Distributed optical fiber vibration monitoring method and system fused with unmanned aerial vehicle image recognition

Abstract

The invention provides a distributed optical fiber vibration monitoring method and system fused with unmanned aerial vehicle image recognition, and the method comprises the steps: obtaining time sequence vibration data through employing a distributed optical fiber sensor network disposed in a preset terrain; and generating threat event trigger information by using a real-time analysis module in combination with an adaptive threshold algorithm and a machine learning driven abnormal mode detection mechanism, and generating an optimal flight path plan for the threat event trigger information according to the threat event trigger information, the method comprises the following steps: collecting target image data acquired by an unmanned aerial vehicle and three-dimensional terrain data collected by an airborne laser radar, generating a real environment panoramic view, and generating an operation guide containing visual suggestions by using a 5G communication network in combination with a differential privacy protection technology; according to the technical scheme provided by the invention, the coverage range of data acquisition is expanded, the false alarm rate is reduced, the threat event triggering information can be generated in time, quick response is supported, the resource utilization rate is maximized, and the emergency response time is shortened.

Description

Technical Field

[0001] The embodiments of the present invention relate to the technical field of unmanned aerial vehicles, and in particular to a distributed optical fiber vibration monitoring method integrating unmanned aerial vehicle image recognition. Background Art

[0002] In the fields of security monitoring and emergency response, especially for real-time monitoring of large areas and complex terrains, a method that can quickly respond, accurately locate, and comprehensively analyze vibration signals is needed. Natural disasters (such as earthquakes, landslides), human activities (such as illegal intrusion, construction blasting), or other emergencies may trigger vibrations, and these vibration signals need to be captured and analyzed in a timely manner; Currently, traditional vibration monitoring systems mainly rely on ground sensor networks or fixed cameras for local area monitoring. Although these systems can capture vibration signals to a certain extent, they are overwhelmed when faced with large areas and dynamically changing environments. Unmanned aerial vehicle monitoring systems focus on image acquisition and lack the professional ability to analyze vibration signals, resulting in limitations in identifying and predicting potential threats. Existing solutions usually operate independently and fail to organically combine vibration monitoring with image recognition, thus limiting the overall system efficiency and response speed. Summary of the Invention

[0003] The embodiments of the present invention provide a distributed optical fiber vibration monitoring method and system integrating unmanned aerial vehicle image recognition to solve the problems of being overwhelmed and lacking professional analysis ability for vibration signals in the prior art when faced with large areas and dynamically changing environments.

[0004] In a first aspect, the embodiments of the present invention provide a distributed optical fiber vibration monitoring method integrating unmanned aerial vehicle image recognition, including: Using a distributed optical fiber sensor network deployed in a preset terrain to synchronously collect vibration signals caused by external events to obtain time-series vibration data; Using a real-time analysis module to combine an adaptive threshold algorithm and a machine learning-driven abnormal pattern detection mechanism to analyze the time-series vibration data, distinguish natural background noise and potential threat events, and generate threat event trigger information; According to the threat event trigger information, the cloud intelligent unmanned aerial vehicle scheduling system integrates dynamic meteorological data, geographical obstacle information, and unmanned aerial vehicle performance parameters, and generates an optimal flight path plan for the threat event trigger information through a reinforcement learning algorithm; Based on the optimal flight path plan for the threat event trigger information, collect the target image data obtained by the unmanned aerial vehicle and the three-dimensional terrain data collected by the airborne lidar, and fuse the target image data with the three-dimensional terrain data to generate a panoramic view of the real environment; Utilize the 5G communication network in combination with differential privacy protection technology to transmit the panoramic view of the real environment to the central server. Based on the spatio-temporal context understanding model, analyze the content transmitted in the panoramic view of the real environment, identify the specific entities that cause vibration signals and predict the behavior trends of the specific entities, establish deep association rules between the specific entities and historical vibration signal patterns, so as to evaluate the nature of the event and the scope of influence of the event nature, and calculate the event priority, and generate an operation guide containing visualization suggestions.

[0005] Optionally, according to the threat event trigger information, the cloud intelligent UAV scheduling system integrates dynamic meteorological data, geographical obstacle information and UAV performance parameters, and generates an optimal flight path plan for the threat event trigger information through a reinforcement learning algorithm, including: Determine the threat event location and the threat event influence scope in the threat event trigger information, and predict potential event chains based on geographical information systems and historical data analysis to obtain an initial flight path planning environment; According to the initial flight path planning environment, integrate dynamic meteorological data, geographical obstacle information, UAV performance parameters, real-time traffic flow data and temporary control area information to construct a comprehensive environment model; Based on the comprehensive environment model, through a reinforcement learning algorithm combined with a risk assessment model, evaluate different flight path selections, calculate the flight time, energy consumption and flight path risk index, and generate multiple flight path candidate plans; For the multiple flight path candidate plans, conduct simulation tests, calculate the expected effects according to preset evaluation indicators, and select the flight path with a score greater than the preset value as the optimal flight path plan through a multi-objective optimization algorithm.

[0006] Optionally, according to the initial flight path planning environment, integrate dynamic meteorological data, geographical obstacle information, UAV performance parameters, real-time traffic flow data and temporary control area information to construct a comprehensive environment model, including: Utilize the threat event location and the threat event influence scope determined in the initial flight path planning environment to integrate the dynamic meteorological data, and analyze the weather change trend of the time based on historical meteorological data to obtain environmental basic data; According to the environmental basic data, combined with geographical obstacle information, analyze the physical obstacles encountered outside the preset target of UAV flight through 3D modeling technology to generate an obstacle distribution map; Based on the obstacle distribution map and UAV performance parameters, combined with the changes in UAV performance under different meteorological conditions, comprehensively evaluate the ability of the UAV to perform tasks under various conditions, and generate a UAV performance evaluation report; Integrate real-time traffic flow data and temporary control area information, combine with the data of the air traffic management system, comprehensively analyze the airspace usage situation, generate the analysis results of airspace usage situation, and establish a dynamic update mechanism to cope with the changes in meteorological conditions, ground traffic flow, air traffic conditions, temporary control area setting and the state of the UAV itself during the flight process; Combine the environmental basic data, obstacle distribution map, UAV performance evaluation report and the analysis results of airspace usage situation, and construct a comprehensive environmental model through a multi-source data fusion algorithm.

[0007] Optionally, use the 5G communication network combined with differential privacy protection technology to transmit the panoramic view of the real environment to the central server, parse the content of the panoramic view of the real environment transmitted based on the spatio-temporal context understanding model, identify the specific entities that cause the vibration signal and predict the behavior trend of the specific entities, establish the deep association rules between the specific entities and the historical vibration signal patterns, so as to evaluate the nature of the event and the influence scope of the nature of the event, calculate the event priority, and generate an operation guide containing visualization suggestions, including: Use the 5G communication network combined with differential privacy protection technology to perform secure encrypted transmission of the panoramic view of the real environment, and use distributed edge computing nodes to assist the central server in receiving data to obtain an enhanced panoramic view of the real environment; Use the spatio-temporal context understanding model and multi-modal data analysis framework, combine with deep learning algorithms, parse the content of the enhanced panoramic view of the real environment, identify the specific entities that cause the vibration signal, analyze the spatial distribution, time series behavior pattern of the specific entities and the dynamic interaction relationship between the specific entities, obtain the specific entity information, and refine the specific entity information through semantic segmentation and target tracking technology to generate the specific entity information and the analysis results of the specific entity behavior pattern; According to the specific entity information and the analysis results of the specific entity behavior pattern, combine machine learning algorithms and Bayesian networks to predict the behavior trend of the specific entities, compare and match with the historical vibration signal pattern library, introduce a causal inference model to explore the potential causal relationship between threat events, establish the deep association rules between the specific entities and the historical vibration signal patterns, and evaluate the geographical features and socio-economic background of the threat event occurrence location to enhance the accuracy and relevance of the prediction, and obtain the predicted behavior trend and deep association rules; Evaluate the nature of the threat event and the influence scope of the threat event, calculate the event priority through a risk assessment model by comprehensively evaluating the predicted behavior trend, location, historical data and socio-economic factors, and dynamically adjust the weight parameters to meet the requirements of different scenarios, integrate the scenario simulation function, generate a threat event impact report and a scenario plan outside the preset target, and generate an operation guide containing visualization suggestions based on the threat event impact report and the scenario plan outside the preset target.

[0008] Optionally, using a spatio-temporal context understanding model and a multi-modal data analysis framework, combined with deep learning algorithms, parse the panoramic view content of the augmented reality environment, identify the specific entities that cause vibration signals, analyze the spatial distribution, time series behavior patterns of the specific entities, and the dynamic interaction relationships between the specific entities, obtain specific entity information, and refine the specific entity information through semantic segmentation and target tracking technologies, generating specific entity information and analysis results of specific entity behavior patterns, including: Using a spatio-temporal context understanding model and a multi-modal data analysis framework, combined with deep learning algorithms and an adaptive perception network, comprehensively analyze and process the images and 3D terrain data in the panoramic view of the augmented reality environment. By introducing an adaptive perception network, adjust the parsing accuracy to meet the requirements of different complexity scenarios, identify the specific entities that cause vibration signals, and extract the visual features, spatial position information, and dynamic behavior features of the specific entities, obtaining initial specific entity information and specific entity dynamic attributes; Based on the initial specific entity information and specific entity dynamic attributes, apply geographic information systems and geocoding technologies to analyze the spatial distribution of specific entities, determine the real-time positions of each specific entity in the geographic coordinate system, generate a specific entity distribution map with geographic tags, and accelerate the spatial query efficiency by introducing a geographic spatio-temporal index, obtaining an optimized specific entity distribution map; According to the optimized specific entity distribution map, adopt a time series prediction model combined with a long short-term memory network and a variational autoencoder to track and record the behavior trajectories of each specific entity over time, establish a dynamic interaction relationship model, use the dynamic interaction relationship model to describe the interactions and influences between different entities, and predict the behavior trends within a preset time, providing forward-looking analysis results to generate analysis results of specific entity behavior patterns; Based on the analysis results of specific entity behavior patterns, apply semantic segmentation and target tracking technologies, combined with the Transformer architecture, to refine the specific entity information, generating target specific entity information and analysis results of target specific entity behavior patterns.

