Mine disaster site rapid assessment and rescue command system based on unmanned aerial vehicle

Abstract

This invention relates to the field of mine emergency rescue technology, specifically providing a rapid assessment and rescue command system for mine disaster sites based on unmanned aerial vehicles (UAVs). The system includes: a UAV module, a communication analysis module, a control module, and a command and assessment module. The UAV module is used to enter the mine disaster area and collect on-site environmental data and communication link signals in real time. The communication analysis module analyzes the communication link signals to determine signal strength changes. The control module dynamically adjusts the information transmission quality based on signal strength changes to address data integrity issues in complex terrain. The command and assessment module receives the adjusted transmission data, performs a rapid assessment of the disaster site, and generates a rescue command plan. This UAV-based rapid assessment and rescue command system for mine disaster sites adjusts the information transmission quality according to changes in communication link signal strength to address data integrity issues in complex terrain.

Description

Technical Field

[0001] This application relates to the field of mine emergency rescue technology, and in particular to a rapid assessment and rescue command system for mine disaster sites based on unmanned aerial vehicles (UAVs). Background Technology

[0002] In mine disaster sites, traditional manual investigation methods suffer from slow response and high risk. In recent years, unmanned aerial vehicle (UAV)-based emergency assessment and rescue command systems have been gradually introduced into mine emergency management. These systems typically include unmanned aerial platforms equipped with multiple sensors, image and data acquisition modules, communication links, and ground command terminals, enabling rapid reconnaissance of disaster areas, data transmission, and emergency decision support.

[0003] However, the complex terrain and structure of mines, such as vertical shafts, inclined tunnels, and branch tunnels, severely impact wireless signal propagation, leading to unstable communication signals. This is especially true when the flight path traverses multiple spatial areas or encounters obstacles, causing data transmission issues such as packet loss, interruptions, or increased latency, significantly affecting the integrity and real-time performance of information collected by the UAV. To address these problems, existing technologies attempt to improve communication quality by increasing transmission power, switching communication frequency bands, and configuring relay nodes. However, these methods often lack dynamic response capabilities to environmental changes. In actual mine rescue operations, communication link signal strength fluctuates frequently, making static strategies ineffective in ensuring transmission stability. Particularly in areas with communication disruptions, data interruptions can lead to decision-making delays. Summary of the Invention

[0004] The main purpose of this application is to provide a rapid assessment and rescue command system for mine disaster sites based on unmanned aerial vehicles (UAVs), which aims to solve the technical problem of data integrity in complex terrain.

[0005] To achieve the above objectives, this application provides a rapid assessment and rescue command system for mine disaster sites based on unmanned aerial vehicles (UAVs), the system comprising: The drone module is used to enter the mine disaster area and collect on-site environmental data and communication link signals in real time; The communication analysis module is used to analyze the communication link signals to determine changes in signal strength. The control module is used to dynamically adjust the information transmission quality based on the changes in signal strength in order to solve the data integrity problem in complex terrain. The command and assessment module is used to receive the transmitted data after regulation, perform rapid assessment of the disaster site, and generate rescue command plans.

[0006] In some embodiments, the drone module includes: The system includes a multi-rotor flight platform, an environmental sensor array, and a high-definition image acquisition device. The environmental sensor array is used to detect parameters such as temperature, gas concentration, humidity, and dust density.

[0007] In some embodiments, the communication analysis module includes: The signal receiving unit and the signal trend evaluation unit are used to construct a communication stability evaluation model based on the signal strength changes over a continuous time period.

[0008] In some embodiments, the communication analysis module further includes: The link failure prediction submodule is used to identify areas where communication may be interrupted and generate alternative transmission path prompts.

[0009] In some embodiments, the control module includes: The system includes a transmission protocol adaptive unit and a coding level adjustment unit, wherein the transmission protocol adaptive unit is used to dynamically switch between multiple communication methods such as WiFi, LTE, and Mesh.

[0010] In some embodiments, the coding level adjustment unit is used to dynamically adjust the forward error correction coding bit rate according to the channel bit error rate in order to balance transmission speed and data integrity.

