efficient relay node deployment can be achieved, ensuring stable transmission of electromagnetic wave signals and thus improving the overall performance and reliability of pipeline communication systems.

[0041] S4. Based on the relay node deployment scheme, iteratively adjust the relay node's transmission frequency band, transmission power, and transmission angle to make the network simulated transmission efficiency reach the target threshold, and obtain the optimized frequency configuration combination, which includes the transmission frequency band, transmission power, and transmission angle.

[0042] Specifically, by iteratively adjusting the relay node deployment scheme, the transmission frequency band, transmission power, and transmission angle of the relay nodes are finely controlled to improve network transmission efficiency and achieve a preset target threshold. This technology relies on the optimization results of the initial node layout, where the location of each relay node is rationally arranged based on structural and environmental data using an optimization algorithm. Network performance is further optimized by meticulously adjusting the communication parameters of each relay node. In the specific implementation process, an iterative optimization algorithm is used to adjust the transmission frequency band, transmission power, and transmission angle based on the node deployment scheme. The adjustment of each parameter is closely related to the specific location of the node, the surrounding geological environment, and the propagation characteristics of electromagnetic waves. The selection of the transmission frequency band needs to consider the matching of signal attenuation with environmental factors. For example, in some high-loss areas, a lower frequency band may need to be selected to reduce signal attenuation. The optimization of transmission power needs to be adjusted according to the distance between nodes and the signal coverage requirements to ensure good signal coverage in each area without wasting excessive energy. The adjustment of the transmission angle mainly targets the optimization of the signal propagation path, enabling the signal to cover the required area to the maximum extent while avoiding interference sources and high-loss areas.

[0043] Adjusting these communication parameters requires deriving network transmission efficiency through simulation calculations. The optimization algorithm iteratively adjusts these parameters until the simulated network transmission efficiency reaches the target threshold, ensuring the system's stability and efficiency in actual operation. Ultimately, the optimized frequency configuration combination is output, encompassing the transmission frequency band, transmission power, and transmission angle of each node. These optimized configuration combinations ensure that each relay node achieves optimal signal propagation under different geological environments, avoiding excessive energy loss and effectively improving the overall system transmission efficiency. For example, in a specific karst area, where signal attenuation is significant in certain areas, adjusting the node's transmission frequency band and power can enhance signal coverage, overcome the adverse effects of geological factors, and thus improve the overall communication quality and efficiency of the system.

[0044] S5. Calculate the matching degree between the optimized frequency configuration combination and the structural data, and use the propagation loss model to perform simulation to generate the optimized signal attenuation distribution map.

[0045] Specifically, the optimized frequency configuration combination includes the transmission frequency band, transmission power, and transmission angle. Structural data reflects the characteristics of the rock strata surrounding the karst conduit, fracture distribution, porosity, etc. This embodiment calculates the matching degree between the frequency configuration combination and the geological data by comparing the influence of various factors in the geological structure on signal propagation and the mutual adaptability of the optimized parameters, thereby determining whether the optimized configuration can effectively improve signal propagation. Furthermore, a propagation loss model is used for simulation testing. This model is based on the physical theory of electromagnetic wave propagation and combines actual geological environment data to simulate signal attenuation under different frequency configurations. Through simulation, an optimized signal attenuation distribution map can be generated. For example, in a specific karst conduit section, the optimized frequency configuration may improve signal strength in certain high-loss areas and has a high degree of matching with the geological data; simulation results show a significant reduction in signal attenuation. This indicates that the optimized frequency configuration combination can effectively improve signal propagation, thereby ensuring the stability and coverage of the communication system. This process demonstrates the close relationship between the propagation loss model, structural data, and optimization algorithm, ensuring the effectiveness of technical means in solving practical technical problems and improving the performance and reliability of the entire system.

[0046] S6. Evaluate the signal propagation stability based on the optimized signal attenuation distribution map. If the signal loss rate and communication delay time both meet the preset conditions, then confirm the relay node deployment scheme and the optimized frequency configuration combination as the final deployment scheme.