[0009] Optionally, use a distributed fiber optic sensor network deployed in a preset terrain to synchronously collect vibration signals triggered by external events, obtaining time series vibration data, including: Use a distributed fiber optic sensor network deployed in a preset terrain to synchronously collect vibration signals triggered by external events, adopt optical time domain reflectometry technology combined with frequency division multiplexing and wavelength division multiplexing technologies to ensure the time and spatial resolution of vibration signals, and introduce edge computing nodes for real-time data preprocessing, obtaining initial time series vibration data; Through the adaptive data preprocessing module built in the distributed optical fiber sensor network, applying a deep learning-driven noise filtering algorithm, intelligently cleaning and formatting the initial time series vibration data, identifying and removing background noise, and retaining vibration signals meeting preset conditions to generate target time series vibration data; According to the target time series vibration data, applying a multi-scale adaptive filtering algorithm to separate natural background noise and potential threat event signals, capturing the characteristics of vibration signals at different time scales, enhancing the distinguishability of potential threat event signals, obtaining enhanced time series vibration data, and generating feature vectors for subsequent analysis; Using the enhanced time series vibration data and feature vectors, combining with geographic information system and global positioning system, mapping the vibration signals to specific geographical locations, through spatio-temporal correlation analysis, determining the real-time location where the vibration occurs and the distribution pattern of the vibration occurrence, and establishing a spatio-temporal database of vibration signals to generate optimal time series vibration data.

[0010] Optionally, based on the optimal flight path planning for threat event trigger information, collecting target image data obtained by the unmanned aerial vehicle and three-dimensional terrain data collected by the airborne lidar, fusing the target image data and the three-dimensional terrain data to generate a panoramic view of the real environment, including: Based on the optimal flight path planning for threat event trigger information, capturing images of the target area and collecting three-dimensional terrain data to obtain target image data and three-dimensional terrain data; Using the target image data obtained by the unmanned aerial vehicle, combining with the three-dimensional terrain data collected by the airborne lidar, and performing preliminary processing through an edge computing node to obtain optimized target image data and optimized three-dimensional terrain data; According to the optimized target image data and the optimized three-dimensional terrain data, applying a multi-modal data fusion algorithm to align and fuse the optimized target image data and the optimized three-dimensional terrain data to obtain fused data, and introducing a time synchronization mechanism to ensure the docking of the fused data in the spatio-temporal dimension to generate an intermediate view of the real environment; Integrating geographic information system and semantic segmentation technology, parsing the intermediate view of the real environment, identifying and annotating key entities in the intermediate view of the real environment to generate a panoramic view of the real environment.

[0011] In a second aspect, an embodiment of the present invention provides a distributed optical fiber vibration monitoring system integrating unmanned aerial vehicle image recognition, including: An acquisition module, configured to synchronously acquire vibration signals caused by external events by using a distributed optical fiber sensor network deployed in a preset terrain to obtain time series vibration data; A parsing module, configured to parse the time series vibration data by using a real-time analysis module in combination with an adaptive threshold algorithm and a machine learning-driven abnormal pattern detection mechanism, distinguish natural background noise and potential threat events, and generate threat event trigger information; An integration module, configured to integrate dynamic meteorological data, geographical obstacle information, and UAV performance parameters according to the threat event trigger information by a cloud intelligent UAV scheduling system, and generate an optimal flight path plan for the threat event trigger information through a reinforcement learning algorithm; A fusion module, configured to collect target image data obtained by a UAV and three-dimensional terrain data collected by an airborne lidar based on the optimal flight path plan for the threat event trigger information, fuse the target image data and the three-dimensional terrain data, and generate a panoramic view of the real environment; An identification module, configured to transmit the panoramic view of the real environment to a central server by using a 5G communication network in combination with differential privacy protection technology, analyze the content transmitted in the panoramic view of the real environment based on a spatio-temporal context understanding model, identify the specific entity that causes the vibration signal and predict the behavior trend of the specific entity, establish a deep association rule between the specific entity and the historical vibration signal pattern, evaluate the nature of the event and the influence range of the nature of the event, calculate the event priority, and generate an operation guide including visual suggestions.

[0012] In a third aspect, an embodiment of the present invention provides a computing device, including a processor and a memory, where a computer program is stored in the memory, and the processor is configured to run the computer program to execute a distributed optical fiber vibration monitoring method for fusing UAV image recognition according to any one of the first aspects.

[0013] In a fourth aspect, an embodiment of the present invention provides a computer storage medium, on which computer program instructions are stored, and when the computer program instructions are executed by a processor, a distributed optical fiber vibration monitoring method for fusing UAV image recognition according to any one of the first aspects is implemented.

[0014] In the embodiments of the present invention, a distributed fiber optic sensor network deployed in a preset terrain is used to synchronously collect vibration signals caused by external events, and time series vibration data is obtained; a real-time analysis module combines an adaptive threshold algorithm and a machine learning-driven abnormal pattern detection mechanism to analyze the time series vibration data, distinguish natural background noise and potential threat events, and generate threat event trigger information; according to the threat event trigger information, the cloud intelligent UAV scheduling system integrates dynamic meteorological data, geographical obstacle information and UAV performance parameters, and generates an optimal flight path plan for the threat event trigger information through a reinforcement learning algorithm; based on the optimal flight path plan for the threat event trigger information, the target image data obtained by the UAV and the three-dimensional terrain data collected by the airborne lidar are collected, and the target image data and the three-dimensional terrain data are fused to generate a panoramic view of the real environment; the panoramic view of the real environment is transmitted to the central server by using a 5G communication network combined with differential privacy protection technology, and the content of the panoramic view of the real environment is analyzed based on a spatio-temporal context understanding model, the specific entity that causes the vibration signal is identified and the behavior trend of the specific entity is predicted, a deep association rule between the specific entity and the historical vibration signal pattern is established to evaluate the nature of the event and the influence range of the event nature, and the event priority is calculated, and an operation guide including visual suggestions is generated; the technical solution provided by the present invention improves the coverage of data collection, enhances the accuracy of the system, reduces the false alarm rate, can generate threat event trigger information in a timely manner, supports rapid response, maximizes the resource utilization rate, shortens the emergency response time, ensures the efficiency and security of data transmission, protects sensitive information at the same time, and meets the requirements of modern monitoring systems for data privacy; Further, by determining the threat event location and influence range in the threat event trigger information, and predicting potential event chains based on geographic information system (GIS) and historical data analysis, an initial flight path planning environment is obtained. This ensures the accuracy of the basic data for path planning and provides a reliable basis for subsequent optimization; integrating dynamic meteorological data, geographical obstacle information, UAV performance parameters, real-time traffic flow data and temporary control area information, a comprehensive environment model is constructed. This model takes into account the influence of various factors, makes the path planning closer to the actual situation, and improves the safety and feasibility of UAV flight; through a reinforcement learning algorithm combined with a risk assessment model, different flight path selections are evaluated, the flight time, energy consumption and flight path risk index are calculated, and multiple flight path candidate plans are generated. This method not only considers flight efficiency but also takes into account risk control to ensure the safe completion of UAV tasks; simulation tests are carried out and the expected effects are calculated according to preset evaluation indicators, and the path with the highest score is selected as the optimal flight path plan through a multi-objective optimization algorithm. This process ensures the scientificity and rationality of path selection and improves the ability of UAVs to cope with complex environments Furthermore, by using the 5G communication network in combination with differential privacy protection technology, the panoramic view of the real environment is securely encrypted and transmitted, and distributed edge computing nodes are employed to assist the central server in receiving data. This approach not only ensures the security and low latency of data transmission but also reduces the burden on the central server and improves the overall system response speed; based on the spatio-temporal context understanding model and multi-modal data analysis framework, combined with deep learning algorithms, the content of the panoramic view of the augmented reality environment is analyzed. This analysis process can not only identify the specific entities that cause vibration signals but also analyze the spatial distribution, time series behavior patterns of these entities, and their dynamic interaction relationships, providing a detailed description of the on-site situation; according to the specific entity information and the analysis results of the behavior patterns, combined with machine learning algorithms and Bayesian networks, the behavior trends of specific entities are predicted, and a causal inference model is introduced to explore the potential causal relationships between threat events. This method enhances the accuracy and relevance of predictions, helps to take preventive measures in advance, and reduces potential risks; by comprehensively evaluating the predicted behavior trends, locations, historical data, and socio-economic factors, a risk assessment model is used to calculate the event priorities, and the weight parameters are dynamically adjusted to adapt to different scenario requirements. An integrated scenario simulation function is used to generate threat event impact reports and scenario plans outside the preset goals, providing strong support for decision-making; based on all the above analysis results, an operation guide containing visual suggestions is generated. This guide not only provides decision-making support information required for emergency response but also includes customized communication strategies and action plans for different stakeholders to ensure the effective transmission of information and rapid response.

[0015] These aspects or other aspects of the present invention will be more clearly understood in the following description of the embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or in the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0017] Figure 1 It is a flowchart of a distributed optical fiber vibration monitoring method integrating UAV image recognition provided by an embodiment of the present invention; Figure 2 It is a schematic structural diagram of a distributed optical fiber vibration monitoring system integrating UAV image recognition provided by an embodiment of the present invention; Figure 3 It is a schematic structural diagram of a computing device provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0018] In order to enable those skilled in the art to better understand the solution of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0019] In some processes described in the specification and claims of the present invention and the above-mentioned accompanying drawings, a plurality of operations appear in a specific order. However, it should be clearly understood that these operations may not be executed in the order in which they appear in this article or may be executed in parallel. The serial numbers of the operations, such as 101, 102, etc., are only used to distinguish different operations, and the serial numbers themselves do not represent any execution order. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions such as "first" and "second" in this article are used to distinguish different messages, devices, modules, etc., and do not represent a sequence, nor do they limit that "first" and "second" are of different types.

[0020] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative efforts belong to the scope of protection of the present invention.

