Underground space surveying and mapping and environment monitoring system and method

Abstract

An underground space surveying and mapping and environment monitoring system comprises a data acquisition unit, an edge calculation unit, a dynamic scheduling unit and a simulation optimization unit. The data acquisition unit is used for acquiring various parameter data; the edge computing unit receives various parameter data, and predicts future tasks, node energy consumption and network congestion risks based on historical data and real-time states; the dynamic scheduling unit is used for selecting sensor nodes according to future tasks, node energy consumption and network congestion risks, aggregating collected data of the sensor nodes, executing a low-delay routing algorithm, and performing dynamic adjustment to obtain a scheduling strategy; and the simulation optimization unit verifies the performance of the scheduling strategy and feeds back optimization parameters to the dynamic scheduling unit to adjust the scheduling strategy so as to obtain an optimized scheduling strategy. According to the invention, a multi-objective optimization strategy and an energy monitoring and scheduling mechanism are combined, so that the network can realize low-energy-consumption and high-efficiency real-time data transmission and monitoring tasks in a complex underground space environment.

Description

Technical Field

[0001] The present invention relates to the field of wireless sensor network technology, and more specifically, to an underground space mapping and environment monitoring system and method based on a low-latency routing algorithm and an energy optimization strategy. Background Art

[0002] With the expansion of underground space development and utilization, underground space mapping and environmental monitoring are becoming increasingly important in urban construction, mining, tunnel construction, and other fields. Traditional methods rely on manual labor or mechanical equipment, resulting in high energy consumption, low efficiency, and high data transmission latency. Complex underground environments make wireless sensor networks (WSNs) deployment and management difficult, especially in confined spaces. Achieving efficient and stable data acquisition and transmission while reducing energy consumption and latency are key challenges for current technologies.

[0003] Currently, data transmission latency is high, network paths are not optimized, congestion is difficult to predict, and the fixed topology structure leads to increased delays; energy consumption is uneven, and static energy management does not take into account changes in node status, resulting in premature failure of some nodes; network congestion and data loss, existing congestion control cannot predict and adjust in real time, and congestion is difficult to alleviate in time; topology change adaptability is poor, and static routing algorithms cannot cope with dynamic changes in node status and location; task coordination management efficiency is low, multi-device collaborative task scheduling lacks flexibility, and resources are seriously wasted, among other problems.

[0004] Therefore, the problems existing in the prior art need to be further improved and developed. Summary of the Invention

[0005] (1) Purpose of the invention: To solve the problems existing in the above-mentioned prior art, the purpose of the present invention is to provide an underground space mapping and environmental monitoring system and method to solve the key problems of low latency, energy optimization and network stability in space mapping and environmental monitoring systems.

[0006] (2) Technical Solution: To solve the above technical problems, this technical solution provides an underground space mapping and environmental monitoring system, including a data acquisition unit, an edge computing unit, a dynamic scheduling unit, and a simulation optimization unit; The data acquisition unit includes multiple sensor nodes for acquiring multiple parameter data; The edge computing unit receives a variety of parameter data and predicts future tasks, node energy consumption, and network congestion risks based on historical data and real-time status; The dynamic scheduling unit selects sensor nodes based on future tasks, node energy consumption, and network congestion risk, aggregates the collected data of the sensor nodes, and executes a low-latency routing algorithm to perform dynamic adjustments to obtain a scheduling strategy; The simulation optimization unit verifies the performance of the scheduling strategy, and feeds back the optimization parameters to the dynamic scheduling unit to adjust the scheduling strategy to obtain an optimized scheduling strategy.

[0007] Preferably, sensor nodes are deployed in underground spaces through unmanned mobile devices to collect various parameter data; the parameter data include environmental parameter data, spatial image data, and node status parameters.

[0008] Preferably, the edge computing unit first predicts the energy, bandwidth and computing resource requirements of the task based on historical task data, node energy status and real-time network status; , in, represents the remaining energy at time t; represents the initial energy of the node; Indicates the energy consumption rate of the current node; represents the time step, the frequency of node energy update; Finally, based on node load, traffic and topology changes, network congestion risk is predicted, and the congestion area and congestion degree are output.

[0009] Preferably, the low-latency routing algorithm includes a cluster routing submodule, an opportunistic routing submodule, and a multi-objective optimization submodule; the cluster routing submodule divides the sensor nodes into multiple clusters, and selects the global optimal cluster head node through a particle swarm optimization algorithm based on the node residual energy, the distance to the base station, and the load; The opportunistic routing submodule monitors the remaining energy, load and distance of the node to the target node in real time, and dynamically selects the node with sufficient energy and low load as the next hop transmission path; The multi-objective optimization submodule combines the minimum spanning tree algorithm with the communication distance and energy consumption as the dual objectives to optimize the transmission path between cluster heads and minimize the total transmission cost.

[0010] Preferably, the dynamic scheduling unit also includes a distributed predictive congestion control module; first, the node cache queue status, remaining energy and distance to the target node are collected in real time to calculate the node transmission gravity; secondly, the data flow is dynamically adjusted based on the transmission gravity, and a node with a gravity less than the first gravity threshold is selected as the transmission path; finally, combined with the hybrid CSMA / TDMA protocol, CSMA is used to avoid channel conflicts when the node is less than the first gravity threshold, and TDMA is used to allocate fixed time slots for transmission when the node is greater than or equal to the first gravity threshold.

[0011] Preferably, the dynamic scheduling unit further includes a prediction and matching module, specifically including pre-deployment of cluster head selection, path planning and task priority strategy based on the prediction results of the edge computing unit; Comparing the deviation between the preset strategy and the actual execution, and executing the preset strategy when the delay deviation is less than or equal to the first delay threshold and the energy consumption deviation is less than or equal to the first energy threshold; When the delay deviation is greater than the first delay threshold and / or the energy consumption deviation is greater than the first energy threshold, a new cluster head is selected by re-running the PSO algorithm based on the current node status, network load and task priority, and the transmission path is updated.

[0012] Preferably, the simulation optimization unit performs multi-index verification on the scheduling strategy based on NS-3 and MATLAB tools, including simulating the network load of multiple nodes, testing the end-to-end transmission delay; monitoring the node energy consumption rate; simulating high-traffic scenarios, testing the packet loss rate and throughput; The verification results are fed back to the dynamic scheduling unit to optimize the particle velocity parameters of the PSO algorithm and the weight coefficient of the MST.