[0047] Specifically, by simulating the stability of signal propagation and communication performance, the optimized network scheme is ensured to meet preset performance standards. Signal attenuation distribution maps are used to evaluate signal propagation in each area, identifying areas with poor signal stability and those with significantly improved signal strength. Signal loss rate and communication delay are two key indicators during the evaluation process. The signal loss rate reflects signal loss during transmission, while the communication delay determines the real-time performance and response speed of data transmission. By calculating these indicators, the network performance can be comprehensively measured to ensure it meets the preset standards. If the signal loss rate is low and the communication delay meets system requirements, it indicates that the relay node deployment scheme and frequency configuration combination are effective, signal propagation is stable, and network performance is good. For example, suppose the signal attenuation map of a karst pipeline section shows that in some high-loss areas, the optimized frequency configuration effectively reduces signal loss, and the communication delay is within an acceptable range. This indicates that the optimized scheme successfully solves the signal attenuation problem and ensures the stability and real-time performance of signal transmission. Therefore, if the optimized frequency configuration combination meets these conditions, the scheme can be confirmed as the final deployment scheme. This technique allows for comprehensive verification of the entire system's performance, preventing network failures caused by improper relay node deployment or inappropriate frequency selection. It enables the communication system to operate stably and efficiently in real-world scenarios, while also improving its adaptability and reliability, demonstrating significant technological advantages and innovation.

[0048] Following step S6, a dynamic monitoring and management phase is also included, used for long-term, adaptive monitoring and management of the karst water system after the final deployment plan is implemented. Specifically, this includes: periodically or trigger-based re-execution of step S1, using the autonomous navigation robot to collect the latest three-dimensional geological structure data and environmental parameters, and updating the propagation loss model and signal attenuation distribution map; based on the updated signal attenuation distribution map, repeating steps S2 to S6 to dynamically optimize and adjust the relay node deployment plan and frequency configuration combination to adapt to the dynamic changes in the geological conditions of the karst pipeline; during system operation, real-time monitoring of the signal loss rate and communication delay time of each relay node, automatically initiating adjustments to the communication parameters of local nodes or suggesting maintenance when any indicator continuously deviates from preset conditions; and transmitting the monitoring data to the management platform through an optimized IoT network for data analysis and visualization, achieving real-time monitoring, trend warning, and scientific management of the karst water dynamics.

[0049] In one specific embodiment, the process of performing step S1 specifically includes the following steps:

[0050] The three-dimensional point cloud data and wall moisture content data of the karst pipes were obtained by using a lidar and humidity probe carried by an autonomous navigation robot; and the permeability was calculated based on Darcy's law through injection experiments.

[0051] The point cloud data is registered and voxelized to construct a three-dimensional karst pipe model with centimeter-level accuracy.

[0052] Based on the moisture content and permeability data, a joint humidity-permeability random field is generated using geostatistical methods;

[0053] Based on the water content distribution in the joint random field, the water content values ​​are mapped to dielectric constant and conductivity values ​​using a pre-established rock physics model, thereby generating the spatial distribution of dielectric constant and conductivity.

[0054] Using the three-dimensional karst pipeline model and the spatial distribution of dielectric constant and conductivity as input, the electromagnetic wave propagation is simulated using the finite-difference time-domain method to generate an initial signal attenuation distribution map that reflects the propagation characteristics of electromagnetic waves in the pipeline.