[0011] In some embodiments, the command assessment module includes: The system includes an image fusion analysis module and a disaster scene modeling module. The image fusion analysis module is used to stitch together and denoise multiple images collected by UAVs, and the disaster scene modeling module is used to generate a three-dimensional disaster scene map.

[0012] In some embodiments, the command assessment module further includes: The task planning module automatically generates personnel evacuation routes, material delivery points, and operation priorities based on the disaster scenario model.

[0013] In some embodiments, the UAV module is equipped with an autonomous flight control unit, which dynamically adjusts the flight altitude, path, and hovering strategy based on the communication link quality and obstacle recognition results to ensure communication continuity and image acquisition stability.

[0014] In some embodiments, a feedback control channel is established between the communication analysis module and the UAV module to automatically trigger flight path optimization or return-to-home decision when the link signal drops below a preset threshold.

[0015] This invention provides a rapid assessment and rescue command system for mine disaster sites based on unmanned aerial vehicles (UAVs). The UAV module enters the mine disaster area and collects real-time environmental data and communication link signals. A communication analysis module analyzes the communication link signals to determine signal strength changes, and a control module dynamically adjusts the information transmission quality based on these changes to address data integrity issues in complex terrain. The command and assessment module receives the adjusted transmission data, performs a rapid assessment of the disaster site, and generates a rescue command plan. This system effectively alleviates link instability in multi-channel, multi-obstructed environments in mines, reduces packet loss and retransmission delays, thereby improving the integrity and timeliness of information collected by the UAVs. It provides a reliable basis for emergency decision-making, possesses adaptive capabilities to complex terrain changes, and can automatically adjust communication strategies according to environmental changes. It is particularly suitable for continuous patrol tasks in highly uncertain areas such as disaster sites, reducing human intervention and improving the speed of rescue response and the scientific nature of decision-making. It is especially suitable for time-sensitive emergency rescue scenarios. The system has a modular structure, supports collaborative operation with multiple UAVs, and has a task allocation and status synchronization mechanism, enabling it to adapt to the complex needs of large-scale, multi-regional, and multi-target mine emergency rescue tasks.

[0016] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0018] Figure 1 This is a schematic diagram of the structure of the UAV-based rapid assessment and rescue command system for mine disaster sites provided by the present invention.

[0019] Figure label: 101. Unmanned Aerial Vehicle (UAV) Module; 102. Communication Analysis Module; 103. Control Module. Detailed Implementation

[0020] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0021] In the following description, specific details such as particular system architectures, interfaces, and technologies are presented for illustrative purposes rather than for limiting purposes, in order to provide a thorough understanding of this application.

[0022] The terms "first," "second," and "third" in this application are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Therefore, a feature defined as "first," "second," or "third" may explicitly or implicitly include at least one of the stated features. In the description of this application, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified. All directional indications (such as up, down, left, right, front, back, etc.) in the embodiments of this application are only used to explain the relative positional relationships and movement of components in a specific posture (as shown in the figures). If the specific posture changes, the directional indications also change accordingly. The terms "comprising" and "having," and any variations thereof, in the embodiments of this application are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or components inherent to these processes, methods, products, or devices.

[0023] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment that is mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0024] The present application will now be described in detail with reference to the accompanying drawings and embodiments.

[0025] Please see Figure 1 This application provides a rapid assessment and rescue command system for mine disaster sites based on unmanned aerial vehicles (UAVs). The system includes: a UAV module 101, a communication analysis module 102, a control module 103, and a command and assessment module 104; wherein... Unmanned aerial vehicle (UAV) module 101 is used to enter the mine disaster area and collect on-site environmental data and communication link signals in real time; Communication analysis module 102 is used to analyze the communication link signal to determine changes in signal strength; The control module 103 is used to dynamically adjust the information transmission quality based on the changes in signal strength in order to solve the data integrity problem in complex terrain. The command and assessment module 104 is used to receive the transmitted data after adjustment, perform rapid assessment of the disaster site, and generate a rescue command plan.