[0013] Preferably, it also includes an energy management unit, which is connected to the data acquisition unit and the dynamic scheduling unit; includes adjusting the acquisition frequency of the sensor node; controlling the transmission power of the node; triggering the sleep mode of the low-energy node, activating the energy supply of the high-priority task first when the task priority is reduced, and delaying the execution of the low-priority task.

[0014] Preferably, the data acquisition unit, the edge computing unit, and the dynamic scheduling unit communicate with each other through a wireless sensor network, using a hybrid CSMA / TDMA protocol; when the node utilization is less than or equal to the first node utilization threshold, the CSMA mechanism is used, the node listens to the channel before transmission, and transmits immediately when idle; When the node utilization is greater than the first node utilization threshold, a TDMA mechanism is used to allocate a fixed transmission time slot to each node; When a second node utilization threshold is reached, the dynamic scheduling unit automatically triggers protocol switching according to the real-time node utilization; the second node utilization threshold is greater than the first node utilization threshold.

[0015] A method for underground space mapping and environmental monitoring, applicable to an underground space mapping and environmental monitoring system, specifically comprising: Step 1: Multiple sensor nodes acquire multiple parameter data; Step 2: The edge computing unit receives various parameter data and predicts future tasks, node energy consumption, and network congestion risks based on historical data and real-time status. Step 3: The dynamic scheduling unit selects sensor nodes based on future tasks, node energy consumption, and network congestion risk, aggregates the collected data of the sensor nodes, and executes a low-latency routing algorithm to perform dynamic adjustments to obtain a scheduling strategy; Step 4: The simulation optimization unit verifies the performance of the scheduling strategy and feeds back the optimization parameters to the dynamic scheduling unit to adjust the scheduling strategy to obtain the optimized scheduling strategy.

[0016] (III) Beneficial effects: The present invention provides an underground space mapping and environmental monitoring system and method. First, through task prediction and network status prejudgment, task strategies are deployed in advance, routing and task scheduling are adjusted dynamically, and data transmission delays are significantly reduced, so that the system can respond promptly and efficiently complete real-time tasks in a complex and dynamic underground environment. Secondly, an intelligent energy management module is introduced to monitor the energy status and consumption rate of nodes in real time, and the working frequency and data transmission strategy of nodes are optimized in combination with the prejudgment mechanism to extend the service life of nodes and avoid task failure or network interruption due to energy depletion. Thirdly, through distributed predictive congestion control (DPCC) and dynamic routing adjustment, possible congested areas in the network are effectively predicted, and data traffic and path selection are dynamically adjusted, avoiding the problems of inflexible path selection or insufficient congestion control in the prior art. Routing strategies and task priorities can be adjusted according to dynamic changes in task requirements, node status, network load, etc., and through multi-objective optimization routing algorithms and prejudgment mechanisms, frequent changes in network topology in confined environments such as underground spaces can be flexibly responded to. Finally, through prediction and matching strategies, the optimal cluster head node and data transmission path are selected in advance according to task requirements, reducing redundant transmission, lowering delay and energy consumption, and avoiding task failure caused by excessive node load or insufficient energy. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a structural diagram of an underground space mapping and environment monitoring system of the present invention; Figure 2 It is a flow chart of the steps of an underground space mapping and environmental monitoring method of the present invention. DETAILED DESCRIPTION

[0018] The present invention is further described in detail below in conjunction with preferred embodiments. More details are set forth in the following description to facilitate a full understanding of the present invention. However, the present invention can obviously be implemented in a variety of other ways different from the description. Those skilled in the art can make similar generalizations and deductions based on actual application situations without violating the connotation of the present invention. Therefore, the scope of protection of the present invention should not be limited by the content of this specific embodiment.

[0019] The accompanying drawings are schematic diagrams of embodiments of the present invention. It should be noted that the drawings are merely examples and are not drawn to scale, and should not be used to limit the actual scope of protection claimed in the present invention.

[0020] An underground space mapping and environmental monitoring system, such as Figure 1As shown, it includes a data acquisition unit, an edge computing unit, a dynamic scheduling unit, a simulation optimization unit, and an energy management unit. The data acquisition unit includes a variety of sensor nodes for acquiring a variety of parameter data. The edge computing unit receives a variety of parameter data and predicts future tasks, node energy consumption, and network congestion risks based on historical data and real-time status. The dynamic scheduling unit selects sensor nodes based on future tasks, node energy consumption, and network congestion risks, aggregates the collected data of the sensor nodes, and executes a low-latency routing algorithm to perform dynamic adjustments to obtain a scheduling strategy. The simulation optimization unit verifies the performance of the scheduling strategy and feeds back the optimization parameters to the dynamic scheduling unit to adjust the scheduling strategy to obtain an optimized scheduling strategy. The energy management unit is connected to the data acquisition unit and the dynamic scheduling unit respectively; the energy management unit includes adjusting the acquisition frequency of the sensor nodes; controlling the transmission power of the nodes; triggering the sleep mode of the low-energy nodes, and activating the energy supply of the high-priority tasks first when the task priority is reduced, and delaying the execution of the low-priority tasks.

[0021] A method for underground space mapping and environmental monitoring is applicable to an underground space mapping and environmental monitoring system, such as Figure 2 As shown, the specific steps include: Step 1: Multiple sensor nodes acquire multiple parameter data; Step 2: The edge computing unit receives various parameter data and predicts future tasks, node energy consumption, and network congestion risks based on historical data and real-time status. Step 3: The dynamic scheduling unit selects sensor nodes based on future tasks, node energy consumption, and network congestion risk, aggregates the collected data of the sensor nodes, and executes a low-latency routing algorithm to perform dynamic adjustments to obtain a scheduling strategy; Step 4: The simulation optimization unit verifies the performance of the scheduling strategy and feeds back the optimization parameters to the dynamic scheduling unit to adjust the scheduling strategy to obtain the optimized scheduling strategy.

[0022] More specifically, the data collection unit includes an unmanned mobile device and multiple sensor nodes, which are deployed in underground spaces via the unmanned mobile device. The sensor nodes collect multiple parameter data, including environmental parameter data, spatial image data, and node status parameters.

[0023] The unmanned mobile devices include drones, unmanned vehicles, and quadruped robots. Each unmanned mobile device has autonomous navigation, positioning, and sensor acquisition capabilities, enabling it to flexibly adapt to complex environments. The sensor nodes include sensors such as temperature and humidity sensors, gas concentration sensors, and pressure sensors, as well as imaging devices such as lidar and cameras.