[0055] Specifically, in the simulation of electromagnetic wave propagation characteristics in karst pipelines, step S1 involves a series of meticulous measurements and data processing to generate an initial signal attenuation distribution map, providing a foundation for subsequent optimization design. In practice, an autonomous navigation robot equipped with a lidar and humidity probe acquires 3D point cloud data and wall moisture content data of the pipeline. Point cloud data represents the spatial geometry inside the pipeline, while wall moisture content data reflects the distribution of moisture on the pipeline wall; these data are crucial for simulating electromagnetic wave propagation in karst pipelines. The acquired point cloud data undergoes further processing through registration and voxelization. Registration aligns point cloud data from different sources or at different times, ensuring spatial coordinate consistency; voxelization discretizes continuous 3D data into regular grid points, constructing a 3D karst pipeline model with centimeter-level precision, ensuring refined spatial resolution during the simulation to more accurately reflect the complex geological characteristics of the pipeline. Based on moisture content and permeability data, a joint random field of humidity and permeability is generated using geostatistical methods. This joint random field describes the spatial variation characteristics of humidity and permeability inside the pipeline. Furthermore, permeability was calculated using injection experiments combined with Darcy's law, enriching the parameters of the geological model. After combining water content and permeability data with geostatistical methods, a pre-established rock physics model was used to map the water content values ​​within the pipeline to dielectric constant and conductivity values. This step provided the necessary electromagnetic parameters for electromagnetic wave propagation simulation. The spatial distribution of these dielectric constants and conductivity have a decisive influence on the propagation characteristics of electromagnetic waves. Inputting the three-dimensional karst pipeline model and the spatial distribution of dielectric constants and conductivity, the finite-difference time-domain (FDTD) method was used for electromagnetic wave propagation simulation. The FDTD method, by discretizing time and space, transforms the propagation process of electromagnetic waves in complex media into a numerical solution, thereby generating an initial signal attenuation distribution map reflecting the propagation characteristics of electromagnetic waves within the pipeline. This distribution map shows the degree of electromagnetic wave signal attenuation in different regions, serving as an important basis for subsequent communication parameter optimization and node layout design. For example, in practical applications, if the water content of a certain section of the pipeline is high, it may lead to an increase in the dielectric constant and conductivity of that region, thus exacerbating the attenuation of electromagnetic waves. This technology can accurately identify these high-attenuation areas, providing crucial data support for subsequent signal enhancement or relay node deployment.

[0056] In one specific embodiment, the process of performing step S2 specifically includes the following steps:

[0057] Based on the initial signal attenuation distribution map, the reflected signal energy of each spatial point is extracted as the reflection intensity of each spatial point.

[0058] Cluster continuous spatial points with reflection intensity exceeding a preset threshold into high-loss regions, and calculate the geometric center coordinates and equivalent radius of each high-loss region.

[0059] Calculate the sum of the distances and equivalent radii between the geometric centers of any N high-loss regions. If the distances are less than the sum of the equivalent radii, then the high-loss regions are determined to be interconnected, where N ≥ 3.

[0060] The connected regions formed by the interconnected high-loss regions are identified as dynamically changing influence regions.

[0061] Specifically, the spatial points in the initial signal attenuation distribution map are analyzed to extract the reflected signal energy within a specific frequency band. This reflected signal energy is used as the reflection intensity of each spatial point. Reflection intensity is an important indicator in electromagnetic wave propagation, reflecting the attenuation and reflection effects experienced by the signal during propagation. After extracting the reflection intensity data, if the reflection intensity of a spatial point exceeds a preset threshold, that point is considered a member of a potential high-loss region. By clustering these consecutive spatial points with reflection intensities higher than the threshold, multiple high-loss regions can be identified. Each high-loss region is defined as a region with strong signal attenuation characteristics. The geometric center coordinates and equivalent radius of each high-loss region are calculated, where the geometric center is the centroid of the region, and the equivalent radius reflects the spatial extent of the region. To determine the relationship between multiple high-loss regions, the distance between the geometric centers of any N high-loss regions is calculated, and these distances are compared with the sum of their corresponding equivalent radii. If the distance between the geometric centers of these high-loss regions is less than the sum of their respective equivalent radii, then these high-loss regions are determined to be interconnected, forming a connected region. The interconnected high-loss zones are identified as dynamically changing influence areas. These areas represent variations in the pipeline's internal geological conditions, such as fissures, cavities, or other unstable regions, which significantly impact electromagnetic wave propagation. This identification of dynamically changing influence areas provides crucial information for subsequent signal optimization and communication path adjustments, enabling the development of targeted solutions such as node deployment and signal enhancement.

[0062] In one specific embodiment, the process of performing step S3 specifically includes the following steps:

[0063] Based on the structural data of the area affected by the dynamic changes, the surface curvature of the inner surface of the karst pipe is extracted, and the area with an absolute curvature value exceeding a preset curvature threshold is marked as a geologically high-risk area.

[0064] With the core constraint of avoiding the geologically high-risk areas and the goal of maximizing network coverage, the optimal solution for the coordinates of relay nodes is obtained through an optimization algorithm, and a node spatial layout map is generated.