[0021] Figure 1 A flowchart of a distributed optical fiber vibration monitoring method integrating UAV image recognition is provided for an embodiment of the present invention. As Figure 1 shown, the method includes: Step 101: Using a distributed optical fiber sensor network deployed in a preset terrain, synchronously collect vibration signals caused by external events to obtain time-series vibration data; In this step, a distributed optical fiber sensor network deployed in a preset terrain is used to synchronously collect vibration signals caused by external events to obtain time-series vibration data. The distributed optical fiber sensor network is a highly sensitive sensing system. Through optical time domain reflectometer (OTDR) technology, it can monitor minute changes along the optical fiber in real time and capture vibration signals caused by external events such as earthquakes, landslides, and explosions. Time-series vibration data refers to the change in vibration intensity recorded in chronological order for subsequent analysis and processing; The key to this step lies in achieving wide - area and high - precision vibration signal acquisition. Fiber optic sensors are pre - buried in the terrain to be monitored, forming a dense monitoring network. When external events occur, the sensors capture the vibrations caused by these events and convert these vibration signals into electrical signals, which are transmitted to the data center through a communication link. The data center performs preliminary processing on the received data to ensure the time synchronization and integrity of the data, thus generating time - series vibration data that can be used for further analysis; In a large - scale mine monitoring project, the fiber optic sensor network covers the entire mining area and its surrounding areas. Whenever there is blasting operation or geological activity, the sensors immediately capture the vibration signals and quickly transmit them to the data center through the optical fiber. The data center processes these signals in real - time, identifies abnormal vibration patterns, and triggers an alarm mechanism to notify relevant personnel to take necessary safety measures.

[0022] Step 102: Use the real - time analysis module to combine the adaptive threshold algorithm and the machine - learning - driven abnormal pattern detection mechanism to analyze the time - series vibration data, distinguish natural background noise from potential threat events, and generate threat event trigger information; In this step, the real - time analysis module refers to a system component that can instantaneously process and analyze data. It can analyze the data immediately when it arrives, without waiting for the entire data set to be collected. The adaptive threshold algorithm is a technology that dynamically adjusts the judgment criteria, automatically adjusting the detection threshold according to the environmental or data characteristics, so as to more accurately distinguish normal and abnormal situations. The machine - learning - driven abnormal pattern detection mechanism uses machine - learning models to identify atypical features or behaviors in the time - series vibration data. These models can learn to distinguish natural background noise (including vibrations caused by natural factors such as wind and rain) from potential threat events (such as illegal intrusion, geological activities, etc., which may pose risks to the safety of facilities), and generate threat event trigger information based on this. This information is used to initiate subsequent response measures or further data collection; In the embodiments of the present application, the process of the real-time analysis module parsing time series vibration data by combining the adaptive threshold algorithm and the machine learning-driven anomaly pattern detection mechanism is as follows: First, the real-time analysis module receives time series vibration data from the sensor network, and these data reflect the physical activity status in the target area. Then, the data stream is preliminarily screened by the adaptive threshold algorithm to remove most of the background noise caused by natural phenomena. Next, the pre-trained machine learning model is used to deeply analyze the preprocessed data to identify abnormal vibration characteristics that conform to known threat patterns. Once a possible threat event is detected, the system will generate threat event trigger information, which usually includes a preliminary assessment of the nature, location, and severity of the suspected event. Finally, the trigger information is sent to the UAV scheduling system or other emergency response units to provide decision support for the next step of action; For example, in the scenario of a mine monitoring project, while the UAV is performing tasks according to the optimal flight path planning, the vibration sensors deployed on the ground continuously monitor the vibration conditions around the mining area and transmit the time series vibration data to the central control system in real time. After receiving these data, the real-time analysis module immediately starts working, using the adaptive threshold algorithm to filter out the background noise caused by daily mine operations and natural factors to ensure that resources are not wasted due to false alarms. Then, the machine learning-based anomaly pattern detection mechanism intervenes. By comparing historical data with current data, an abnormal vibration pattern is successfully captured, which is similar to the previously recorded earthquake precursors or illegal excavation activities. The system quickly generates threat event trigger information, which not only details the location and possible causes of the abnormal vibration but also recommends dispatching a UAV to the incident location for further investigation. After receiving the instruction, the UAV scheduling system immediately arranges the nearby standby UAVs to fly to the specified coordinates along the optimal path, ready to capture images and collect three-dimensional terrain data, and at the same time prepare to fuse these data to generate a panoramic view of the real environment for technicians to make more informed decisions. In this process, step 102, as a key link, ensures the rapid linkage from data collection to UAV response, improving the efficiency and accuracy in dealing with potential threats.

[0023] Step 103: According to the threat event trigger information, the cloud intelligent UAV scheduling system integrates dynamic meteorological data, geographical obstacle information, and UAV performance parameters, and generates an optimal flight path plan for the threat event trigger information through a reinforcement learning algorithm; In this step, the cloud-based intelligent UAV scheduling system integrates dynamic meteorological data, geographical obstacle information, and UAV performance parameters, and generates an optimal flight path plan for threat event trigger information through a reinforcement learning algorithm. The dynamic meteorological data includes information such as wind speed, temperature, and humidity that affect flight conditions; the geographical obstacle information covers factors such as terrain and buildings that may impede UAV flight; the UAV performance parameters involve indicators such as maximum flight speed and endurance. The optimal flight path plan is the best flight route formulated after comprehensively considering the above factors, ensuring that the UAV can reach the target location efficiently and safely; Based on the threat event trigger information, the cloud-based intelligent UAV scheduling system first determines the specific location and its impact range of the event, and constructs an initial flight path planning environment. Then, the system collects the latest dynamic meteorological data, geographical obstacle information, and UAV performance parameters, and constructs a comprehensive environment model. On this basis, the system uses a reinforcement learning algorithm combined with a risk assessment model to evaluate the selection of different flight paths, calculates the flight time, energy consumption, and flight path risk index, and finally generates multiple candidate solutions, and selects the optimal flight path plan through a multi-objective optimization algorithm; In a mine monitoring project, once threat event trigger information is generated, the cloud-based intelligent UAV scheduling system immediately starts working. The system first confirms that the event occurred near an abandoned mine in the southeast corner of the mining area, and then collects the latest meteorological data (such as strong wind warnings), geographical obstacle information (such as nearby high-voltage line towers) in this area, and the specific performance parameters of the UAV used. Through the reinforcement learning algorithm, the system simulates multiple flight paths, evaluates the risks and efficiencies of each path, and finally selects the shortest path that avoids the high-voltage line tower as the optimal flight path plan to ensure that the UAV can quickly and safely reach the scene.

[0024] Step 104: Based on the optimal flight path plan for the threat event trigger information, collect the target image data obtained by the UAV and the three-dimensional terrain data collected by the airborne lidar, and fuse the target image data with the three-dimensional terrain data to generate a panoramic view of the real environment; In this step, based on the optimal flight path plan, the UAV flies along a predetermined path, obtains the target image data and the three-dimensional terrain data collected by the airborne lidar, and fuses these data to generate a panoramic view of the real environment. The target image data refers to the photos or videos taken by the UAV camera, providing visual information; the three-dimensional terrain data is obtained through lidar and contains height information for constructing an accurate geomorphic structure. The panoramic view of the real environment is a comprehensive description of the target area, integrating visual and terrain information to support subsequent analysis and decision-making; The drone flies to the designated location according to the optimal flight path planning, uses a high-resolution camera to capture images of the target area, and uses the onboard laser radar to scan the terrain and collect detailed 3D point cloud data. After the drone returns to the base, it uploads the acquired data to the central server. The multimodal data fusion algorithm on the server processes the image and terrain data to ensure the consistency of the two in time and space dimensions, and finally generates a panoramic view of the real environment, intuitively showing the full picture of the target area; In the mine monitoring project, the drone flew along the optimal path to the abandoned mine, used a high-definition camera to capture images around the mine, and created a three-dimensional terrain model of the area through lidar. After the drone returned to the base, all data was uploaded to the central server, and after multi-modal data fusion processing, a detailed panoramic view of the real environment was generated. This view not only shows the appearance of the mine, but also provides detailed information about the surrounding terrain, helping technicians assess potential risks and plan the next step.

[0025] Step 105: Utilize the 5G communication network in combination with differential privacy protection technology to transmit the panoramic view of the real environment to the central server, parse the content of the panoramic view of the real environment based on the spatiotemporal context understanding model, identify the specific entity causing the vibration signal and predict the behavior trend of the specific entity, establish a deep association rule between the specific entity and the historical vibration signal pattern, so as to evaluate the nature of the event and the scope of influence of the nature of the event, calculate the event priority, and generate an operation guide including visualization suggestions; In this step, the panoramic view of the real environment is securely transmitted to the central server using the 5G communication network combined with differential privacy protection technology. The content is parsed based on the spatiotemporal context understanding model to identify the specific entity that causes the vibration signal and predict the behavior trend of the specific entity. The deep association rules between the specific entity and the historical vibration signal pattern are established to evaluate the nature of the event and its scope of influence, calculate the event priority, and generate an operation guide containing visual suggestions. The 5G communication network provides a high-speed, low-latency data transmission channel; differential privacy protection technology ensures the privacy of data during transmission; the spatiotemporal context understanding model is an advanced data analysis tool used to parse complex relationships in image and terrain data; the operation guide is a specific response measure provided based on the analysis results; The core task of this step is to parse the content of the panoramic view of the real environment and extract valuable information from it. The 5G communication network ensures the efficiency and security of data transmission, while differential privacy protection technology safeguards the non-disclosure of sensitive information. After receiving the panoramic view, the central server conducts in-depth parsing through a spatio-temporal context understanding model, identifies specific vibration source entities, and predicts their future behavior. The system also compares the current event with historical vibration signal patterns, explores causal relationships, and evaluates the scope and severity of the event. Finally, the system calculates the event priority based on the evaluation results and generates an operation guide containing visualization suggestions to guide the emergency response team to take corresponding actions; In the mine monitoring project, the data collected by the drone is securely transmitted back to the central server through the 5G network. The server uses the spatio-temporal context understanding model to analyze the panoramic view of the real environment, identifies the specific location of the mine collapse, and predicts the possible risk of secondary collapse. The system compares this event with historical vibration data, discovers similar patterns, and indicates the possible existence of a larger area of geological instability. Based on this information, the system calculates that the event priority is at the highest level and generates a detailed operation guide, suggesting immediate evacuation of nearby personnel and setting up a warning area. The relevant departments acted quickly according to the guide, effectively avoiding potential safety accidents.

[0026] Through the above steps, the present invention realizes a complete chain from the high-precision synchronous acquisition of vibration signals to the generation of the panoramic view of the real environment, and then to the final parsing and decision-making support. Each step is closely connected, forming an efficient and intelligent monitoring system, significantly improving the response speed and accuracy to external events. Especially in the field of drones, this method not only enhances the flexibility and security of drone missions, but also provides a scientific decision-making basis, greatly improving the effectiveness and efficiency of emergency response.