[0024] A sensor network is composed of multiple sensor nodes, each of which has wireless communication capabilities. The sensor nodes include sensors and imaging devices. The sensors collect underground environmental data and monitor node status in real time, while the imaging devices acquire underground space data in real time, ensuring comprehensive and accurate acquisition of underground space information.

[0025] More specifically, the edge computing unit performs the following operations: The edge computing unit includes edge computing nodes arranged around the sensor nodes. After receiving multiple parameter data, the edge computing nodes perform data preprocessing and feature extraction, and based on task prediction and network status analysis, predict future task requirements, node energy status, network congestion, etc.

[0026] Step 201: Preprocess the parameter data through the edge computing node to obtain unified parameter data.

[0027] Specifically, the collected parameter data is received and preprocessed. The preprocessing includes data cleaning, data formatting, and data aggregation. Data cleaning is performed to remove noise and outliers. Data cleaning can be performed using a filtering algorithm (such as a Kalman filter) to remove invalid data generated by transmission interference and device errors.

[0028] Data formatting converts acquired parameter data into a unified format based on different task requirements, facilitating subsequent analysis and calculations. Data is standardized to eliminate dimensional differences (e.g., unifying the units of temperature, humidity, and pressure data) to ensure the accuracy of subsequent analysis.

[0029] The data aggregation performs spatiotemporal aggregation of parameter data of the same type (e.g., fusion of data from multiple nodes in the same region) to reduce redundant transmission. The data aggregation can also be performed by compressing the pre-processed data using a lightweight compression algorithm to reduce transmission bandwidth requirements.

[0030] By performing real-time preprocessing through the edge computing node, delay can be reduced.

[0031] Step 202: perform data fusion and feature extraction on the unified parameter data, and calculate the resource requirements and path optimization of the task.

[0032] Predict resource requirements, latency requirements, and priorities for future tasks based on historical task data, node energy consumption, and network status. Use machine learning algorithms and historical data analysis methods to generate predictive strategies for task execution, including task scheduling and data transmission paths.

[0033] Specifically, the LiDAR point cloud data is voxelized, converting the 3D spatial data into a regular voxel grid to extract spatial geometric features (such as obstacle outlines and spatial topology). Edge detection and target recognition (such as using the HOG+SVM algorithm) are performed on the camera image data to extract features of objects in the environment (such as pipes, cracks, and structural outlines).

[0034] Extract the state parameters of sensor nodes (remaining energy, communication signal strength, load rate) to form the node health status feature vector. Analyze network topology characteristics (such as node connectivity and cluster head node load distribution) to identify potential network bottlenecks.

[0035] Converting raw data into a more informative and easily transferable format reduces data redundancy and optimizes data flow. Feature extraction extracts key features such as object shape, position, and size from the data, while voxelization further converts spatial data into a voxel format, improving data compression and processing efficiency.

[0036] Step 203: Based on the resource requirements and path optimization of the tasks, predict future tasks, node energy consumption, and network congestion risks.

[0037] Specifically, the edge computing node predicts the resource requirements, execution period, network load, etc. of the task based on task prediction and network status analysis, combined with historical data and real-time data, generates task scheduling and cluster head selection plans, and presets the data transmission path.

[0038] Based on historical task data and real-time environment data, the resource requirements of the task (such as energy, bandwidth, computing power, etc.) are predicted, as shown in formula (1): (1), in, Indicates the task resource requirements (including energy, bandwidth, etc.); Represents historical task data (such as execution time, resource consumption, and delay); Indicates the current energy consumption rate of the node; It represents real-time information such as node load and network status; Δt represents the time window, the expected time for task execution.

[0039] The node energy state is shown in formula (2): (2), in, represents the remaining energy at time t; represents the initial energy of the node; Indicates the energy consumption rate of the current node; Indicates the time step, the frequency of node energy update.

[0040] Finally, based on the node load, traffic and topology changes, the network congestion risk is predicted, and the congestion area and congestion degree are output, as shown in formula (3): (3), in, Indicates network load (predicted congestion); Indicates the load condition of the node; Indicates the node's traffic (amount of data transmitted); Indicates changes in network topology (node ​​connectivity, signal strength, etc.); Indicates the time window and the predicted duration of the network load.

[0041] More specifically, the dynamic scheduling unit performs the following operations: Based on predicted task requirements, network status, node energy, and other information, the system selects appropriate cluster heads and data transmission paths, and dynamically adjusts task priorities and execution order. This ensures that tasks are executed smoothly according to their priorities, avoiding delays or failures caused by excessive energy consumption or network congestion. Based on task priorities, node energy status, and network load, the system dynamically selects cluster heads and deploys appropriate path selection strategies. Task execution order and paths are adjusted in real time to ensure timely completion and minimize data transmission delays.

[0042] The dynamic scheduling unit includes a low-latency routing module, a distributed predictive congestion control module, and a prejudgment and matching scheduling module. The low-latency routing module includes a cluster routing submodule, an opportunistic routing submodule, and a multi-objective optimization submodule. Based on node residual energy, transmission distance, and network load, it combines cluster routing, opportunistic routing, particle swarm optimization (PSO), and minimum spanning tree (MST) algorithms to dynamically select cluster head nodes and data transmission paths to minimize data transmission latency.

[0043] The cluster routing submodule divides sensor nodes into multiple clusters. Cluster routing is an initial stage of routing where sensor nodes are grouped, and each group selects a cluster head node. The cluster head node is responsible for data aggregation and transmission. This cluster routing algorithm can reduce redundant data transmission and uneven load, while improving network energy efficiency.

[0044] By grouping sensor nodes into clusters and selecting cluster heads for data aggregation and forwarding, redundant transmission in the network is reduced, improving energy efficiency. Among energy-optimized low-latency routing algorithms, clustering algorithms can effectively reduce redundant communication between nodes and reduce energy consumption by aggregating data, thereby extending network life.

[0045] Sensor nodes are deployed and grouped according to specific rules to form clusters. Each cluster is managed by a cluster head node. Cluster head selection is based on the node's remaining energy and distance from the base station, ensuring optimal cluster head performance while preventing some cluster heads from prematurely depleting their energy due to overload. Each cluster head collects and aggregates data from all sensor nodes in its cluster to reduce redundant data transmission. This aggregated data is then transmitted to the base station via an optimized transmission path.

[0046] The energy monitoring module monitors the energy consumption of the entire network in real time, ensuring that each node and cluster head in the network can complete its tasks before depleting its battery. The energy optimization module adjusts data collection frequency and transmission strategies to reduce energy consumption and extend network life. It dynamically selects transmission paths based on each node's energy and distance information. Path optimization considers both energy and distance to ensure minimal latency and energy consumption during data transmission.