[0026] This system utilizes four collaboratively operating functional modules to achieve rapid information collection and decision support in mine disaster environments. First, the UAV module 101, acting as the front-end execution unit, is equipped with various sensors and communication devices, possessing automatic path planning and obstacle avoidance capabilities. It can traverse narrow mine tunnels and high-risk areas, collecting environmental data including temperature, humidity, toxic gas concentration, air composition, visual images, and wireless signal strength. Subsequently, the communication analysis module 102 performs frequency and time domain analysis on the collected link signals, identifying signal attenuation trends, signal blind spots, and interference sources. It quantifies the communication reachability of each area by establishing a communication quality assessment model. Based on the analysis results, the control module 103 executes multi-level control strategies, including dynamically adjusting the UAV relay point location, switching to high-reliability frequency bands, optimizing data packet splitting and redundancy mechanisms, and introducing bit error rate prediction and fault-tolerant coding strategies to improve transmission quality, ensuring the stability and integrity of data transmission even under extreme communication conditions. Finally, the command and assessment module 104, based on integrated data and images, uses embedded decision models such as rule-based disaster level assessment algorithms or deep learning networks to quickly identify the nature of the disaster area, the possible locations of trapped personnel, and feasible routes, and generates graphical command and dispatch suggestions and rescue plans. The entire process is designed with real-time performance, robustness, and adaptability as its core principles, achieving comprehensive and efficient perception and command and dispatch support for mine disaster sites.

[0027] The system provided by this invention brings significant technical improvements at multiple levels. First, the rapid deployment and high flexibility of the UAV module 101 enable it to quickly enter confined areas of the mine for multi-source data collection. Second, the collaborative work of the communication analysis and control module 103 successfully solves the communication instability problem caused by the complex structure and severe signal obstruction of the mine, ensuring the reliable transmission of critical data. Third, the intelligent analysis capabilities of the command and evaluation module 104 eliminate reliance on subjective human judgment in rescue decisions, improving response efficiency and decision accuracy. The system supports modular upgrades and compatibility with multiple communication standards, exhibiting excellent scalability and adaptability. Furthermore, its high degree of automation reduces the need for manual entry into high-risk areas, significantly improving the safety and scientific rigor of rescue operations, and possessing significant engineering value for promoting the modernization of mine emergency response systems.

[0028] The UAV module 101 includes a multi-rotor flight platform, an environmental sensor group, and a high-definition image acquisition device. The environmental sensor group is used to detect parameters such as temperature, gas concentration, humidity, and dust density.

[0029] The UAV module 101 described in this embodiment uses a multi-rotor flight platform as its basic flight unit. It possesses excellent hovering stability and vertical takeoff and landing capabilities, enabling it to move flexibly in the narrow, winding, and complex terrain of mines, and stably perform positioning and data acquisition tasks. The multi-rotor flight platform integrates an autonomous navigation module and an attitude stabilization system, allowing for precise path planning and dynamic adjustment via preset flight paths or ground commands. The onboard environmental sensor suite includes temperature sensors, gas sensors (such as CO, CH4, and H2S), humidity sensors, and a dust particle concentration detector. This allows for real-time, multi-point acquisition of key environmental parameters within the mine, generating a time-series data stream to support disaster level assessment. Simultaneously, a high-definition image acquisition device records on-site visual information through a high-resolution camera, supporting image compression transmission and target recognition processing. This is used to identify key scenarios within the mine, such as obstacles, fire sources, collapsed structures, and personnel traces. All of the above equipment is powered by the flight platform and transmits the acquired information to the ground or relay station in real time via a unified data interface, enabling information fusion and dynamic response at the front-end perception layer.