[0065] Calculate the Euclidean distance between each node in the node spatial layout diagram. If there is a node spacing less than the minimum value required by the communication protocol, adjust the node positions symmetrically along the perpendicular bisector of the node connection line until the spacing between all nodes is greater than or equal to the minimum value, and finally generate the relay node deployment scheme.

[0066] Specifically, based on structural data of the dynamically changing affected areas, the surface curvature of the inner surface of the karst pipeline is extracted to identify potential high-risk geological zones on the pipeline's inner surface. By calculating the surface curvature, regions with abrupt changes or instabilities within the pipeline, such as fissures or cavities, can be effectively identified. These regions significantly affect electromagnetic wave propagation and the stability of communication signals. By setting a curvature threshold, areas with absolute curvature values ​​exceeding this threshold are marked as high-risk geological zones. These high-risk zones must be avoided during node deployment to ensure the stability and reliability of the communication system.

[0067] To optimize the relay node deployment, a genetic algorithm is employed. This algorithm sets the population size to 100 individuals, each representing a node coordinate combination. Network coverage is used as the fitness function. The population iteratively evolves through roulette wheel selection, single-point crossover, and basic position mutation. The crossover probability is set to 0.8, the mutation probability to 0.01, and the maximum number of iterations to 200 generations. Constraints are used during the evolution process to ensure that all node coordinates are located outside geologically high-risk areas. The method for maximizing network coverage involves discretizing the internal space of the karst pipe into cubic evaluation units with a side length of 1 meter. Ray tracing is used to determine whether the center point of each unit can be covered by the signal from at least one relay node. The ratio of the number of covered units to the total number of units is used as the coverage rate. The genetic algorithm continuously optimizes the node coordinate combinations to maximize this percentage. The algorithm terminates prematurely when the fitness improvement is less than 1% for 10 consecutive generations. After obtaining the node spatial layout diagram through this optimization process, the Euclidean distances between nodes are calculated. If any node spacing is less than the minimum value required by the communication protocol, the positions of these nodes need to be adjusted. Specifically, by calculating the distances between nodes, nodes that are too close together are identified. If the distance between any two nodes is less than the minimum required by the communication protocol, the coordinates of the midpoint of the line connecting the two nodes are calculated. A unit normal vector perpendicular to the line vector is also calculated. Starting from the midpoint, the two nodes are moved the same distance in the opposite direction of the unit normal vector, and the distances between the nodes are recalculated until the distances between all nodes are greater than or equal to the minimum. This ensures that the requirements of the communication protocol are met while avoiding the problem of nodes being too densely packed or interfering with each other. Through this process, a relay node deployment scheme is ultimately generated, ensuring that the distribution of nodes not only avoids geologically high-risk areas but also maximizes network coverage.

[0068] In one specific embodiment, the process of performing step S4 specifically includes the following steps:

[0069] Configure the initial transmission frequency band, transmission power and transmission angle for each relay node in the relay node deployment scheme;

[0070] The network simulated transmission efficiency is calculated based on the transmission frequency band, the transmission power, and the transmission angle.

[0071] Determine whether the network simulated transmission efficiency has reached the target threshold. If not, iteratively adjust the relay node's transmission frequency band, transmission power, and transmission angle until the network simulated transmission efficiency is greater than or equal to the target threshold.

[0072] The transmission frequency band, the transmission power, and the transmission angle are determined as the optimized frequency configuration combination.

[0073] Specifically, each relay node in the relay node deployment scheme is configured with an initial transmission frequency band, transmission power, and transmission angle. These parameters determine the signal coverage and propagation capability of the relay node; therefore, the initial configuration is based on a comprehensive consideration of the geological environment and communication requirements. Based on this initial configuration, the transmission process of data packets in a multi-hop network is simulated using an established propagation loss model, and the network simulated transmission efficiency is calculated using a weighted average of the quantified network delivery success rate and the average end-to-end delay. If the simulated transmission efficiency does not reach the target threshold, a systematic parameter iterative adjustment process is initiated. The target threshold is a pre-set, explicit numerical standard based on the reliability requirements of the specific monitoring application scenario; for example, the transmission efficiency must reach 90%. The specific parameter iterative adjustment process is as follows: for the transmission frequency band, switching is performed in a preset order within the system-allowed frequency band; for the transmission power, an incremental or decremental search is performed within the equipment's safe range; for the transmission angle, the beam pointing is adjusted with discrete step angles. Once the required transmission efficiency is achieved through the iterative process—that is, when the simulated transmission efficiency reaches or exceeds the target threshold—the resulting transmission frequency band, transmission power, and transmission angle configuration are considered the optimized frequency configuration combination. This configuration combination will serve as an important parameter in the relay node deployment scheme, providing a basis for subsequent network deployment and signal optimization. The innovation of this technique lies in combining simulation and iterative optimization to ensure optimal signal coverage and transmission performance even in complex geological environments, maximizing the effectiveness of the communication system.