[0047] The core goal of the cluster routing algorithm in this paper is to improve the performance of wireless sensor networks in confined spaces (such as mines and tunnels) by optimizing energy consumption and latency in the sensor network. The main steps of the algorithm are as follows: Sensor nodes are dynamically grouped within the network, each managed by a cluster head node. Using a clustering algorithm, nodes are assigned to clusters based on their location, energy status, and communication load. This process effectively reduces redundant data transmission within the network and evenly distributes the load across cluster head nodes, preventing some cluster head nodes from being overloaded and depleted.

[0048] Cluster head node selection is based on factors such as remaining energy, distance from the base station, and network load. The algorithm prioritizes nodes with sufficient energy and close proximity to the base station as cluster heads to ensure efficient data transmission and low latency. Cluster heads not only aggregate data but also transmit it, making energy consumption and load management crucial.

[0049] The cluster head node is responsible for receiving and aggregating data from sensor nodes within its cluster. Data aggregation helps reduce redundant transmissions and optimize network bandwidth usage. After aggregating the data, the cluster head transmits it to the base station via the selected optimal path. The transmission path is selected based on factors such as the distance between the node and the base station, the remaining energy of the cluster head, and network load.

[0050] To further reduce transmission latency and energy consumption, the algorithm optimizes data transmission by dynamically selecting the optimal transmission path. This path optimization not only considers the energy status of each node but also adjusts the data transmission path based on each node's distance from the cluster head. Nodes with sufficient energy and low communication load are prioritized as relay nodes to avoid bottlenecks or node overload in the network.

[0051] The energy monitoring module monitors the energy consumption of each node and cluster head in the network in real time. When the energy of a node or cluster head drops below a preset threshold, the algorithm automatically adjusts the node's operating state, such as reducing data collection frequency and lowering transmission power, thereby optimizing energy use and extending network life.

[0052] To prevent certain cluster heads from depleting their energy too quickly and affecting network performance, the algorithm also incorporates a node rotation mechanism. By regularly updating cluster head roles, it ensures that energy consumption across all nodes is as balanced as possible, preventing network interruptions or transmission failures.

[0053] The clustering routing algorithm achieves low latency and energy optimization in a confined space by combining multiple strategies, including node grouping, cluster head selection, data aggregation, path optimization, and energy management. This algorithm not only effectively reduces network congestion but also maximizes network efficiency within limited energy resources.

[0054] The opportunistic routing submodule monitors the remaining energy, load and distance of the node to the target node in real time, and dynamically selects a node with sufficient energy and low load as the next hop transmission path.

[0055] Opportunistic routing selects the next hop node based on its remaining energy and distance to the sink node. Based on the principle of opportunistic routing, data is transmitted at the appropriate time, balancing network energy consumption. In energy-optimized low-latency routing algorithms, opportunistic routing combines with clustering algorithms to balance energy distribution, reduce node burden, and thus reduce latency and extend network life.

[0056] Each sensor node monitors its remaining energy, distance from the cluster head node, and current load in real time. This information forms the basis of the opportunistic routing algorithm, determining whether a node should be selected as part of a data transmission path. Based on node status monitoring, the routing selection module calculates and selects the optimal transmission path based on factors such as each node's remaining energy, load, and distance to the target node. The algorithm dynamically adjusts the routing strategy to ensure minimal network energy consumption and latency. Once path selection is complete, the opportunistic routing algorithm selects the optimal next-hop node for data transmission based on the current network status and the selected path. This allows data transmission to proceed between nodes with sufficient energy and low load, thus avoiding network congestion and energy depletion.

[0057] The energy monitoring module tracks the energy usage of the entire network in real time and adjusts the data collection frequency and transmission strategy based on the energy consumption of each node. The energy optimization module ensures that each node completes its task before its battery runs out, avoiding network interruptions caused by node energy depletion.

[0058] The core purpose of the opportunistic routing algorithm is to achieve low-latency, energy-optimized data transmission in wireless sensor networks within confined spaces by dynamically adjusting routing paths. The algorithm is designed to select the optimal transmission path based on the real-time status of sensor nodes (such as remaining energy, communication load, and distance to the target node), ensuring data transmission in the shortest possible time while minimizing energy consumption.

[0059] Each sensor node regularly monitors its remaining energy, communication load, and distance to other nodes. A node's remaining energy is a key factor in the algorithm's decision-making process, prioritizing nodes with sufficient energy for data transmission. This information is then transmitted to the cluster head or network optimization module for subsequent routing decisions.

[0060] Based on each node's status information, the algorithm dynamically selects the next-hop node. Routing selection considers not only the node's remaining energy but also its communication load and distance. The algorithm prioritizes nodes with sufficient remaining energy and close proximity to the target node as data relay nodes to avoid energy depletion and network congestion. After selecting the optimal next-hop node, data is transmitted along the selected path. The transmission path is dynamically adjusted based on the real-time network status. Unlike static routing algorithms, opportunistic routing algorithms can adapt to changing network conditions, ensuring efficient data transmission with low energy consumption.

[0061] The energy monitoring module tracks each node's energy consumption in real time and adjusts data collection frequency and transmission strategies based on the monitoring results. For nodes that consume energy too quickly, the system reduces their activity frequency or delays data transmission to prevent node failure or network interruption due to energy depletion.

[0062] The opportunistic routing algorithm uses a feedback mechanism to optimize and adjust the routing strategy after each transmission based on current network conditions (such as network congestion and node energy changes). Through simulation and real-time feedback, the algorithm continuously improves data transmission paths, ensuring continuous optimization of overall network performance.

[0063] Through real-time monitoring of node status, intelligent routing, dynamic data path adjustment, and energy optimization, the opportunistic routing algorithm enables efficient, low-latency data transmission in complex, confined spaces, while also extending the network's lifespan. This algorithm is particularly well-suited for wireless sensor network applications requiring low latency and high energy efficiency, such as underground space mapping and environmental monitoring.

[0064] The multi-objective optimization submodule combines particle swarm optimization (PSO) and minimum spanning tree (MST) algorithms, takes communication distance and energy consumption as dual objectives, optimizes the transmission path between cluster heads, and minimizes the total transmission cost.

[0065] By simulating the behavior of particle swarms, cluster head selection is optimized, taking into account factors such as the node's remaining energy and distance from the base station to ensure the global optimality of cluster head selection. In the improved LEACH algorithm, the PSO algorithm optimizes cluster head selection, avoids uneven energy consumption, and improves the energy efficiency of the network.