[0027] To solve the problems of insufficient intelligence and flexibility in the existing technology path planning, in some embodiments, as described in step 103, according to the threat event trigger information, the cloud intelligent drone scheduling system integrates dynamic meteorological data, geographical obstacle information, and drone performance parameters, and generates an optimal flight path plan for the threat event trigger information through a reinforcement learning algorithm, specifically including: Determine the location of the threat event and the scope of influence of the threat event in the threat event trigger information, and predict potential event chains based on geographical information systems and historical data analysis to obtain an initial flight path planning environment; according to the initial flight path planning environment, integrate dynamic meteorological data, geographical obstacle information, UAV performance parameters, real-time traffic flow data, and temporary control area information to construct a comprehensive environment model; based on the comprehensive environment model, evaluate different flight path selections through a reinforcement learning algorithm combined with a risk assessment model, calculate flight time, energy consumption, and flight path risk index, and generate multiple flight path candidate plans; for the multiple flight path candidate plans, conduct simulation tests, calculate the expected effect according to the preset evaluation indicators, and select the university path with a score greater than the preset value as the optimal flight path planning through a multi-objective optimization algorithm; In this embodiment, the threat event trigger information includes the specific location and its scope of influence, which are obtained based on the analysis of the previous vibration data and are used to guide the UAV to go to the correct location for further investigation. The initial flight path planning environment refers to a preliminary flight environment model constructed by predicting potential event chains based on the location and scope of influence in the threat event trigger information, combined with geographical information systems (GIS) and historical data analysis, ensuring the accuracy of the basic data for subsequent path planning. The comprehensive environment model integrates dynamic meteorological data (such as wind speed, temperature, humidity, etc.), geographical obstacle information (such as terrain, buildings, etc.), UAV performance parameters (such as maximum flight speed, endurance time, etc.), as well as real-time traffic flow data and temporary control area information, comprehensively considering the influence of various factors, making the path planning closer to the actual situation. The multiple flight path candidate plans are a series of possible flight paths generated by evaluating different flight path selections through a reinforcement learning algorithm combined with a risk assessment model. The flight time, energy consumption, and flight path risk index are calculated for each path for subsequent optimization and selection. The multi-objective optimization algorithm is a method that can simultaneously consider multiple evaluation indicators (such as flight time, safety, and energy consumption) and select the optimal solution. By conducting simulation tests on the multiple flight path candidate plans, calculate the expected effect according to the preset evaluation indicators, and finally select the best path with a score greater than the preset value as the optimal flight path planning; In the embodiments of the present application, after receiving the threat event trigger information, the cloud intelligent UAV scheduling system first determines the specific location of the event and its impact range, and uses GIS and historical data analysis to predict potential event chains, and constructs an initial flight path planning environment. Next, the system collects the latest dynamic meteorological data, geographical obstacle information, UAV performance parameters, real-time traffic flow data, and temporary control area information to construct a comprehensive environment model. On this basis, the system uses the reinforcement learning algorithm combined with the risk assessment model to evaluate the selection of different flight paths, calculates the flight time, energy consumption, and flight path risk index, and generates multiple flight path candidate solutions. Finally, the system performs simulation tests on these candidate solutions through a multi-objective optimization algorithm, calculates the expected effect according to the preset evaluation indicators, and selects the best path as the optimal flight path planning; For example, in a mine monitoring project, when the monitoring system detects an abnormal vibration in the southeast corner of the mining area, the cloud intelligent UAV scheduling system immediately starts the path planning process. First, the system confirms that the vibration occurs near an abandoned mine and predicts the risk of possible geological instability. Subsequently, the system collects the latest meteorological data (such as strong wind warnings) in the area, geographical obstacle information (such as nearby high-voltage line towers), and the specific performance parameters of the UAV used. In addition, considering the busy air traffic flow and the temporarily established control area in this area, the system also obtains relevant real-time data.

[0028] Based on the above information, the system constructs a comprehensive environment model and simulates various possible flight paths. Through the reinforcement learning algorithm, the system evaluates the safety, efficiency, and energy consumption of each path and generates multiple flight path candidate solutions. After simulation tests, the system selects the shortest path that avoids the high-voltage line tower as the optimal flight path planning. This path not only ensures that the UAV can quickly reach the scene, but also minimizes the flight risk and ensures the successful completion of the task. The UAV quickly arrives at the scene according to the planned path, takes high-definition images and performs lidar scans, providing valuable first-hand information to help technicians take effective measures in time and avoid potential safety accidents.

[0029] To solve the problems of insufficient intelligence and flexibility in the existing technology path planning, according to the previous embodiment, based on the initial flight path planning environment, integrating dynamic meteorological data, geographical obstacle information, UAV performance parameters, real-time traffic flow data, and temporary control area information, constructing a comprehensive environment model specifically includes: Utilize the threat event locations and threat event influence ranges determined in the initialized flight path planning environment to integrate dynamic meteorological data, and based on historical meteorological data, analyze the weather change trends over time to obtain environmental basic data; according to the environmental basic data, combined with geographical obstacle information, analyze the physical obstacles encountered by the unmanned aerial vehicle (UAV) outside the preset flight target through three-dimensional modeling technology to generate an obstacle distribution map; based on the obstacle distribution map and UAV performance parameters, combined with the changes in UAV performance under different meteorological conditions, comprehensively evaluate the UAV's ability to perform tasks under various conditions to generate a UAV performance evaluation report; integrate real-time traffic flow data and temporary control area information, combined with the data of the air traffic management system, comprehensively analyze the airspace usage situation to generate an airspace usage situation analysis result, and establish a dynamic update mechanism to cope with changes in meteorological conditions, ground traffic flow, air traffic conditions, and the setting of temporary control areas and the UAV's own state during flight; combine the environmental basic data, obstacle distribution map, UAV performance evaluation report, and airspace usage situation analysis result, and through a multi-source data fusion algorithm, construct a comprehensive environment model; In this embodiment, the environmental basic data includes threat event locations and influence ranges, dynamic meteorological data and its historical change trends. These data are used to construct the basic conditions for flight path planning to ensure that the path planning takes into account the impact of weather changes on flight. The obstacle distribution map is a chart generated through three-dimensional modeling technology based on geographical obstacle information, which details the locations and forms of all physical obstacles encountered by the UAV outside the preset flight target, helping to avoid collision risks. The UAV performance evaluation report comprehensively evaluates the UAV's performance under different meteorological conditions, such as maximum flight speed, endurance time, etc., and analyzes the UAV's ability to perform tasks in combination with the obstacle distribution map to ensure the selection of the most suitable flight plan for the current conditions. The airspace usage situation analysis result integrates real-time traffic flow data, temporary control area information, and the data of the air traffic management system, comprehensively analyzes the airspace usage situation, and establishes a dynamic update mechanism to cope with various changes that may occur during flight. The multi-source data fusion algorithm is a technology that integrates data from different sources to ensure that all relevant information can be fully utilized, thereby constructing an accurate and comprehensive comprehensive environment model; In the embodiments of the present application, after receiving the initialization of the flight path planning environment, the system first integrates the dynamic meteorological data by using the determined threat event location and influence range, analyzes the weather change trend based on historical meteorological data, and obtains the environmental basic data. Next, according to these environmental basic data, combined with the geographical obstacle information, an obstacle distribution map is generated through three-dimensional modeling technology to clarify all physical obstacles that the unmanned aerial vehicle (UAV) may encounter. Subsequently, based on the obstacle distribution map and the UAV performance parameters, combined with the changes in UAV performance under different meteorological conditions, the ability of the UAV to perform tasks under various conditions is comprehensively evaluated, and a UAV performance evaluation report is generated. In addition, the system integrates the real-time traffic flow data and the temporary control area information, combines the data of the air traffic management system, comprehensively analyzes the airspace usage situation, generates the analysis result of the airspace usage situation, and establishes a dynamic update mechanism to cope with the changes in meteorological conditions, ground traffic flow, air traffic conditions, the setting of temporary control areas, and the UAV's own state during the flight. Finally, combining the environmental basic data, the obstacle distribution map, the UAV performance evaluation report, and the analysis result of the airspace usage situation, a comprehensive environment model is constructed through a multi-source data fusion algorithm; For example, in a large-scale mine monitoring project, when the monitoring system detects an abnormal vibration in the southeast corner of the mining area, the cloud-based intelligent drone scheduling system starts the path planning process. The system confirms that the vibration occurs near an abandoned mine and predicts the possible risk of geological instability. To develop the safest and most effective flight path, the system first integrates the latest dynamic meteorological data in the area and predicts the future weather change trend based on historical meteorological data to form the environmental basic data. Next, the system combines geographical obstacle information (such as nearby high-voltage pylons) and generates a detailed obstacle distribution map through 3D modeling technology to ensure that the drone can avoid all potential physical obstacles. Then, based on the obstacle distribution map and the drone performance parameters, the system evaluates the flight ability and endurance time of the drone under different meteorological conditions and generates a drone performance evaluation report, providing a scientific basis for subsequent path selection. At the same time, the system also integrates real-time traffic flow data and temporary control area information, and comprehensively analyzes the airspace usage situation in combination with the data provided by the air traffic management system. Considering possible weather changes, increased ground traffic flow, air traffic congestion, and adjustments to temporary control areas, the system establishes a dynamic update mechanism to ensure that the flight path planning can flexibly respond to various emergencies. Finally, through a multi-source data fusion algorithm, the system combines the environmental basic data, the obstacle distribution map, the drone performance evaluation report, and the airspace usage situation analysis results to construct a comprehensive environment model. This model not only covers all necessary flight conditions but also has high adaptability and flexibility, ensuring that the drone can efficiently and safely complete tasks in a complex and changing environment. The drone quickly arrives at the scene according to the optimized path, takes high-definition images and conducts lidar scans, providing valuable first-hand information to help technicians take effective measures in a timely manner and avoid potential safety accidents.