[0030] By integrating a multi-rotor flight platform and various types of sensors and image acquisition devices into the UAV module 101, this implementation greatly enhances the adaptability and mission execution capabilities of UAVs in mine disaster environments. The multi-rotor platform ensures that the equipment can achieve precise control and hovering operations in confined spaces, avoiding the difficulties encountered by traditional track-based or ground robots in entering such environments. The environmental sensor array enables continuous monitoring of key safety indicators such as temperature, toxic gases, humidity, and smoke, providing objective evidence for disaster nature determination and hazard level assessment. The high-definition image acquisition device provides abundant on-site image data, offering data support for remote assessment, human-assisted identification, and subsequent machine learning analysis. The overall design achieves collaborative perception of environmental and image data, improving the overall information acquisition efficiency and disaster identification accuracy of the system.

[0031] The communication analysis module 102 includes a signal receiving unit and a signal trend evaluation unit. The signal trend evaluation unit is used to construct a communication stability evaluation model based on the signal strength changes over a continuous time period.

[0032] In this embodiment, the communication analysis module 102 is further refined into two core components: a signal receiving unit and a signal trend evaluation unit. The signal receiving unit is responsible for continuously receiving multiple communication parameter data from the UAV module 101, including RSSI (Received Signal Strength Indicator), SNR (Signal-to-Noise Ratio), and link packet loss rate, and organizing them into a continuous signal data stream according to timestamps. Based on the received data, the signal trend evaluation unit uses a sliding window algorithm to extract the signal strength change trend over different time periods. Through time series analysis, filtering, and outlier removal techniques, it models the stability of signal fluctuations. During the modeling process, quantitative indicators such as the weighted coefficient of variation and the channel stability index are introduced to form a communication stability evaluation model, which characterizes the reliability of communication links in different areas and environments of the mine. This evaluation model can be trained to obtain representative parameter distributions for multiple communication scenarios, possessing the ability to adaptively identify the current communication state and warn of signal attenuation risks, providing accurate communication state references for the control module 103.

[0033] By constructing an independent signal reception and signal trend assessment unit within the communication analysis module 102, this implementation significantly enhances the monitoring and analysis capabilities for communication stability in the mine environment. This module can not only record and track signal quality changes in real time, but also identify potential communication failure areas in advance based on trend analysis over a time period, avoiding data loss or command delays caused by link interruptions. The introduction of the communication stability assessment model enables quantitative prediction of communication status under complex mine structures, improving the rationality of communication planning for different work areas, providing a more reliable foundation for subsequent control and command, and enhancing the overall robustness and safety of the system.

[0034] The communication analysis module 102 further includes a link interruption prediction submodule, which is used to identify areas where communication interruption may occur and generate alternative transmission path prompts.

[0035] In this embodiment, the communication analysis module 102 further integrates a link failure prediction submodule on top of the existing signal reception and trend assessment to enhance the proactive identification and response capabilities to communication interruption risks. This link failure prediction submodule works in conjunction with the signal trend assessment unit. First, based on the collected time-series signal data, it identifies precursory features such as continuous decline, drastic fluctuations, and abnormal packet loss. Then, it uses machine learning algorithms (such as decision tree classification, random forest, or Bayesian networks) to establish a link failure determination model and predict the probability of impending link interruption. Simultaneously, this module combines mine spatial topology information and historical communication heatmaps to locate high-risk areas and automatically generates a set of feasible alternative transmission path prompts when a communication degradation trend occurs. The alternative paths are calculated based on signal strength gradient distribution, obstacle avoidance rules, and communication relay node distribution, and are ultimately fed back to the control module 103 through the task planning interface to adjust the UAV deployment path or communication link to avoid interruption risks.

[0036] By introducing a link failure prediction submodule, this implementation effectively enhances the predictive capability of the communication analysis module 102 and the system's fault tolerance. Compared to traditional passive interruption detection methods, this module proactively identifies potential communication blind spots and significantly improves the accuracy and response speed of interruption identification through a quantitative prediction model. Simultaneously, automatically generated alternative transmission path prompts provide the system with optional adjustment strategies, enabling the control module 103 to complete communication reconstruction before an interruption occurs, reducing the risk of data loss and ensuring the continuity of the information chain. This capability is of great significance in mine scenarios with complex disasters and extremely unstable communication environments, helping to maintain overall system communication and improve the stability and efficiency of emergency response.