[0066] In the particle swarm optimization algorithm, the particle positions and velocities must first be initialized. Each particle represents a candidate solution (e.g., cluster head selection or path selection), and its position and velocity reflect the current state of the solution. The initial position is typically randomly selected, and the velocity is adjusted as needed to find the optimal solution in the search space.

[0067] For each particle, the quality of its solution is evaluated by calculating its fitness. The fitness function evaluates particle performance based on factors such as the energy efficiency of cluster head selection, transmission path latency, and network load. Particles with higher fitness represent better solutions (i.e., cluster heads or paths with high energy efficiency and low latency).

[0068] Based on each particle's fitness, its velocity and position are updated. The update rules are typically based on classic formulas from particle swarm optimization algorithms. A particle's position and velocity are influenced by both the current particle's optimal position and the global optimal position. By continuously adjusting the particle's direction and velocity, the particle gradually approaches the optimal solution.

[0069] In the particle swarm optimization algorithm, the particle with the highest fitness value is selected as the current optimal solution. In the cluster head selection problem, particles with high fitness represent a cluster head selection solution with minimal energy consumption and shortest transmission delay; in the path selection problem, particles with high fitness represent the optimal transmission path.

[0070] Throughout the entire process, the energy monitoring module tracks the energy consumption of each node and cluster head in real time to ensure that the system operates efficiently within energy constraints. The data flow optimization module optimizes data flow and transmission strategies based on the optimal path and cluster head selection scheme provided by the particle swarm optimization algorithm, thereby reducing energy consumption and improving transmission efficiency.

[0071] Using the PSO algorithm, this invention efficiently selects cluster heads and transmission paths in wireless sensor networks, balancing energy consumption and latency. The particle swarm optimization algorithm adaptively searches for optimal solutions and continuously iterates to improve network energy efficiency and performance. This design is suitable for a variety of complex network environments, particularly wireless sensor network applications requiring efficient and stable communication in confined spaces.

[0072] By constructing a minimum spanning tree, the data transmission path between cluster heads is optimized, the communication distance is reduced, and energy consumption is reduced. In the PSO-ACO-LEACH algorithm, the minimum spanning tree is used to select the optimal transmission path between cluster heads, thereby maximizing the energy efficiency of the network.

[0073] In the minimum spanning tree (MST) algorithm, the communication distance between cluster head nodes must first be calculated. Each cluster head node calculates the communication cost based on its distance to other cluster heads. This distance is typically proportional to energy consumption, making calculating the communication distance a key step in optimizing network performance.

[0074] Based on the communication distance between cluster heads, the minimum spanning tree algorithm selects the paths with the lowest cost to construct the optimal transmission path between cluster heads. This minimum spanning tree ensures that each cluster head node in the network can connect to other cluster heads via the shortest path, thereby reducing communication distances and lowering energy consumption. After constructing the minimum spanning tree, the path optimization module further optimizes the transmission path. The goal of path optimization is to reduce energy consumption by shortening the communication distance between cluster heads, while ensuring stable and efficient data transmission. This optimized path not only improves network energy efficiency but also reduces data transmission latency.

[0075] The energy monitoring module tracks the energy consumption of each node and cluster head in real time. By adjusting data collection frequency and transmission strategies, it ensures that cluster heads and sensor nodes operate stably and for extended periods of time within limited energy resources. The data flow optimization module, based on optimized transmission paths and cluster head selection strategies, further optimizes data flow and transmission strategies, ensuring improved overall network performance.

[0076] The minimum spanning tree (MST) algorithm is shown in formula (4). Its purpose is to select an edge set E such that the total weight of the edges is Minimum, while ensuring that all nodes in the graph are connected and do not form cycles.

[0077] (4), Among them, MST represents the edge set of the minimum spanning tree; Represents the set of all edges in the graph, each edge connecting two nodes , and has a weight ; Represents an edge The weight of , usually represents the communication distance or energy consumption.

[0078] By using a minimum spanning tree algorithm, the present invention can effectively reduce the communication distance between cluster heads, thereby minimizing energy consumption and transmission delay. This algorithm not only optimizes the data transmission path but also improves the overall performance of the network, making it particularly suitable for energy-constrained and delay-sensitive wireless sensor network applications.

[0079] The distributed predictive congestion control module monitors network traffic, node load, and latency in real time to predict potential congestion areas and dynamically adjust data traffic and transmission paths to avoid increased latency and packet loss caused by congestion. This allows for early prediction and relief of network congestion, reducing latency and packet loss.

[0080] Specifically, the node cache queue status, remaining energy, and distance to the target node are first collected in real time to calculate the node transmission gravity. Secondly, the data flow is dynamically adjusted based on the transmission gravity, and nodes with a gravity less than the first gravity threshold are selected as the transmission path. Finally, a hybrid CSMA / TDMA protocol is combined. When the node gravity is less than the first gravity threshold, CSMA is used to avoid channel conflicts. When the node gravity is greater than or equal to the first gravity threshold, TDMA is used to allocate fixed time slots for transmission.

[0081] By predicting network congestion and dynamically adjusting it, data traffic distribution is controlled to avoid increased latency caused by network congestion. In low-latency routing algorithms based on congestion control, the DPCC mechanism is used to predict congestion. Combined with a hybrid CSMA / TDMA mechanism, it optimizes data flow within the network and reduces latency. Congestion prediction: The first key step in DPCC is to predict potential future network congestion by analyzing historical data. Each sensor node and cluster head node regularly reports network status, including traffic flow, load, and latency, to the control center. Based on this information, the DPCC module assesses potential congestion in the network and predicts which areas or nodes are likely to experience congestion. Based on the output of the congestion prediction module, the traffic regulation module dynamically adjusts data traffic to avoid network congestion. This module adjusts the data transmission rate based on the real-time network status to avoid congestion caused by excessive data volume. By adjusting transmission times and node transmission frequencies, network traffic is optimized to ensure timely data transmission.

[0082] When congestion occurs, the system selects uncongested paths for data transmission. By selecting less-loaded relay nodes or cluster heads, congested paths are avoided, reducing latency and packet loss, ensuring rapid data transmission to the target node. The DPCC system also works in conjunction with the energy management system to monitor node and cluster head energy consumption in real time. When node energy is low, the system adjusts its data collection frequency and transmission strategy to avoid excessive transmission on high-energy-consuming nodes, ensuring long-term stable network operation. The data flow optimization module dynamically adjusts data flow and transmission paths based on node energy status and network traffic to balance energy consumption and data transmission efficiency.