[0030] To solve the problems of insufficient data transmission security, insufficient parsing depth, and limited decision support in the prior art, and to increase real-time performance and privacy protection, as another embodiment, according to step 105, the panoramic view of the real environment is transmitted to the central server by using a 5G communication network in combination with differential privacy protection technology. Based on the spatio-temporal context understanding model, the content of the transmitted panoramic view of the real environment is parsed, the specific entity causing the vibration signal is identified, and the behavior trend of the specific entity is predicted. Deep association rules between the specific entity and the historical vibration signal pattern are established to evaluate the nature of the event and the scope of influence of the event nature, and the event priority is calculated to generate an operation guide containing visual suggestions, specifically including: Utilize the 5G communication network in combination with differential privacy protection technology to securely encrypt and transmit the panoramic view of the real environment, and use distributed edge computing nodes to assist the central server in receiving data to obtain the panoramic view of the augmented reality environment; utilize the spatio-temporal context understanding model and multi-modal data analysis framework, combined with deep learning algorithms, to analyze the content of the panoramic view of the augmented reality environment, identify the specific entities that cause vibration signals, analyze the spatial distribution, time series behavior patterns of the specific entities, and the dynamic interaction relationships between the specific entities to obtain specific entity information, and refine the specific entity information through semantic segmentation and object tracking technologies to generate specific entity information and the analysis results of specific entity behavior patterns; according to the specific entity information and the analysis results of specific entity behavior patterns, combine machine learning algorithms and Bayesian networks to predict the behavior trends of specific entities, compare and match with the historical vibration signal pattern library, and introduce a causal reasoning model to explore the potential causal relationships between threat events, establish deep association rules between specific entities and historical vibration signal patterns, and evaluate the geographical features and socio-economic backgrounds of the locations where threat events occur to enhance the accuracy and relevance of predictions, and obtain the predicted behavior trends and deep association rules; evaluate the nature of threat events and the scope of influence of threat events, calculate the event priority through a risk assessment model by comprehensively evaluating the predicted behavior trends, locations, historical data, and socio-economic factors, and dynamically adjust the weight parameters to meet the requirements in different scenarios, integrate the scenario simulation function, generate a threat event impact report and a scenario plan outside the preset goals, and generate an operation guide containing visual suggestions based on the threat event impact report and the scenario plan outside the preset goals; In this embodiment, the panoramic view of the augmented reality environment includes a dataset formed by the target image data obtained by the drone and the three-dimensional terrain data collected by the airborne lidar, which is used to provide a comprehensive and detailed description of the on-site situation after preliminary processing and fusion; the spatio-temporal context understanding model is an advanced data analysis tool that can analyze the complex relationships in image and terrain data and extract spatio-temporal features; the multi-modal data analysis framework combines various types of data (such as visual, geographical, time series, etc.) to provide a richer analysis perspective; the specific entity information refers to the objects identified from the panoramic view and their attributes, such as location, shape, behavior pattern, etc.; the analysis results of specific entity behavior patterns are the refined specific entity information through semantic segmentation and object tracking technologies, which provide the behavior patterns of these entities over time; the predicted behavior trends and deep association rules are the results obtained through machine learning algorithms and Bayesian networks, aiming to predict possible future behaviors and developments and explore the causal relationships between different events; the threat event impact report and the scenario plan outside the preset goals are documents generated based on the above analysis, providing countermeasures and suggestions; In the embodiments of the present application, after receiving the panoramic view of the real environment collected by the drone, the system first uses the 5G communication network combined with differential privacy protection technology to securely encrypt and transmit the panoramic view, and uses distributed edge computing nodes to assist the central server in receiving data to ensure the security and low latency of data transmission. Next, the system uses the spatio-temporal context understanding model and the multimodal data analysis framework, combined with deep learning algorithms, to analyze the content of the panoramic view of the augmented reality environment, identify the specific entities that cause vibration signals, and analyze the spatial distribution, time series behavior patterns of these entities, and their dynamic interaction relationships with each other to obtain specific entity information. Subsequently, the system further refines the specific entity information through semantic segmentation and object tracking technologies to generate specific entity information and the analysis results of specific entity behavior patterns. Then, based on these analysis results, the system combines machine learning algorithms and Bayesian networks to predict the behavior trends of specific entities, and by comparing and matching with the historical vibration signal pattern library, introduces a causal reasoning model to explore the potential causal relationships between threat events, and establishes deep association rules between specific entities and historical vibration signal patterns. Finally, the system evaluates the nature and scope of influence of threat events, calculates the event priority using a risk assessment model by comprehensively evaluating the predicted behavior trends, locations, historical data, and socio-economic factors, dynamically adjusts the weight parameters to meet the requirements in different scenarios, integrates the scenario simulation function, generates a threat event impact report and a scenario plan outside the preset goals, and finally generates an operation guide containing visual suggestions; For example, in a large-scale mine monitoring project, when the monitoring system detected an abnormal vibration in the southeast corner of the mining area, the drone quickly flew to the scene to collect high-resolution images and three-dimensional terrain data, forming a panoramic view of the real environment. To ensure the security and efficiency of data transmission, the system used a 5G communication network combined with differential privacy protection technology to securely encrypt and transmit the panoramic view, and adopted distributed edge computing nodes to assist the central server in receiving data, ensuring that the data was transmitted to the data center completely and accurately; after receiving the data, the data center immediately started the parsing process. Through the spatio-temporal context understanding model and multi-modal data analysis framework, combined with deep learning algorithms, the system identified the specific entity that caused the vibration - an abandoned mine, and analyzed in detail the terrain and structural characteristics around the mine. The system further refined this information through semantic segmentation and object tracking technologies, generating specific entity information and analysis results of the specific entity behavior pattern, revealing the risk of possible geological instability around the mine; next, based on these analysis results, the system combined machine learning algorithms and Bayesian networks to predict the possible future behavior trends of the mine, such as whether a secondary collapse might occur, and through comparison and matching with the historical vibration signal pattern library, found similar geological activity patterns, indicating the possible existence of a larger range of geological instability. The system introduced a causal reasoning model to explore the potential causal relationships between this event and other historical events, and established deep association rules between specific entities and historical vibration signal patterns; finally, the system evaluated the nature of the threat event and its scope of influence, calculated the event priority as the highest level through a risk assessment model by comprehensively evaluating the predicted behavior trends, locations, historical data, and socio-economic factors, and dynamically adjusted the weight parameters to adapt to the current emergency situation. The system generated a threat event impact report and a scenario plan outside the preset goals, suggesting immediate evacuation of nearby personnel and setting up a warning area, and relevant departments acted quickly according to the guidelines, effectively avoiding potential safety accidents.

[0031] To solve the problems of insufficient parsing accuracy, insufficiently fine spatial distribution analysis, and limited behavior pattern prediction ability in the prior art, according to the previous embodiment, the spatio-temporal context understanding model and multi-modal data analysis framework are used, combined with deep learning algorithms, to parse the content of the panoramic view of the augmented reality environment, identify the specific entity that causes the vibration signal, analyze the spatial distribution, time series behavior pattern of the specific entity, and the dynamic interaction relationship between the specific entities, obtain specific entity information, and refine the specific entity information through semantic segmentation and object tracking technologies, generating specific entity information and analysis results of the specific entity behavior pattern, specifically including: Using a spatio-temporal context understanding model and a multi-modal data analysis framework, combined with deep learning algorithms and an adaptive perception network, comprehensively analyze and process the images and three-dimensional terrain data in the panoramic view of the augmented reality environment. By introducing an adaptive perception network, adjust the parsing accuracy to adapt to the scene requirements of different complexities, identify the specific entities that cause vibration signals, and extract the visual features, spatial location information, and dynamic behavior features of the specific entities to obtain initial specific entity information and specific entity dynamic attributes; Based on the initial specific entity information and specific entity dynamic attributes, apply geographic information systems and geocoding technologies to analyze the spatial distribution of specific entities, determine the real-time position of each specific entity in the geographic coordinate system, generate a specific entity distribution map with geographic labels, and accelerate the spatial query efficiency by introducing a geographic spatio-temporal index to obtain an optimized specific entity distribution map; According to the optimized specific entity distribution map, use a time series prediction model combined with a long short-term memory network and a variational autoencoder to track and record the behavior trajectories of each specific entity over time, establish a dynamic interaction relationship model, use the dynamic interaction relationship model to describe the interactions and influences between different entities, and predict the behavior trends within a preset time to provide forward-looking analysis results to generate specific entity behavior pattern analysis results; Based on the specific entity behavior pattern analysis results, apply semantic segmentation and object tracking technologies, combined with the Transformer architecture, to refine the specific entity information to generate target specific entity information and target specific entity behavior pattern analysis results; In this embodiment, the spatio-temporal context understanding model is an advanced data processing tool that can analyze the complex relationships in image and terrain data, extract spatio-temporal features, and be used to understand the objects and their behaviors in the scene; the multi-modal data analysis framework combines various types of data (such as visual, geographical, time series, etc.) to provide a richer analysis perspective to enhance the understanding of complex scenes; the adaptive perception network is a neural network structure that can adjust the parsing accuracy according to the scene complexity to ensure the best parsing effect under different conditions; the initial specific entity information and specific entity dynamic attributes include the objects identified from the panoramic view and their visual features, spatial location information, and dynamic behavior features, which are the basis for further analysis; the specific entity distribution map with geographical tags refers to the distribution map formed by determining the real-time positions of each specific entity in the geographical coordinate system through geographic information system (GIS) and geocoding technology, and the geographical spatio-temporal index is a technology that accelerates the spatial query efficiency, making it possible to quickly locate and retrieve information in a specific area in a large-scale or high-density dataset; the time series prediction model combined with long short-term memory network (LSTM) and variational autoencoder (VAE) is an advanced method for tracking the behavior trajectories of each specific entity over time, which can not only describe the interactions and influences between different entities but also predict future behavior trends; the dynamic interaction relationship model describes the interactions and influences between different entities and helps to understand the development process of events; the target specific entity information and the analysis results of the target specific entity behavior pattern are the results generated after refining the specific entity information, providing more accurate target objects and their behavior patterns; In the embodiments of the present application, the system first uses a spatio-temporal context understanding model and a multi-modal data analysis framework, combines deep learning algorithms and an adaptive perception network, and comprehensively analyzes and processes the images and 3D terrain data in the panoramic view of the augmented reality environment. By introducing the adaptive perception network, the system can adjust the parsing accuracy to meet the requirements of scenarios with different complexities, identify the specific entities that cause vibration signals, and extract the visual features, spatial location information, and dynamic behavior features of these entities to obtain initial specific entity information and specific entity dynamic attributes. Based on this information, applying geographic information system (GIS) and geocoding technologies, the system analyzes the spatial distribution of specific entities, determines the real-time position of each specific entity in the geographic coordinate system, generates a specific entity distribution map with geographic tags, and accelerates the spatial query efficiency by introducing a geographic spatio-temporal index to obtain an optimized specific entity distribution map. Next, the system uses a time series prediction model combined with a long short-term memory network (LSTM) and a variational autoencoder (VAE) to track and record the behavior trajectories of each specific entity over time, establish a dynamic interaction relationship model, describe the interactions and influences between different entities, and predict the behavior trends within a preset time to provide forward-looking analysis results for generating specific entity behavior pattern analysis results. Finally, based on the specific entity behavior pattern analysis results, the system applies semantic segmentation and object tracking technologies, combined with the Transformer architecture, to refine the specific entity information and generate more accurate target specific entity information and target specific entity behavior pattern analysis results; For example, in a large-scale mine monitoring project, when the monitoring system detected an abnormal vibration in the southeast corner of the mining area, the drone quickly flew to the scene to collect high-resolution images and three-dimensional terrain data, forming a panoramic view of the augmented reality environment. To deeply analyze the content of this view, the system first used a spatio-temporal context understanding model and a multi-modal data analysis framework, combined with deep learning algorithms and an adaptive perception network, to comprehensively analyze and process the images and three-dimensional terrain data. Through the adaptive perception network, the system adjusted the analysis accuracy, successfully identified the specific entity causing the vibration - an abandoned mine, and extracted the visual features, spatial location information, and dynamic behavior characteristics of the mine, obtaining the initial specific entity information and specific entity dynamic attributes; based on this information, the system applied geographic information system (GIS) and geocoding technologies to analyze the spatial distribution of specific entities, determine the real-time positions of each specific entity in the geographic coordinate system, and generate a distribution map of specific entities with geographic tags. By introducing a geographic spatio-temporal index, the system significantly improved the spatial query efficiency, obtained an optimized distribution map of specific entities, which not only clarified the exact location of the mine but also revealed the detailed conditions of the surrounding geological structures; next, the system used a time series prediction model combined with a long short-term memory network (LSTM) and a variational autoencoder (VAE) to track and record the behavior trajectory of the abandoned mine over time, establishing a dynamic interaction relationship model. This model describes the interactions and influences between the mine and other geological features, predicts the behavior trends within a preset time, and provides forward-looking analysis results. For example, the system predicts the risk of a secondary collapse of the mine in the next few days, indicating greater geological instability; finally, based on the analysis results of the specific entity behavior patterns, the system applied semantic segmentation and object tracking technologies, combined with the Transformer architecture, to refine the specific entity information, generating more accurate target specific entity information and target specific entity behavior pattern analysis results. These results not only helped technicians accurately evaluate the status of the abandoned mine but also provided detailed decision-making support for the emergency response team, suggesting immediately evacuating nearby personnel and setting up a warning area, and relevant departments quickly took action according to the guidelines, effectively avoiding potential safety accidents.