[0074] In one specific embodiment, the process of performing step S5 specifically includes the following steps:

[0075] The frequency band and power parameters in the optimized frequency configuration combination, as well as the dielectric constant and conductivity distribution parameters in the structural data, are quantized into feature vectors respectively.

[0076] Calculate the cosine similarity between the feature vector of the frequency configuration combination and the feature vector of the structural data;

[0077] If the cosine similarity value is less than the preset matching threshold, the frequency configuration combination is adjusted until the matching degree meets the requirements.

[0078] Using the final frequency configuration combination that meets the matching degree as input conditions, the electromagnetic wave propagation path is recalculated using the propagation loss model to generate the optimized signal attenuation distribution map.

[0079] Specifically, the frequency band and power parameters in the optimized frequency configuration combination are quantified with the dielectric constant and conductivity distribution parameters in the structural data. The cosine similarity between the resulting frequency configuration combination feature vector and the structural data feature vector is then calculated. Cosine similarity is a commonly used method to measure the similarity between two vectors; it assesses their similarity by calculating the cosine of the angle between the two vectors. A higher similarity indicates a better match between the frequency configuration and the structural data, and vice versa. In practical applications, this step helps determine whether the current frequency configuration combination adequately considers the influence of the geological environment on electromagnetic wave propagation. If the calculated cosine similarity value is lower than a preset matching threshold, it indicates insufficient matching between the frequency configuration combination and the geological environment, requiring further adjustment of the frequency configuration combination. This adjustment is achieved through a particle swarm optimization algorithm. During initialization, the algorithm randomly generates a set of particle positions representing different transmission frequency bands and power combinations, with each particle representing a candidate solution. In each iteration, the algorithm evaluates the cosine similarity value (i.e., fitness) corresponding to the parameter combination represented by each particle, and updates the next movement direction and step size of all particles based on the particle's own historical best position and the globally best position found by the entire population. This guides the entire population to collaboratively search and converge towards regions with higher matching degrees in the parameter solution space until a parameter combination that meets the matching degree requirement is found. The core logic of this adjustment process lies in the fact that the propagation loss model has encapsulated and quantified the complex physical influence of the node's specific location and surrounding geological environment (reflected in the spatial distribution of dielectric constant and conductivity) on electromagnetic wave propagation. By continuously trying and evaluating the performance (i.e., cosine similarity) of different frequency bands and power combinations under the "environment-signal" interaction defined by the model, the particle swarm algorithm is essentially systematically exploring and searching for the transmission parameters best suited to this specific location and geological environment, thus clearly establishing a dynamic feedback and optimization relationship between parameter adjustment and geographical location and geological environment. After the matching degree meets the requirements, the resulting optimized frequency configuration combination will be used as input conditions and substituted into the propagation loss model to recalculate the electromagnetic wave propagation path. The propagation loss model is then used to accurately simulate signal propagation, generating an optimized signal attenuation distribution map. This signal attenuation distribution map can more accurately reflect the propagation characteristics of electromagnetic waves in complex geological environments such as karst pipes, providing a more precise basis for network optimization.

[0080] In one specific embodiment, the process of performing step S6 specifically includes the following steps:

[0081] Based on the optimized signal attenuation distribution map, the percentage of areas where the signal strength is lower than the receiver sensitivity is statistically analyzed and used as the signal loss rate.

[0082] The electromagnetic wave propagation speed is calculated based on the dielectric constant in the structural data, and the total topological length of the communication path is calculated in combination with the node coordinates in the relay node deployment scheme. The communication delay time is then calculated based on the propagation speed and the total topological path length.