[0083] Predict future congestion based on historical traffic and load data. Dynamically adjust data traffic based on predicted congestion information to avoid excessive network load. Select uncongested paths for data transmission to ensure smooth data transmission.

[0084] Combined with energy monitoring, the working status of nodes and cluster heads is optimized to extend the life cycle of the network.

[0085] DPCC can predict network congestion in advance, preventing network congestion and reducing latency and packet loss. Furthermore, by dynamically adjusting data flow and transmission paths, DPCC effectively optimizes network energy consumption, ensuring efficient and stable operation in constrained environments. This algorithm is particularly well-suited for complex and dynamically changing network environments, such as wireless sensor networks in confined spaces like mines and tunnels.

[0086] The prediction and matching module pre-deploys task strategies (including cluster head selection, path planning, and task prioritization) based on the predictions from the edge computing processing module. If the deviation between the prediction and actual situation is within the tolerance range, the pre-determined strategy is executed; otherwise, the optimal strategy is recalculated based on the current state. This strategy predicts and dynamically adjusts tasks based on historical task data and real-time environmental information, achieving more efficient task scheduling and path selection, significantly improving the system's real-time performance, energy efficiency, and stability.

[0087] The method includes pre-deploying cluster head selection, path planning and task priority strategies based on the prediction results of the edge computing unit; comparing the deviation between the preset strategy and the actual execution, and executing the preset strategy if the delay deviation is less than or equal to the first delay threshold and the energy consumption deviation is less than or equal to the first energy threshold; when the delay deviation is greater than the first delay threshold and / or the energy consumption deviation is greater than the first energy threshold, selecting a new cluster head by re-running the PSO algorithm based on the current node status, network load and task priority, and updating the transmission path.

[0088] The most suitable cluster head node is selected based on factors such as the node's remaining energy, distance from the base station, and node load, as shown in formula (5): (5), in, represents the optimal cluster head selection; represents the remaining energy of node i; represents the distance between node i and the base station; Indicates the load condition of node i.

[0089] As shown in formula (6), the optimal data transmission path is selected based on the remaining energy, load conditions and distance of the node: (6), in, Indicates the optimal path selected; represents the distance to node j (transmission path length); represents the residual energy of node j.

[0090] As shown in formula (7), the priority of the task is dynamically adjusted according to the urgency of the task, the node energy consumption rate, the network load, etc.: (7), in, Indicates the priority of the task; Indicates the urgency of the task; represents the node energy; Indicates network load; Represents the weight coefficient, which is adjusted according to different environments and task types.

[0091] The core goal is to predict task information, network conditions, and node status, enabling preemptive system decisions and deployments, thereby achieving efficient task scheduling, path selection, and low-latency data transmission. This strategy leverages the real-time computing capabilities of edge computing nodes to predict task execution based on historical data and real-time status. Based on these predictions, it selects appropriate routing strategies and task scheduling, ensuring real-time system response in confined environments, reducing energy consumption, and improving data transmission efficiency.

[0092] The energy management unit is connected to the data acquisition unit and the dynamic scheduling unit. It regulates the data acquisition frequency of sensor nodes, controls the transmission power of nodes, and triggers the sleep mode of low-energy nodes. When task priority decreases, it activates the energy supply for high-priority tasks and delays the execution of low-priority tasks. The energy management unit monitors the energy consumption rate of each node in real time and dynamically adjusts the data acquisition frequency, transmission power, and operating mode to balance node energy consumption and extend the network lifecycle.

[0093] The data acquisition unit, edge computing unit, and dynamic scheduling unit communicate with each other through a wireless sensor network, using a hybrid CSMA / TDMA protocol. When the node utilization is less than or equal to a first node utilization threshold, the CSMA mechanism is used, and the node listens to the channel before transmission and transmits immediately when idle. When the node utilization is greater than the first node utilization threshold, the TDMA mechanism is used to allocate a fixed transmission time slot to each node. When a second node utilization threshold is reached, the dynamic scheduling unit automatically triggers protocol switching according to the real-time node utilization. The second node utilization threshold is greater than the first node utilization threshold.

[0094] The simulation optimization module uses tools such as NS-3 and MATLAB to simulate and verify the low-latency routing algorithm, energy optimization strategy and congestion control mechanism, and feeds back the optimization parameters to the dynamic routing scheduling module and energy management module to improve the system's adaptability in complex underground environments.

[0095] The simulation optimization unit uses NS-3 and MATLAB tools to verify the scheduling strategy using multiple indicators, including simulating the network load of multiple nodes and testing the end-to-end transmission delay; monitoring the node energy consumption rate; simulating high-traffic scenarios and testing the packet loss rate and throughput; and feeding the verification results back to the dynamic scheduling unit to optimize the particle velocity parameters of the PSO algorithm and the weight coefficient of the MST.

[0096] As shown in formula (8), feedback adjustment is performed based on the real-time status to optimize task scheduling: (8), in, Indicates the adjusted resource allocation and task strategy; Indicates the current resource allocation and task status; Indicates the change in transmission delay; Indicates node load changes; Indicates the change of node remaining energy.

[0097] Example 1: Application to underground space surveying and mapping system.

[0098] The underground space mapping system uses unmanned devices (such as drones, autonomous vehicles, and quadruped robots) to rapidly deploy wireless sensor nodes. These devices, such as lidar and cameras, collect geographic location data for underground spaces. This collected data undergoes preliminary processing on edge devices (such as unmanned devices or edge computing nodes) and is transmitted back to a control center via a wireless sensor network (WSN). Unmanned devices rapidly deploy sensor nodes in constrained environments through the WSN, collecting data such as temperature, humidity, gas concentration, and pressure, laying the foundation for subsequent environmental monitoring and spatial analysis. The collected raw data is not only transmitted to the edge computing nodes for processing but also undergoes feature extraction and voxelization to convert it into a more informative and easily transmittable format, reducing data redundancy and optimizing data flow. The feature extraction stage extracts key features such as object shape, position, and size from the data, while the voxelization process further converts the spatial data into a voxel format, improving data compression and processing efficiency.

[0099] After these processes, the data is optimized using a low-latency routing algorithm and quickly and efficiently transmitted back to the control center. During this process, the low-latency routing algorithm ensures data transmission in the shortest possible time. In conjunction with the energy management module, it dynamically adjusts data collection frequency and transmission strategies to reduce device energy consumption and maximize operating time. The energy management module also monitors device energy consumption in real time, ensuring system stability during extended operation and preventing interruptions caused by rapid battery drain.