[0032] To solve the problems of insufficient vibration signal acquisition accuracy, serious background noise interference, and low data analysis efficiency in the prior art, and to increase the real-time processing ability and spatio-temporal resolution, as another embodiment, according to what is described in step 101, a distributed fiber optic sensor network deployed in a preset terrain is used to synchronously collect vibration signals caused by external events, obtaining time series vibration data, specifically including: Using a distributed fiber optic sensor network deployed in a preset terrain, synchronously collect vibration signals caused by external events. Adopt optical time domain reflectometry technology combined with frequency division multiplexing and wavelength division multiplexing technologies to ensure the time and spatial resolution of vibration signals, and introduce edge computing nodes for real-time data preprocessing to obtain initial time series vibration data; through an adaptive data preprocessing module built into the distributed fiber optic sensor network, apply a noise filtering algorithm driven by deep learning to perform intelligent cleaning and formatting processing on the initial time series vibration data, identify and remove background noise, and retain vibration signals under preset conditions to generate target time series vibration data; according to the target time series vibration data, apply a multi-scale adaptive filtering algorithm to separate natural background noise from potential threat event signals, capture the characteristics of vibration signals at different time scales, enhance the recognition of potential threat event signals, obtain enhanced time series vibration data, and generate feature vectors for subsequent analysis; use the enhanced time series vibration data and feature vectors, combined with geographic information systems and global positioning systems, map the vibration signals to specific geographical locations, determine the real-time location where the vibration occurs and the distribution pattern of the vibration through spatio-temporal correlation analysis, and establish a spatio-temporal database of vibration signals to generate optimal time series vibration data; In this embodiment, optical time domain reflectometry technology combined with frequency division multiplexing and wavelength division multiplexing technologies ensures the time and spatial resolution of vibration signals, and edge computing nodes are introduced for real-time data preprocessing to obtain initial time series vibration data; the adaptive data preprocessing module built into the distributed fiber optic sensor network applies a noise filtering algorithm driven by deep learning to perform intelligent cleaning and formatting processing on the initial time series vibration data, identify and remove background noise, and retain vibration signals under preset conditions to generate target time series vibration data; the multi-scale adaptive filtering algorithm is used to separate natural background noise from potential threat event signals, capture the characteristics of vibration signals at different time scales, enhance the recognition of potential threat event signals, obtain enhanced time series vibration data, and generate feature vectors for subsequent analysis; geographic information systems and global positioning systems map the vibration signals to specific geographical locations, determine the real-time location where the vibration occurs and the distribution pattern of the vibration through spatio-temporal correlation analysis, and establish a spatio-temporal database of vibration signals to generate optimal time series vibration data; In the embodiments of the present application, first, the system uses a distributed fiber optic sensor network deployed in a preset terrain to synchronously collect vibration signals triggered by external events. By adopting the optical time domain reflectometer (OTDR) technology combined with frequency division multiplexing (FDM) and wavelength division multiplexing (WDM) technologies, high time and space resolutions are ensured, and edge computing nodes are introduced for real-time data preprocessing, obtaining initial time series vibration data. Next, through an adaptive data preprocessing module built into the distributed fiber optic sensor network, a deep learning-driven noise filtering algorithm is applied to intelligently clean and format the initial time series vibration data, identify and remove background noise, while retaining vibration signals that meet preset conditions, generating target time series vibration data. Then, the system applies a multi-scale adaptive filtering algorithm to separate natural background noise from potential threat event signals, capture the characteristics of vibration signals at different time scales, enhance the recognition of potential threat event signals, obtain enhanced time series vibration data, and generate feature vectors for subsequent analysis. Finally, the system uses the enhanced time series vibration data and feature vectors, combines with the geographic information system (GIS) and the global positioning system (GPS), accurately maps the vibration signals to specific geographical locations, determines the real-time location where the vibration occurs and the distribution pattern of the vibration occurrence through spatio-temporal correlation analysis, and establishes a spatio-temporal database of vibration signals, finally generating the optimal time series vibration data; For example, in a large-scale mine monitoring project, when the monitoring system detects an abnormal vibration in the southeast corner of the mining area, the distributed fiber optic sensor network quickly initiates the acquisition process. The system uses optical time domain reflectometry (OTDR) technology combined with frequency division multiplexing (FDM) and wavelength division multiplexing (WDM) technologies to ensure the time and spatial resolution of vibration signals, and introduces edge computing nodes for real-time data preprocessing to obtain the initial time series vibration data. Next, through the built-in adaptive data preprocessing module, the system applies a deep learning-driven noise filtering algorithm to intelligently clean and format the initial time series vibration data, identify and remove background noises such as wind and vehicle movement, and retain the vibration signals that may be caused by geological activities or human activities to generate the target time series vibration data. Subsequently, the system applies a multi-scale adaptive filtering algorithm to further separate the natural background noise from the potential threat event signals, capture the characteristics of the vibration signals at different time scales, enhance the recognition of the potential threat event signals, obtain the enhanced time series vibration data, and generate feature vectors for subsequent analysis. Finally, the system uses the enhanced time series vibration data and feature vectors, combined with geographic information system (GIS) and global positioning system (GPS), to accurately map the vibration signals to specific geographical locations, determine the real-time location of the vibration and the distribution pattern of the vibration occurrence through spatio-temporal correlation analysis, and establish a spatio-temporal database of vibration signals. Finally, the optimal time series vibration data is generated, which not only clarifies the exact location of the vibration but also reveals the detailed conditions of the surrounding geological structure.