[0083] If the signal loss rate is higher than a preset loss rate threshold or the communication delay time is higher than a preset delay threshold, the frequency configuration combination or node layout will be adjusted and re-verified until both the signal loss rate and the communication delay time meet the preset conditions.

[0084] The final deployment scheme is determined by the frequency configuration of the relay node, which ensures that both the signal loss rate and communication delay time meet the preset conditions.

[0085] Specifically, based on the optimized signal attenuation distribution map, the percentage of areas where the signal strength is lower than the receiver sensitivity is statistically analyzed; this percentage is the signal loss rate. The calculation of the signal loss rate depends on the signal strength distribution and the receiver's sensitivity threshold; a higher rate indicates less than ideal signal coverage, potentially affecting network reliability. Using the dielectric constant from the structural data, the propagation speed of electromagnetic waves is calculated. Combined with the node coordinates in the relay node deployment scheme, the total topological length of the communication path is calculated. The total topological length reflects the actual propagation distance between nodes in the network and is crucial for calculating network latency. The combination of propagation speed and total topological path length yields the communication latency, a metric that directly impacts network real-time performance and user experience. After calculating the signal loss rate and communication latency, the system compares them to preset loss rate and latency thresholds. If the signal loss rate exceeds the limit or the communication latency is too long, adjustments to the frequency configuration or relay node layout are triggered. During the adjustment process, frequency configurations (such as transmission bands and power) may be re-optimized, or the relay node deployment locations may be adjusted to optimize signal coverage and propagation latency. At this point, the system will regenerate and verify the signal attenuation distribution map based on the new configuration and layout scheme until the set loss rate and delay time conditions are met. The final deployment scheme is determined by the frequency configuration relay node deployment scheme and frequency configuration combination that both meet the preset conditions for signal loss rate and communication delay time. The advantage of this technical solution is that, through dynamic adjustment and verification, it can accurately optimize network performance for different geological conditions and network requirements, thereby ensuring the efficient and stable operation of the communication system.

[0086] Secondly, please refer to Figure 3 This application provides an IoT-based dynamic monitoring and management system for karst water, including:

[0087] The environmental perception and modeling module 101 is used to collect structural data and environmental parameters of the karst pipeline, establish a propagation loss model based on the structural data and environmental parameters, and generate an initial signal attenuation distribution map that reflects the propagation characteristics of electromagnetic wave signals in the pipeline.

[0088] The risk area identification module 102 is used to analyze the initial signal attenuation distribution map, identify high loss areas where the signal reflection intensity exceeds a preset threshold, and determine the high loss areas as dynamic change influence areas characterizing geological instability.

[0089] The node layout module 103 is used to determine the location coordinates of relay nodes based on the structural data of the dynamically changing affected area and to generate a relay node layout scheme.

[0090] The communication parameter optimization module 104 is used to iteratively adjust the transmission frequency band, transmission power and transmission angle of the relay node based on the relay node deployment scheme so that the network simulated transmission efficiency reaches the target threshold, and obtain the optimized frequency configuration combination, which includes the transmission frequency band, transmission power and transmission angle.

[0091] The scheme matching verification module 105 is used to calculate the matching degree between the optimized frequency configuration combination and the structural data, and to perform simulation using the propagation loss model to generate the optimized signal attenuation distribution map.

[0092] The final confirmation module 106 is used to evaluate the signal propagation stability based on the optimized signal attenuation distribution map. If the signal loss rate and communication delay time both meet the preset conditions, the relay node deployment scheme and the optimized frequency configuration combination are confirmed as the final deployment scheme.

[0093] The technical solution provided in this embodiment, by combining structural data, propagation loss models, and relay node optimization algorithms, can achieve accurate signal propagation prediction and relay node deployment optimization in complex environments, effectively improving the coverage and signal stability of wireless communication networks. By dynamically adjusting frequency configuration and node layout, it maximizes network transmission efficiency, reduces communication latency and signal loss rate, and ensures high-quality communication services. Furthermore, the system possesses adaptive optimization capabilities, allowing for flexible adjustments based on different geological environments and requirements, thereby enhancing the overall performance and reliability of the network. It exhibits significant technical advantages and broad application prospects.

[0094] The above description is merely a specific implementation of this specification. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, modules, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here. It should be understood that the scope of protection of this specification is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this specification, and these modifications or substitutions should all be covered within the scope of protection of this specification.