[0100] By leveraging simulation and optimization systems (such as NS-3 and MATLAB), this invention enables pre-operation optimization, ensuring that the system can adjust tasks and data transmission strategies based on actual needs under varying environmental conditions, thereby maximizing system efficiency and transmission quality. Ultimately, the control center monitors the status of underground spaces in real time through data analysis and decision-making, ensuring efficient and cost-effective spatial mapping and real-time environmental monitoring in complex and constrained environments.

[0101] This system achieves efficient collaborative operation of unmanned equipment in underground spaces through the comprehensive application of edge computing, low-latency routing algorithms, energy management, and data optimization technologies. This makes underground space mapping and environmental monitoring more accurate, flexible, and scalable, greatly improving the efficiency and quality of surveying and mapping work. It is especially suitable for complex underground environments that are difficult to reach with traditional methods, such as mines, tunnels, and wartime bunkers.

[0102] This system's low-latency routing algorithm optimizes data transmission paths in wireless sensor networks by combining cluster routing, opportunistic routing, and load balancing strategies, aiming to minimize latency and energy consumption. First, the cluster routing algorithm divides sensor nodes into clusters and selects appropriate cluster heads based on their remaining energy, distance from the base station, and communication load. The cluster head nodes are responsible for data aggregation and transmission, reducing redundant data transmission and effectively lowering energy consumption and latency.

[0103] Second, opportunistic routing algorithms dynamically select data transmission paths based on the real-time status of each node in the network (such as remaining energy, load, and distance to the target node). This algorithm ensures that data is transmitted using nodes with sufficient energy and low network load as the next hop, thus avoiding transmission failures or increased latency caused by energy depletion or congestion at some nodes.

[0104] To further improve system performance, a load balancing algorithm evenly distributes data flows, avoiding overloading of a single cluster head node. This balances the load across the entire network, reducing network bottlenecks and ensuring stable data transmission with low latency.

[0105] On this basis, the energy management module monitors the energy consumption of each node in real time and dynamically adjusts the node's data collection frequency and transmission strategy to ensure that each device can continue to operate under limited energy. The data return mechanism transmits data from the sensor node to the base station through an optimized low-latency routing algorithm, ensuring that the data can be transmitted back to the control center in the shortest possible time.

[0106] Combined with simulation systems (such as NS-3 and MATLAB), the entire low-latency routing algorithm can be validated and optimized in different network environments, ensuring optimal performance in a variety of application scenarios, meeting real-time requirements while ensuring efficient energy use. This design is particularly suitable for sensor network applications such as underground space mapping, smart city monitoring, and environmental monitoring, which have strict requirements on latency and energy efficiency. The underground space mapping system of the present invention utilizes a multi-objective optimized low-latency routing algorithm, combining low latency, energy optimization, and congestion control strategies to achieve efficient and stable underground space data transmission. The system rapidly deploys wireless sensor nodes using unmanned devices (such as drones, autonomous vehicles, and quadruped robots), and uses devices such as lidar and cameras to collect geographic location data of underground spaces. This data is first processed on edge computing nodes, including feature extraction and voxelization, to reduce data volume and increase information density. The processed data is then transmitted using an optimized low-latency routing algorithm that comprehensively considers network latency, energy consumption, and congestion to dynamically select the optimal transmission path, ensuring data transmission in the shortest possible time while minimizing energy consumption.

[0107] This routing algorithm utilizes a multi-objective optimization mechanism to balance energy consumption and network load while ensuring low-latency data transmission. By incorporating a congestion control strategy, the algorithm predicts and adjusts data flows based on real-time network conditions, avoiding delays caused by network congestion. Data collected by sensor nodes is transmitted back to the base station via a wireless sensor network (WSN). Optimized routing paths ensure rapid and efficient data transmission. Furthermore, an energy management module monitors device energy consumption in real time, adjusting data collection frequency and transmission strategies to ensure energy efficiency over extended periods of operation and extend the system's operating life.

[0108] Supported by simulation and optimization systems (such as NS-3 and MATLAB), this system can be validated for performance before actual deployment, ensuring efficient operation under diverse environmental conditions. Ultimately, the control center receives and analyzes data, adjusting mapping tasks based on real-time feedback to ensure overall system stability and efficiency. This system provides a low-cost, high-efficiency solution for underground space mapping and real-time environmental monitoring, particularly suitable for complex and confined environments such as mines and tunnels. The multi-objective optimized low-latency routing algorithm of this invention achieves efficient data transmission and network optimization in underground space mapping systems by combining low-latency, energy optimization, and congestion control strategies. The system utilizes unmanned devices (such as drones, autonomous vehicles, and quadruped robots) to rapidly deploy sensor nodes, which collect geographic location data of underground spaces using devices such as lidar and cameras. The data collected by these sensor nodes is first transmitted to a cluster head node for aggregation and processing. The data is then optimized using a low-latency routing algorithm to select the optimal transmission path and transmit the data to the base station with minimal latency. The low-latency routing algorithm combines energy optimization and congestion control strategies to minimize latency while maximizing energy efficiency and avoiding network congestion.

[0109] During algorithm implementation, the energy optimization module adjusts data collection frequency and transmission strategies to reduce unnecessary energy consumption and extend device life. The congestion control module monitors network status in real time, predicting and adjusting data flows to avoid delays and packet loss caused by congestion. These optimization mechanisms enable the system to maintain efficient data transmission and low energy consumption, ensuring smooth progress of surveying and mapping tasks, particularly in complex and confined underground environments.

[0110] The present invention provides a system and method for underground space mapping and environmental monitoring. First, through task prediction and network status pre-judgment, task strategies are deployed in advance, routing and task scheduling are dynamically adjusted, and data transmission latency is significantly reduced, enabling the system to promptly respond to and efficiently complete real-time tasks in complex and dynamic underground environments. Second, an intelligent energy management module is introduced to monitor node energy status and consumption rate in real time. Combined with a pre-judgment mechanism, the module optimizes node operating frequency and data transmission strategies, extending node lifespan and preventing task failures or network outages due to energy depletion. Third, distributed predictive congestion control (DPCC) and dynamic routing adjustments effectively predict possible congested areas in the network and dynamically adjust data flow and path selection, avoiding the inflexible path selection and insufficient congestion control issues of existing technologies. Routing strategies and task priorities can be adjusted based on dynamic changes in task requirements, node status, network load, and other factors. A multi-objective optimization routing algorithm and pre-judgment mechanism flexibly address the frequent changes in network topology in confined environments such as underground spaces. Finally, through pre-judgment and matching strategies, the optimal cluster head node and data transmission path are pre-selected based on task requirements, reducing redundant transmission, latency, and energy consumption, and avoiding task failures due to excessive node load or insufficient energy.