[0033] To solve the problems of insufficient fusion and shallow analysis depth of existing technology image and terrain data, and to increase the real-time processing ability and multi-modal data analysis ability, as another embodiment, according to step 103, based on the optimal flight path planning for threat event trigger information, collect the target image data obtained by the drone and the three-dimensional terrain data collected by the airborne lidar, fuse the target image data with the three-dimensional terrain data, and generate a panoramic view of the real environment, specifically including: Based on the optimal flight path planning for threat event trigger information, image capture is performed on the target area, and three-dimensional terrain data is collected to obtain target image data and three-dimensional terrain data; using the target image data obtained by the unmanned aerial vehicle, combined with the three-dimensional terrain data collected by the airborne lidar, preliminary processing is carried out through the edge computing node to obtain the optimized target image data and the optimized three-dimensional terrain data; according to the optimized target image data and the optimized three-dimensional terrain data, applying a multimodal data fusion algorithm, aligning and fusing the optimized target image data and the optimized three-dimensional terrain data to obtain the fused data, and introducing a time synchronization mechanism to ensure the docking of the fused data in the spatio-temporal dimension to generate an intermediate view of the real environment; integrating the geographic information system and semantic segmentation technology, parsing the intermediate view of the real environment, identifying and annotating the key entities in the intermediate view of the real environment to generate a panoramic view of the real environment; In this embodiment, the target image data includes photos or videos taken by the unmanned aerial vehicle camera, which is used to provide visual information; the three-dimensional terrain data is the height information collected by the airborne lidar (LiDAR), which is used to construct an accurate geomorphic structure; the edge computing node is a computing unit that performs preliminary processing near the data source, reducing data transmission latency and improving processing efficiency; the optimized target image data and the optimized three-dimensional terrain data refer to the data sets with improved quality after preliminary processing, ensuring the accuracy and efficiency of subsequent fusion; the multimodal data fusion algorithm is a technology that combines multiple types of data (such as vision, geography, time series, etc.), providing a richer analysis perspective and ensuring the consistency of data from different sources in the spatio-temporal dimension; the intermediate view of the real environment is the preliminary view formed by the fused data, which contains the optimized target image data and three-dimensional terrain data but has not been finally parsed; the time synchronization mechanism ensures that the timestamps of all data are consistent, enabling the perfect docking of the fused data in the spatio-temporal dimension; the geographic information system (GIS) is a system used to capture, store, operate, analyze, manage, and display all forms of geographic data; the semantic segmentation technology is an image processing method that can identify different objects in an image and annotate their categories, thereby enhancing the understanding of the environment; the panoramic view of the real environment is a comprehensive description of the target area, integrating visual and terrain information, and supporting subsequent analysis and decision-making; In the embodiments of the present application, first, based on the optimal flight path planning for threat event trigger information, the system guides the UAV to fly to the target area for image capture and collect 3D terrain data, obtaining target image data and 3D terrain data. Next, using the target image data obtained by the UAV and combining with the 3D terrain data collected by the airborne lidar, preliminary processing is performed through the edge computing node to remove noise and optimize the image quality and terrain accuracy, obtaining the optimized target image data and the optimized 3D terrain data. Then, based on these optimized data, applying the multi-modal data fusion algorithm, the optimized target image data and the optimized 3D terrain data are aligned and fused, and at the same time, a time synchronization mechanism is introduced to ensure the consistency of the fused data in the spatio-temporal dimension to generate an intermediate view of the real environment. Finally, integrating the geographic information system (GIS) and semantic segmentation technology, the intermediate view of the real environment is analyzed to identify and label key entities, generating the final panoramic view of the real environment, providing detailed and intuitive information for subsequent analysis and decision-making; For example, in a large-scale mine monitoring project, when the monitoring system detects an abnormal vibration in the southeast corner of the mining area, the cloud-based intelligent UAV scheduling system immediately starts the path planning process and determines the optimal flight path plan. The UAV quickly flies to the vicinity of the abandoned mine according to the planned path and begins to collect high-resolution images and three-dimensional terrain data. First, the UAV captures detailed images of the target area and uses the on-board lidar to collect the three-dimensional terrain data of the area, obtaining the target image data and three-dimensional terrain data. Next, the system uses the target image data obtained by the UAV, combines it with the three-dimensional terrain data collected by the on-board lidar, and performs preliminary processing through the edge computing node to remove the background noise, optimize the image quality and terrain accuracy, and obtain the optimized target image data and optimized three-dimensional terrain data. Then, based on these optimized data, the system applies the multi-modal data fusion algorithm to align and fuse the optimized target image data and optimized three-dimensional terrain data, and at the same time introduces a time synchronization mechanism to ensure the consistency of the fused data in the spatio-temporal dimension, generating an intermediate view of the real environment, which not only ensures the quality of the data but also enhances the accuracy of subsequent analysis. Finally, the system integrates the geographic information system (GIS) and semantic segmentation technology to deeply analyze the intermediate view of the real environment, identify and label key entities, such as the specific location of the abandoned mine and the characteristics of the surrounding geological structure, generating a panoramic view of the real environment. This view not only shows the appearance of the mine but also provides detailed information about the surrounding terrain, helping technicians evaluate potential risks and plan the next steps. This intelligent data processing method not only improves the accuracy of image and terrain data fusion but also enhances the analysis depth, greatly improving the effectiveness and efficiency of emergency response. Based on these accurate data, the UAV can complete tasks more quickly and accurately, providing valuable on-site information to help technicians take effective measures in a timely manner to avoid potential safety accidents.

[0034] Figure 2 The structure schematic diagram of a distributed optical fiber vibration monitoring system integrating UAV image recognition provided by an embodiment of the present invention is as Figure 2 shown. The system includes: An acquisition module 21, configured to synchronously acquire vibration signals caused by external events by using a distributed optical fiber sensor network deployed in a preset terrain, and obtain time series vibration data; An analysis module 22, configured to analyze the time series vibration data by using a real-time analysis module in combination with an adaptive threshold algorithm and a machine learning-driven abnormal pattern detection mechanism, distinguish natural background noise and potential threat events, and generate threat event trigger information; An integration module 23, which is configured to integrate dynamic meteorological data, geographical obstacle information, and UAV performance parameters according to the threat event trigger information, and generate an optimal flight path plan for the threat event trigger information through a reinforcement learning algorithm; A fusion module 24, which is configured to collect target image data obtained by the UAV and three-dimensional terrain data collected by the airborne lidar based on the optimal flight path plan for the threat event trigger information, and fuse the target image data and the three-dimensional terrain data to generate a panoramic view of the real environment; An identification module 25, which is configured to use a 5G communication network in combination with differential privacy protection technology to transmit the panoramic view of the real environment to a central server, analyze the content of the panoramic view of the real environment based on a spatio-temporal context understanding model, identify the specific entity that causes the vibration signal and predict the behavior trend of the specific entity, establish a deep association rule between the specific entity and the historical vibration signal pattern, evaluate the nature of the event and the scope of influence of the event nature, calculate the event priority, and generate an operation guide containing visualization suggestions.

[0035] Figure 2 The described distributed optical fiber vibration monitoring system integrating UAV image recognition can execute Figure 1 The described distributed optical fiber vibration monitoring method integrating UAV image recognition in the illustrated embodiment, the implementation principle and technical effects will not be elaborated. For the distributed optical fiber vibration monitoring system integrating UAV image recognition in the above embodiment, the specific ways for each module and unit to execute operations have been described in detail in the embodiments related to the method, and will not be elaborated here.

[0036] In a possible design, Figure 2 The distributed optical fiber vibration monitoring system integrating UAV image recognition in the illustrated embodiment can be implemented as a computing device, such as Figure 3 As shown, the computing device may include a storage component 31 and a processing component 32; The storage component 31 stores one or more computer instructions, wherein the one or more computer instructions are called and executed by the processing component 32.

[0037] The processing component 32 is configured to use a distributed optical fiber sensor network deployed in a preset terrain to synchronously collect vibration signals caused by external events to obtain time-series vibration data; Use a real-time analysis module in combination with an adaptive threshold algorithm and a machine learning-driven anomaly pattern detection mechanism to analyze the time-series vibration data, distinguish natural background noise and potential threat events, and generate threat event trigger information; According to the threat event trigger information, the cloud intelligent UAV scheduling system integrates dynamic meteorological data, geographical obstacle information, and UAV performance parameters, and generates an optimal flight path plan for the threat event trigger information through a reinforcement learning algorithm; Based on the optimal flight path plan for the threat event trigger information, collect the target image data obtained by the UAV and the three-dimensional terrain data collected by the airborne lidar, and fuse the target image data and the three-dimensional terrain data to generate a panoramic view of the real environment; Use the 5G communication network combined with differential privacy protection technology to transmit the panoramic view of the real environment to the central server. Based on the spatio-temporal context understanding model, analyze the content of the panoramic view of the real environment, identify the specific entity that causes the vibration signal and predict the behavior trend of the specific entity, establish a deep association rule between the specific entity and the historical vibration signal pattern, evaluate the nature of the event and the scope of influence of the event nature, calculate the event priority, and generate an operation guide containing visualization suggestions.

[0038] Among them, the processing component 32 may include one or more processors to execute computer instructions to complete all or part of the steps in the above method. Of course, the processing component may also be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors or other electronic components for executing the above method.

[0039] The storage component 31 is configured to store various types of data to support the operation of the terminal. The storage component can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0040] Of course, the computing device may also necessarily include other components, such as input / output interfaces, display components, communication components, etc.

[0041] The input / output interface provides an interface between the processing component and the peripheral interface module, and the peripheral interface module may be an output device, an input device, etc.

[0042] The communication component is configured to facilitate communication between the computing device and other devices in a wired or wireless manner, etc.

[0043] Among them, the computing device can be a physical device or an elastic computing host provided by a cloud computing platform. In this case, the computing device can refer to a cloud server. The above-mentioned processing components, storage components, etc. can be basic server resources leased or purchased from a cloud computing platform.

[0044] An embodiment of the present invention also provides a computer storage medium storing a computer program, and when the computer program is executed by a computer, it can implement the above-mentioned Figure 1 distributed optical fiber vibration monitoring method integrating UAV image recognition shown in the embodiment.

[0045] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the above-described systems, devices, and units can refer to the corresponding processes in the foregoing method embodiments, and will not be elaborated herein.

[0046] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art can understand and implement it without creative efforts.

[0047] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on such an understanding, the essence of the above technical solution, or the part that contributes to the prior art, can be embodied in the form of a software product. The computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disc, etc., and includes several instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0048] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A distributed optical fiber vibration monitoring method integrating drone image recognition, characterized in that: include: Using a distributed fiber optic sensor network deployed in a preset terrain, the vibration signals caused by external events are synchronously collected to obtain time series vibration data; Utilizing a real-time analysis module combined with an adaptive threshold algorithm and a machine learning driven abnormal pattern detection mechanism to parse the time series vibration data, distinguish natural background noise from potential threat events, and generate threat event trigger information; According to the threat event trigger information, the cloud-based intelligent drone dispatching system integrates dynamic meteorological data, geographic obstacle information and drone performance parameters, and generates the optimal flight path planning for the threat event trigger information through a reinforcement learning algorithm; Based on the optimal flight path planning for the threat event trigger information, target image data acquired by the drone and three-dimensional terrain data collected by the airborne laser radar are collected, and the target image data and the three-dimensional terrain data are integrated to generate a panoramic view of the real environment; The 5G communication network is combined with differential privacy protection technology to transmit the panoramic view of the real environment to a central server. The content of the panoramic view of the real environment is parsed based on the spatiotemporal context understanding model, the specific entity that causes the vibration signal is identified and the behavior trend of the specific entity is predicted, and deep association rules between the specific entity and the historical vibration signal pattern are established to evaluate the nature of the event and the scope of influence of the event nature, calculate the event priority, and generate an operation guide with visual suggestions.