Claims

1. A method for dynamic monitoring and management of karst water based on the Internet of Things, characterized in that, include: S1. Collect structural data and environmental parameters of the karst pipeline, establish a propagation loss model based on the structural data and environmental parameters, and generate an initial signal attenuation distribution map that reflects the propagation characteristics of electromagnetic wave signals in the pipeline; S2. Analyze the initial signal attenuation distribution map, identify high-loss areas where the signal reflection intensity exceeds a preset threshold, and determine the high-loss areas as dynamic change influence areas characterizing geological instability. S3. Based on the structural data of the dynamically changing affected area, an optimization algorithm is used to determine the location coordinates of the relay nodes and generate a relay node deployment scheme. S4. Based on the relay node deployment scheme, iteratively adjust the transmission frequency band, transmission power and transmission angle of the relay nodes so that the network simulated transmission efficiency reaches the target threshold, and obtain the optimized frequency configuration combination, which includes the transmission frequency band, transmission power and transmission angle. S5. Calculate the matching degree between the optimized frequency configuration combination and the structural data, and use the propagation loss model to perform simulation to generate the optimized signal attenuation distribution map; S6. Evaluate the signal propagation stability based on the optimized signal attenuation distribution map. If the signal loss rate and communication delay time both meet the preset conditions, then confirm the relay node deployment scheme and the optimized frequency configuration combination as the final deployment scheme.

2. The method for dynamic monitoring and management of karst water based on the Internet of Things as described in claim 1, characterized in that, S1 specifically includes: The three-dimensional point cloud data and wall moisture content data of the karst pipes were obtained by using a lidar and humidity probe carried by an autonomous navigation robot; and the permeability was calculated based on Darcy's law through injection experiments. The point cloud data is registered and voxelized to construct a three-dimensional karst pipe model with centimeter-level accuracy. Based on the moisture content and permeability data, a joint humidity-permeability random field is generated using geostatistical methods; Based on the water content distribution in the joint random field, the water content values ​​are mapped to dielectric constant and conductivity values ​​using a pre-established rock physics model, thereby generating the spatial distribution of dielectric constant and conductivity. Using the three-dimensional karst pipeline model and the spatial distribution of dielectric constant and conductivity as input, the electromagnetic wave propagation is simulated using the finite-difference time-domain method to generate an initial signal attenuation distribution map that reflects the propagation characteristics of electromagnetic waves in the pipeline.

3. The method for dynamic monitoring and management of karst water based on the Internet of Things as described in claim 2, characterized in that, S2 specifically includes: Based on the initial signal attenuation distribution map, the reflected signal energy of each spatial point is extracted as the reflection intensity of each spatial point. Cluster continuous spatial points with reflection intensity exceeding a preset threshold into high-loss regions, and calculate the geometric center coordinates and equivalent radius of each high-loss region. Calculate the sum of the distances and equivalent radii between the geometric centers of any N high-loss regions. If the distances are less than the sum of the equivalent radii, then the high-loss regions are determined to be interconnected, and N≥3. The connected regions formed by the interconnected high-loss regions are identified as dynamically changing influence regions.

4. The method for dynamic monitoring and management of karst water based on the Internet of Things as described in claim 1, characterized in that, S3 includes: Based on the structural data of the area affected by the dynamic changes, the surface curvature of the inner surface of the karst pipe is extracted, and the area with an absolute curvature value exceeding a preset curvature threshold is marked as a geologically high-risk area. With the core constraint of avoiding the geologically high-risk areas and the goal of maximizing network coverage, the optimal solution for the coordinates of relay nodes is obtained through an optimization algorithm, and a node spatial layout map is generated. Calculate the Euclidean distance between each node in the node spatial layout diagram. If there is a node spacing less than the minimum value required by the communication protocol, adjust the node positions symmetrically along the perpendicular bisector of the node connection line until the spacing between all nodes is greater than or equal to the minimum value, and finally generate the relay node deployment scheme.

5. The method for dynamic monitoring and management of karst water based on the Internet of Things as described in claim 4, characterized in that, The objective of maximizing network coverage specifically involves: discretizing the karst pipeline area into multiple evaluation units, and statistically analyzing whether each evaluation unit is covered by the signal of at least one relay node; the network coverage rate is the percentage of the total number of covered evaluation units to the total number of all evaluation units, and maximizing network coverage means maximizing this percentage.