[0037] The control module 103 includes a transmission protocol adaptive unit and a coding level adjustment unit, wherein the transmission protocol adaptive unit is used to dynamically switch between multiple communication methods such as WiFi, LTE, and Mesh.

[0038] In this embodiment, the functionality of the control module 103 is further expanded. By integrating a transmission protocol adaptive unit and a coding level adjustment unit, the system's responsiveness and adaptability to changes in the communication environment are significantly enhanced. The transmission protocol adaptive unit is responsible for real-time monitoring of current link quality indicators (such as signal strength, latency, packet loss rate, and throughput), and dynamically switching between various communication standards such as WiFi, LTE, and Mesh based on preset priorities and judgment thresholds. This unit has a built-in protocol switching decision engine that can evaluate the adaptability of the current protocol and select the optimal solution based on environmental noise levels, node density, signal coverage, and bandwidth requirements. For example, when the WiFi signal is weak or severely interfered with, the system can automatically switch to LTE to obtain wider coverage or switch to Mesh to achieve multi-hop stable communication and avoid interruptions. The coding level adjustment unit dynamically adjusts the redundancy and error correction capability of the channel coding scheme based on the current bit error rate, data redundancy requirements, and transmission priorities of the communication link. For example, it switches between different levels of LDPC, RS, or Turbo coding to improve data anti-interference capability and integrity assurance level. The two units work together through a coordination mechanism to achieve the optimal balance between link stability, data integrity, and transmission efficiency.

[0039] By incorporating a transmission protocol adaptive unit and a coding level adjustment unit, this implementation significantly enhances the proactive control capability for communication link stability in complex mining environments. The system can intelligently determine and select the most suitable communication method for the current environment based on real-time link status, avoiding link paralysis or data loss due to limitations of a single protocol, and enhancing communication redundancy and diversity. Simultaneously, by dynamically adjusting the coding level, the frequency of data retransmission under high-error-rate environments is effectively reduced, ensuring the integrity and reliability of transmitted data. This mechanism not only optimizes the overall system's communication performance but also improves the fault tolerance of data transmission in resource-constrained and signal-fluctuation-prone environments, providing stable and efficient communication support for disaster assessment and rescue command.

[0040] The coding level adjustment unit is used to dynamically adjust the forward error correction coding bit rate according to the channel bit error rate in order to balance transmission speed and data integrity.

[0041] In this embodiment, the core function of the coding level adjustment unit is to dynamically and adaptively adjust the forward error correction (FEC) mechanism to optimize data transmission performance under different communication quality conditions. This unit receives link status information from the communication analysis module 102 in real time, particularly key performance parameters such as the current channel bit error rate (BER) and data loss rate. Combined with the system's preset fault tolerance threshold and transmission priority, it determines the optimal FEC coding bit rate. Specifically, in a communication environment with a low BER, the coding level adjustment unit reduces the proportion of redundant bits and adopts low-overhead coding (such as (255,239) RS coding) to increase the actual effective data rate. In environments with increased BER and interference, it automatically switches to a more redundant FEC strategy (such as (255,223) or Turbo coding) to improve error correction capabilities and ensure the integrity of critical data. This adjustment process is executed in the coding module using a sliding window mechanism to ensure smooth and seamless coding switching without affecting data packet continuity. The adjustment results will be fed back to the overall scheduling strategy of the control module 103 to synchronize the optimization and adjustment of other communication parameters, so as to achieve a dynamic balance between transmission rate and data reliability.

[0042] By dynamically adjusting the FEC coding bit rate based on the channel bit error rate, this implementation significantly improves the system's adaptive capability to data transmission quality in complex mine communication environments. This method allows for flexible redundancy allocation based on actual channel conditions, improving data throughput efficiency and reducing latency when the channel is favorable, while providing stronger error correction protection and preventing the loss of critical data when the channel deteriorates. This strategy achieves efficient utilization of system resources, enhances the robustness and anti-interference capabilities of the communication system, and is of great significance for ensuring the real-time and reliable transmission of disaster site information and decision support. Furthermore, this mechanism is applicable to different coding structures and algorithms, possessing high portability and scalability, facilitating the embedding of more intelligent control logic in subsequent system upgrades.