[0111] The above content is an explanation of the preferred embodiments of the present invention, which can help those skilled in the art to more fully understand the technical solutions of the present invention. However, these embodiments are merely illustrative, and it cannot be determined that the specific implementation methods of the present invention are limited to the description of these embodiments. For those skilled in the art of the present invention, without departing from the concept of the present invention, several simple deductions and transformations can be made, which should be deemed to fall within the scope of protection of the present invention.

Claims

1. An underground space mapping and environmental monitoring system, characterized in that: It includes data acquisition unit, edge computing unit, dynamic scheduling unit and simulation optimization unit; The data acquisition unit includes multiple sensor nodes for acquiring multiple parameter data; The edge computing unit receives a variety of parameter data and predicts future tasks, node energy consumption, and network congestion risks based on historical data and real-time status; The dynamic scheduling unit selects sensor nodes based on future tasks, node energy consumption, and network congestion risk, aggregates the collected data of the sensor nodes, and executes a low-latency routing algorithm to perform dynamic adjustments to obtain a scheduling strategy; The simulation optimization unit verifies the performance of the scheduling strategy, and feeds back the optimization parameters to the dynamic scheduling unit to adjust the scheduling strategy to obtain an optimized scheduling strategy.

2. The underground space mapping and environmental monitoring system according to claim 1, characterized in that: Sensor nodes are deployed in underground spaces through unmanned mobile devices to collect various parameter data; the parameter data include environmental parameter data, spatial image data, and node status parameters.

3. The underground space mapping and environment monitoring system according to claim 1, characterized in that: The edge computing unit first predicts the energy, bandwidth and computing resource requirements of the task based on historical task data, node energy status and real-time network status; The calculation formula of the node energy state is ,in, represents the remaining energy at time t; represents the initial energy of the node; Indicates the energy consumption rate of the current node; represents the time step, the frequency of node energy update; Finally, based on node load, traffic and topology changes, network congestion risk is predicted, and the congestion area and congestion degree are output.

4. The underground space mapping and environmental monitoring system according to claim 1, characterized in that: The low-latency routing algorithm includes a cluster routing submodule, an opportunistic routing submodule, and a multi-objective optimization submodule; the cluster routing submodule divides the sensor nodes into multiple clusters and selects the global optimal cluster head node based on the node residual energy, distance to the base station, and load through the particle swarm optimization algorithm; The opportunistic routing submodule monitors the remaining energy, load and distance of the node to the target node in real time, and dynamically selects the node with sufficient energy and low load as the next hop transmission path; The multi-objective optimization submodule combines the minimum spanning tree algorithm with the communication distance and energy consumption as the dual objectives to optimize the transmission path between cluster heads and minimize the total transmission cost.

5. The underground space mapping and environment monitoring system according to claim 1, characterized in that: The dynamic scheduling unit also includes a distributed predictive congestion control module; first, it collects the node cache queue status, remaining energy and distance to the target node in real time to calculate the node transmission gravity; second, it dynamically adjusts the data flow based on the transmission gravity and selects nodes with a gravity less than a first gravity threshold as the transmission path; finally, it combines the hybrid CSMA / TDMA protocol, adopts CSMA to avoid channel conflicts when the node gravity is less than the first gravity threshold, and adopts TDMA to allocate fixed time slots for transmission when the node gravity is greater than or equal to the first gravity threshold.

6. The underground space mapping and environment monitoring system according to claim 1, characterized in that: The dynamic scheduling unit also includes a prediction and matching module, which specifically includes pre-deployment of cluster head selection, path planning and task priority strategy based on the prediction results of the edge computing unit; Comparing the deviation between the preset strategy and the actual execution, and executing the preset strategy when the delay deviation is less than or equal to the first delay threshold and the energy consumption deviation is less than or equal to the first energy threshold; When the delay deviation is greater than the first delay threshold and / or the energy consumption deviation is greater than the first energy threshold, a new cluster head is selected by re-running the PSO algorithm based on the current node status, network load and task priority, and the transmission path is updated.

7. The underground space mapping and environment monitoring system according to claim 1, characterized in that: The simulation optimization unit uses NS-3 and MATLAB tools to verify the scheduling strategy with multiple indicators, including simulating the network load of multiple nodes and testing the end-to-end transmission delay; monitoring the node energy consumption rate; simulating high-traffic scenarios and testing the packet loss rate and throughput; The verification results are fed back to the dynamic scheduling unit to optimize the particle velocity parameters of the PSO algorithm and the weight coefficient of the MST.

8. The underground space mapping and environment monitoring system according to claim 1, characterized in that: It also includes an energy management unit, which is connected to the data acquisition unit and the dynamic scheduling unit; includes adjusting the acquisition frequency of the sensor node; controlling the transmission power of the node; triggering the sleep mode of the low-energy node, activating the energy supply of the high-priority task first when the task priority is reduced, and delaying the execution of the low-priority task.

9. The underground space mapping and environment monitoring system according to claim 1, characterized in that: The data acquisition unit, the edge computing unit, and the dynamic scheduling unit communicate with each other through a wireless sensor network, using a hybrid CSMA / TDMA protocol; when the node utilization rate is less than or equal to the first node utilization rate threshold, the CSMA mechanism is used, the node listens to the channel before transmission, and transmits immediately when idle; When the node utilization is greater than the first node utilization threshold, a TDMA mechanism is used to allocate a fixed transmission time slot to each node; When a second node utilization threshold is reached, the dynamic scheduling unit automatically triggers protocol switching according to the real-time node utilization; the second node utilization threshold is greater than the first node utilization threshold.

10. A method for underground space mapping and environmental monitoring, applicable to an underground space mapping and environmental monitoring system, characterized in that: Specifically include: Step 1: Multiple sensor nodes acquire multiple parameter data; Step 2: The edge computing unit receives various parameter data and predicts future tasks, node energy consumption, and network congestion risks based on historical data and real-time status. Step 3: The dynamic scheduling unit selects sensor nodes based on future tasks, node energy consumption, and network congestion risk, aggregates the collected data of the sensor nodes, and executes a low-latency routing algorithm to perform dynamic adjustments to obtain a scheduling strategy; Step 4: The simulation optimization unit verifies the performance of the scheduling strategy and feeds back the optimization parameters to the dynamic scheduling unit to adjust the scheduling strategy to obtain the optimized scheduling strategy.

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