6. The method for dynamic monitoring and management of karst water based on the Internet of Things as described in claim 1, characterized in that, S4 specifically includes: Configure the initial transmission frequency band, transmission power and transmission angle for each relay node in the relay node deployment scheme; The network simulated transmission efficiency is calculated based on the transmission frequency band, the transmission power, and the transmission angle. Determine whether the network simulated transmission efficiency has reached the target threshold. If not, iteratively adjust the relay node's transmission frequency band, transmission power, and transmission angle until the network simulated transmission efficiency is greater than or equal to the target threshold. The transmission frequency band, the transmission power, and the transmission angle are determined as the optimized frequency configuration combination.

7. The method for dynamic monitoring and management of karst water based on the Internet of Things as described in claim 1, characterized in that, S5 specifically includes: The frequency band and power parameters in the optimized frequency configuration combination, as well as the dielectric constant and conductivity distribution parameters in the structural data, are quantized into feature vectors respectively. Calculate the cosine similarity between the feature vector of the frequency configuration combination and the feature vector of the structural data; If the cosine similarity value is less than the preset matching threshold, the frequency configuration combination is adjusted until the matching degree meets the requirements. Using the final frequency configuration combination that meets the matching degree as input conditions, the electromagnetic wave propagation path is recalculated using the propagation loss model to generate the optimized signal attenuation distribution map.

8. The method for dynamic monitoring and management of karst water based on the Internet of Things as described in claim 7, characterized in that, The adjustment of the frequency configuration combination is specifically achieved by using a particle swarm optimization algorithm, which uses the cosine similarity value as a fitness function to iteratively adjust the transmission frequency band and transmission power.

9. The method for dynamic monitoring and management of karst water based on the Internet of Things as described in claim 6, characterized in that, S6 specifically includes: Based on the optimized signal attenuation distribution map, the percentage of areas where the signal strength is lower than the receiver sensitivity is statistically analyzed and used as the signal loss rate. The electromagnetic wave propagation speed is calculated based on the dielectric constant in the structural data, and the total topological length of the communication path is calculated in combination with the node coordinates in the relay node deployment scheme. The communication delay time is then calculated based on the propagation speed and the total topological path length. If the signal loss rate is higher than a preset loss rate threshold or the communication delay time is higher than a preset delay threshold, the frequency configuration combination or node layout will be adjusted and re-verified until both the signal loss rate and the communication delay time meet the preset conditions. The final deployment scheme is determined by the frequency configuration of the relay node, which ensures that both the signal loss rate and communication delay time meet the preset conditions.

10. A system for implementing the Internet of Things-based dynamic monitoring and management method for karst water as described in any one of claims 1 to 9, characterized in that, include: The environmental perception and modeling module is used to collect structural data and environmental parameters of karst pipelines, establish a propagation loss model based on the structural data and environmental parameters, and generate an initial signal attenuation distribution map that reflects the propagation characteristics of electromagnetic wave signals in the pipeline. The risk area identification module is used to analyze the initial signal attenuation distribution map, identify high-loss areas where the signal reflection intensity exceeds a preset threshold, and determine the high-loss areas as dynamic change influence areas characterizing geological instability. The node layout module is used to determine the location coordinates of relay nodes based on the structural data of the dynamically changing affected area and to generate a relay node deployment scheme. The communication parameter optimization module is used to iteratively adjust the transmission frequency band, transmission power and transmission angle of the relay nodes based on the relay node deployment scheme so that the network simulated transmission efficiency reaches the target threshold, and obtain the optimized frequency configuration combination, which includes the transmission frequency band, transmission power and transmission angle. The scheme matching and verification module is used to calculate the matching degree between the optimized frequency configuration combination and the structural data, and to perform simulation using the propagation loss model to generate the optimized signal attenuation distribution map. The final confirmation module is used to evaluate the signal propagation stability based on the optimized signal attenuation distribution map. If the signal loss rate and communication delay time both meet the preset conditions, the relay node deployment scheme and the optimized frequency configuration combination are confirmed as the final deployment scheme.

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