[0043] The command and assessment module 104 includes an image fusion analysis module and a disaster scene modeling module. The former is used to stitch together and denoise image data collected by multiple UAVs, while the latter is used to generate a three-dimensional disaster scene map.

[0044] In this embodiment, the command and assessment module 104 integrates an image fusion analysis module and a disaster scene modeling module to achieve a higher level of spatial perception and situational reconstruction of the mine disaster site. The image fusion analysis module receives image data input from multiple UAVs, which typically have different viewpoints, resolutions, and lighting conditions. This module uses image stitching algorithms based on feature point matching (such as SIFT or ORB) to perform geometric correction and spatial registration on the multi-source images. Subsequently, it combines bilateral filtering, wavelet denoising, or deep learning image enhancement algorithms to remove redundant noise and blurred areas, improve the overall image quality, and form a continuous and clear two-dimensional image stitching map. The processed image data is then transmitted to the disaster scene modeling module. This module utilizes three-dimensional modeling technologies such as multi-view stereo reconstruction (MVS), structured light reconstruction, or SLAM (simultaneous localization and mapping) to construct a realistic three-dimensional disaster scene map, combining known structural parameters of the mine with sensor positioning data. This 3D scene map not only includes structural information, obstacle distribution, and spatial passages, but also integrates real-time environmental parameters to achieve dynamic scene visualization. This helps to quickly determine passable paths, collapsed areas, and personnel distribution areas, providing intuitive and data-driven decision-making basis for subsequent command and dispatch.

[0045] By combining the image fusion analysis module and the disaster scene modeling module, this implementation significantly enhances the spatial integration and representation capabilities of disaster site information. Image fusion processing overcomes the redundancy and interference of multi-source images, improving the coherence and clarity of visual data and providing a high-quality image foundation for subsequent analysis. Three-dimensional modeling breaks through the limitations of two-dimensional data, realistically reconstructing the disaster-affected structure and dynamic changes within the mine, providing strong technical support for the spatial planning of rescue strategies. Compared to traditional methods of manual drawing or single-point image judgment, this module achieves three-dimensional reconstruction and intuitive representation of complex disaster environments, significantly improving the responsiveness and operational efficiency of the command center, and providing a compatible foundation for remote expert systems and intelligent dispatch platforms.

[0046] The command and assessment module 104 also includes a task planning module, which automatically generates personnel evacuation routes, material delivery points and operation priorities based on the disaster scenario model.

[0047] In this embodiment, the command and assessment module 104 further integrates a task planning module to achieve closed-loop linkage from environmental modeling to emergency action deployment. The task planning module takes the 3D disaster scene map generated by the disaster scene modeling module as input, and combines it with the mine structure topology map, personnel locations, obstacle distribution, and real-time environmental parameters (such as gas concentration and temperature changes) to construct a dynamic task map. This module employs heuristic path planning algorithms (such as A algorithm, D Lite, or a path search model based on graph neural networks) to automatically calculate the shortest safe evacuation route for each trapped area or task point. The path calculation process considers multiple factors such as environmental hazard level, passage width, collapse probability, and ventilation conditions. For material supply and equipment transportation, the task planning module determines the optimal material delivery point location after comprehensively evaluating terrain accessibility, UAV payload capacity, and communication coverage, ensuring timely and safe supply. Furthermore, the module analyzes the resources required for task execution, risk level, and urgency, generates a task priority ranking table based on set strategies, and dynamically adjusts task priorities based on real-time feedback information to form a continuously updated emergency dispatch plan.