2. The method according to claim 1, characterized in that According to the threat event trigger information, the cloud-based intelligent drone dispatching system integrates dynamic meteorological data, geographic obstacle information and drone performance parameters, and generates the optimal flight path planning for the threat event trigger information through a reinforcement learning algorithm, including: Determining the threat event location and the threat event impact range in the threat event trigger information, and predicting the potential event chain based on the geographic information system and historical data analysis to obtain an initial flight path planning environment; According to the initialized flight path planning environment, dynamic meteorological data, geographic obstacle information, UAV performance parameters, real-time traffic flow data and temporary control area information are integrated to construct a comprehensive environment model; Based on the comprehensive environmental model, different flight path options are evaluated by combining a reinforcement learning algorithm with a risk assessment model, the flight time, energy consumption, and flight path risk index are calculated, and multiple flight path candidate solutions are generated; For the multiple flight path candidate solutions, simulation tests are used to calculate the expected effects according to preset evaluation indicators, and a university path with a score greater than a preset value is selected as the optimal flight path planning through a multi-objective optimization algorithm.

3. The method according to claim 2, characterized in that According to the initial flight path planning environment, dynamic meteorological data, geographic obstacle information, UAV performance parameters, real-time traffic flow data and temporary control area information are integrated to build a comprehensive environment model, including: Using the location of threat events and the impact range of threat events determined in the initial flight path planning environment, the dynamic meteorological data is integrated, and the weather change trend over time is analyzed based on historical meteorological data to obtain basic environmental data; Based on the basic environmental data and in combination with geographic obstacle information, the physical obstacles encountered by the UAV outside the preset flight target are analyzed through three-dimensional modeling technology to generate an obstacle distribution map; Based on the obstacle distribution map and the performance parameters of the UAV, combined with the changes in the performance of the UAV under different meteorological conditions, a comprehensive evaluation of the UAV's ability to perform tasks under various conditions is performed to generate a UAV performance evaluation report; Integrate real-time traffic flow data and temporary control area information, combine with data from the air traffic management system, conduct a comprehensive analysis of airspace usage, generate airspace usage analysis results, and establish a dynamic update mechanism to respond to changes in meteorological conditions, ground traffic flow, air traffic conditions, temporary control area settings, and drone status during flight; By combining the basic environmental data, obstacle distribution map, UAV performance evaluation report and airspace usage analysis results, a comprehensive environmental model is constructed through a multi-source data fusion algorithm.

4. The method according to claim 1, characterized in that The 5G communication network is combined with differential privacy protection technology to transmit the panoramic view of the real environment to the central server. The content of the panoramic view of the real environment is parsed based on the spatiotemporal context understanding model, the specific entity causing the vibration signal is identified and the behavior trend of the specific entity is predicted. The deep association rules between the specific entity and the historical vibration signal pattern are established to evaluate the nature of the event and the scope of influence of the event nature, calculate the event priority, and generate an operation guide containing visual suggestions, including: The panoramic view of the real environment is securely encrypted and transmitted by using a 5G communication network combined with differential privacy protection technology, and a distributed edge computing node is used to assist a central server in receiving data to obtain a panoramic view of the augmented reality environment; Utilize the spatiotemporal context understanding model and multimodal data analysis framework, combined with deep learning algorithms, to parse the panoramic view content of the augmented reality environment, identify the specific entity that causes the vibration signal, analyze the spatial distribution, time series behavior pattern, and dynamic interaction relationship between the specific entities, obtain specific entity information, and refine the specific entity information through semantic segmentation and target tracking technology to generate specific entity information and specific entity behavior pattern analysis results; According to the analysis results of specific entity information and specific entity behavior patterns, the behavior trend of specific entities is predicted by combining machine learning algorithms and Bayesian networks. By comparing and matching with the historical vibration signal pattern library, and introducing causal reasoning models to explore the potential causal relationship between threat events, deep association rules between specific entities and historical vibration signal patterns are established, and the geographical characteristics and socioeconomic background of the location where the threat event occurs are evaluated to enhance the accuracy and relevance of the prediction, and obtain the predicted behavior trend and deep association rules; Evaluate the nature of threat events and the scope of their impact; calculate event priorities using risk assessment models by comprehensively evaluating the predicted behavioral trends, locations, historical data, and socioeconomic factors; and dynamically adjust weight parameters to meet the needs of different scenarios; integrate scenario simulation functions to generate threat event impact reports and scenario plans outside of preset targets; and generate operational guidelines with visual suggestions based on the threat event impact reports and scenario plans outside of preset targets.

5. The method according to claim 4, characterized in that By using the spatiotemporal context understanding model and multimodal data analysis framework, combined with deep learning algorithms, the panoramic view content of the augmented reality environment is parsed, the specific entity that causes the vibration signal is identified, the spatial distribution, time series behavior pattern of the specific entity and the dynamic interaction relationship between the specific entities are analyzed, and the specific entity information is obtained. The specific entity information is refined through semantic segmentation and target tracking technology to generate specific entity information and specific entity behavior pattern analysis results, including: By using the spatiotemporal context understanding model and multimodal data analysis framework, combined with deep learning algorithms and adaptive perception networks, the images and three-dimensional terrain data in the panoramic view of the augmented reality environment are comprehensively analyzed and processed. By introducing an adaptive perception network, the analysis accuracy is adjusted to meet the needs of scenes of different complexities, the specific entity causing the vibration signal is identified, and the visual features, spatial location information and dynamic behavior features of the specific entity are extracted to obtain the initial specific entity information and the dynamic attributes of the specific entity; Based on the initial specific entity information and the dynamic attributes of the specific entity, the geographic information system and geocoding technology are applied to analyze the spatial distribution of the specific entity, determine the real-time position of each specific entity in the geographic coordinate system, generate a specific entity distribution map with geographic tags, and accelerate the spatial query efficiency by introducing geographic spatiotemporal indexes to obtain an optimized specific entity distribution map; According to the optimized specific entity distribution map, a time series prediction model composed of a long short-term memory network and a variational autoencoder is used to track and record the behavior trajectory of each specific entity over time, establish a dynamic interaction relationship model, use the dynamic interaction relationship model to describe the interaction and influence between different entities, and predict the behavior trend within a preset time, provide forward-looking analysis results, and generate specific entity behavior pattern analysis results; Based on the analysis results of specific entity behavior patterns, semantic segmentation and target tracking technology are applied, combined with the Transformer architecture, to refine the specific entity information and generate target specific entity information and target specific entity behavior pattern analysis results.

6. The method according to claim 1, characterized in that Using a distributed fiber optic sensor network deployed in a preset terrain, the vibration signals caused by external events are synchronously collected to obtain time series vibration data, including: The distributed fiber optic sensor network deployed in the preset terrain is used to synchronously collect vibration signals caused by external events. The optical time domain reflectometer technology is combined with frequency division multiplexing and wavelength division multiplexing technology to ensure the time and spatial resolution of the vibration signal. The edge computing node is introduced for real-time data preprocessing to obtain the initial time series vibration data. By using the built-in adaptive data preprocessing module of the distributed optical fiber sensor network and applying the deep learning driven noise filtering algorithm, the initial time series vibration data is intelligently cleaned and formatted, the background noise is identified and removed, and the vibration signal of the preset conditions is retained to generate the target time series vibration data; According to the target time series vibration data, a multi-scale adaptive filtering algorithm is applied to separate natural background noise and potential threat event signals, and the characteristics of the vibration signal are captured at different time scales to enhance the recognition of the potential threat event signal, thereby obtaining enhanced time series vibration data and generating a feature vector for subsequent analysis; By using enhanced time series vibration data and feature vectors, combined with geographic information systems and global positioning systems, the vibration signals are mapped to specific geographical locations. Through spatiotemporal correlation analysis, the real-time location of the vibration and the distribution pattern of the vibration are determined, and a spatial and temporal database of the vibration signals is established to generate the best time series vibration data.

7. The method according to claim 1, characterized in that Based on the optimal flight path planning for the threat event trigger information, the target image data acquired by the drone and the three-dimensional terrain data collected by the airborne laser radar are collected, and the target image data and the three-dimensional terrain data are integrated to generate a panoramic view of the real environment, including: Based on the optimal flight path planning for the threat event trigger information, the target area is image captured and three-dimensional terrain data is collected to obtain target image data and three-dimensional terrain data; The target image data acquired by the drone is combined with the 3D terrain data collected by the airborne lidar, and is preliminarily processed through the edge computing node to obtain optimized target image data and optimized 3D terrain data; According to the optimized target image data and the optimized three-dimensional terrain data, a multimodal data fusion algorithm is applied to align and fuse the optimized target image data and the optimized three-dimensional terrain data to obtain fused data, and a time synchronization mechanism is introduced to ensure that the fused data are connected in the time and space dimensions to generate an intermediate view of the real environment; The geographic information system and semantic segmentation technology are integrated to parse the intermediate view of the real environment, identify and annotate the key entities of the intermediate view of the real environment, and generate a panoramic view of the real environment.

8. A distributed optical fiber vibration monitoring method system integrating drone image recognition, characterized in that: include: The acquisition module is used to synchronously acquire vibration signals caused by external events using a distributed optical fiber sensor network deployed in a preset terrain to obtain time series vibration data; A parsing module, for parsing the time series vibration data using a real-time analysis module combined with an adaptive threshold algorithm and a machine learning driven abnormal pattern detection mechanism, distinguishing between natural background noise and potential threat events, and generating threat event trigger information; An integration module, which is used for integrating dynamic meteorological data, geographical obstacle information and drone performance parameters according to the threat event trigger information by the cloud-based intelligent drone dispatching system, and generating an optimal flight path planning for the threat event trigger information through a reinforcement learning algorithm; A fusion module is used to collect target image data acquired by the UAV and three-dimensional terrain data collected by the airborne laser radar based on the optimal flight path planning for the threat event trigger information, and fuse the target image data with the three-dimensional terrain data to generate a panoramic view of the real environment; The identification module is used to utilize the 5G communication network in combination with differential privacy protection technology to transmit the panoramic view of the real environment to a central server, parse the content of the panoramic view of the real environment based on the spatiotemporal context understanding model, identify the specific entity that causes the vibration signal and predict the behavior trend of the specific entity, establish deep association rules between the specific entity and the historical vibration signal pattern to evaluate the nature of the event and the scope of influence of the nature of the event, calculate the event priority, and generate an operation guide containing visualization suggestions.

9. A computing device, characterized in that It comprises a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a distributed optical fiber vibration monitoring method integrating drone image recognition as described in any one of claims 1 to 7.

10. A computer storage medium, characterized in that: A computer program is stored, and when the computer program is executed by a computer, a distributed optical fiber vibration monitoring method integrating drone image recognition as described in any one of claims 1 to 7 is implemented.

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