[0048] By integrating a task planning module, this implementation further enhances the system's command automation and emergency response efficiency. This module achieves automated closed-loop processing from 3D scene understanding to action path generation, significantly shortening the response time from data perception to decision-making and deployment. Automated evacuation route planning reduces the risk of human error, improving the safety and timeliness of personnel evacuation; a rational layout of material delivery points improves emergency supply efficiency, avoiding resource waste or delivery failures; and a task priority mechanism ensures that limited rescue resources are allocated preferentially to high-risk areas or critical tasks, optimizing the resource scheduling and implementation effectiveness of the entire rescue mission. Furthermore, the dynamic adjustment capability of task planning enhances the system's adaptability and resilience to emergencies, providing efficient and intelligent operational organization capabilities for complex mine disaster sites.

[0049] The UAV module 101 is equipped with an autonomous flight control unit, which dynamically adjusts the flight altitude, path and hovering strategy based on the communication link quality and obstacle recognition results to ensure communication continuity and image acquisition stability.

[0050] In this embodiment, the UAV module 101 integrates an autonomous flight control unit, enabling autonomous navigation and mission execution in complex mine environments. This autonomous flight control unit integrates a multi-sensor fusion perception system and real-time control algorithms, allowing for dynamic adjustments to flight strategies based on communication link quality and obstacle recognition results. Communication link quality data is provided in real-time by the communication analysis module 102, including parameters such as signal strength, latency, and packet loss rate. Obstacle recognition relies on onboard LiDAR, ultrasonic sensors, stereo vision, or TOF cameras, using image processing and depth estimation algorithms to identify the location and volume of obstacles such as mine tunnel walls, collapsed structures, and beams. The control unit comprehensively assesses the current flight environment based on this information, dynamically planning the flight path to avoid entering communication blind spots and dangerous areas. During path execution, the control unit can adjust the flight altitude at any time to optimize signal reception and precisely control the hovering position and duration according to image acquisition requirements, ensuring stable and jitter-free image quality in critical areas. If it detects an impending entry into an unstable signal area, the system will preemptively correct the path or reduce speed to extend communication holding time, thus achieving dual protection for communication continuity and image acquisition integrity.

[0051] This implementation significantly improves environmental adaptability and autonomy when performing tasks at disaster sites by configuring an autonomous flight control unit in the UAV module 101. The system can intelligently identify and avoid obstacles without relying on continuous external control, while maintaining a stable communication link and high-quality image acquisition, thus improving operational continuity and mission success rate. The dynamic path and altitude adjustment strategy effectively avoids common problems such as signal interruption and image blurring, providing a stable foundation for subsequent data analysis and decision-making. Furthermore, this control strategy supports real-time response to environmental changes, exhibiting stronger robustness and adaptability, making it particularly suitable for special operational scenarios in mines, such as complex structures, dim lighting, and confined spaces.

[0052] A feedback control channel is established between the communication analysis module 102 and the UAV module 101 to automatically trigger flight path optimization or return-to-home decision when the link signal drops below a preset threshold.

[0053] In this embodiment, to achieve closed-loop linkage between communication status and flight control, a high real-time feedback control channel is established between the communication analysis module 102 and the UAV module 101. This channel, based on a low-latency, bidirectional data transmission mechanism, can transmit link quality parameters and flight command information in real time. The communication analysis module 102 continuously monitors key indicators such as signal strength (e.g., RSSI), signal-to-noise ratio (SNR), and link packet loss rate. When any parameter drops below a preset safety threshold, the system triggers an alarm event and sends control commands to the UAV module 101 via the control channel. Upon receiving signal degradation feedback, the UAV module 101 immediately executes path optimization or return-to-home decision logic: firstly, it prioritizes calling a path replanning algorithm (e.g., an A* variant algorithm based on the current communication hotspot distribution) to attempt to avoid signal attenuation areas and optimize the flight path; secondly, if optimization is infeasible or the safety risk is too high, it automatically switches to return-to-home mode, returning to the takeoff point or safe relay point along the optimal communication path to ensure safe data transmission and prevent equipment loss. The entire control process has a rapid response mechanism to ensure that flight behavior adjustments are completed before communication performance deteriorates sharply, reducing the risk of communication interruption to mission execution.

[0054] By establishing a feedback control channel between the communication analysis module 102 and the UAV module 101, this embodiment establishes a closed-loop coupling mechanism between communication quality and flight behavior, realizing the system's communication self-protection and intelligent recovery capabilities. When the link condition deteriorates, the system can make a judgment and take flight behavior adjustment measures immediately, effectively avoiding problems such as data loss, image acquisition interruption, or UAV loss of control caused by communication interruption, ensuring mission continuity and flight safety. This mechanism improves the system's robustness in complex mine communication environments and enhances flight reliability during emergency response, making it one of the key functions for ensuring stable system operation.

[0055] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A rapid assessment and rescue command system for mine disaster sites based on unmanned aerial vehicles (UAVs), characterized in that, The system includes: The drone module is used to enter the mine disaster area and collect on-site environmental data and communication link signals in real time; The communication analysis module is used to analyze the communication link signals to determine changes in signal strength. The control module is used to dynamically adjust the information transmission quality based on the changes in signal strength in order to solve the data integrity problem in complex terrain. The command and assessment module is used to receive the transmitted data after regulation, perform rapid assessment of the disaster site, and generate rescue command plans.

2. The rapid assessment and rescue command system for mine disaster sites based on unmanned aerial vehicles (UAVs) according to claim 1, characterized in that, The drone module includes: The system includes a multi-rotor flight platform, an environmental sensor array, and a high-definition image acquisition device. The environmental sensor array is used to detect parameters such as temperature, gas concentration, humidity, and dust density.

3. The rapid assessment and rescue command system for mine disaster sites based on unmanned aerial vehicles (UAVs) according to claim 1, characterized in that, The communication analysis module includes: The signal receiving unit and the signal trend evaluation unit are used to construct a communication stability evaluation model based on the signal strength changes over a continuous time period.

4. The rapid assessment and rescue command system for mine disaster sites based on unmanned aerial vehicles (UAVs) according to claim 3, characterized in that, The communication analysis module also includes: The link failure prediction submodule is used to identify areas where communication may be interrupted and generate alternative transmission path prompts.

5. The rapid assessment and rescue command system for mine disaster sites based on unmanned aerial vehicles (UAVs) according to claim 1, characterized in that, The control module includes: The system includes a transmission protocol adaptive unit and a coding level adjustment unit, wherein the transmission protocol adaptive unit is used to dynamically switch between multiple communication methods such as WiFi, LTE, and Mesh.

6. The rapid assessment and rescue command system for mine disaster sites based on unmanned aerial vehicles (UAVs) according to claim 5, characterized in that, The coding level adjustment unit is used to dynamically adjust the forward error correction coding bit rate according to the channel bit error rate in order to balance transmission speed and data integrity.

7. The rapid assessment and rescue command system for mine disaster sites based on unmanned aerial vehicles (UAVs) according to claim 1, characterized in that, The command assessment module includes: The system includes an image fusion analysis module and a disaster scene modeling module. The image fusion analysis module is used to stitch together and denoise multiple images collected by UAVs, and the disaster scene modeling module is used to generate a three-dimensional disaster scene map.

8. The rapid assessment and rescue command system for mine disaster sites based on unmanned aerial vehicles (UAVs) according to claim 1, characterized in that, The command assessment module also includes: The task planning module automatically generates personnel evacuation routes, material delivery points, and operation priorities based on the disaster scenario model.

9. The rapid assessment and rescue command system for mine disaster sites based on unmanned aerial vehicles (UAVs) according to claim 1, characterized in that, The UAV module is equipped with an autonomous flight control unit, which dynamically adjusts the flight altitude, path, and hovering strategy based on the communication link quality and obstacle recognition results to ensure communication continuity and image acquisition stability.

10. The rapid assessment and rescue command system for mine disaster sites based on unmanned aerial vehicles (UAVs) according to claim 1, characterized in that... The communication analysis module establishes a feedback control channel with the UAV module to automatically trigger flight path optimization or return-to-home decision when the link signal drops below a preset threshold.

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