A blasting hidden danger investigation system based on unmanned aerial vehicle tomography

Abstract

The application provides a blasting hidden danger investigation system based on unmanned aerial vehicle tomography, which comprises the following components: a data acquisition component, which performs three-dimensional modeling and tomography on a blasting target area by an unmanned aerial vehicle carrying a laser radar or a multispectral sensor, generates high-resolution point cloud data and tomographic images; a risk assessment component, which analyzes the point cloud data and tomographic images, automatically identifies blasting hidden dangers such as rock mass loosening, crack propagation and geological faults, and performs risk assessment in combination with a geomechanical model, generates a hidden danger distribution map and a risk level report; a dynamic early warning component, which performs long-term monitoring on a blasting key area, and the unmanned aerial vehicle regularly scans the blasting target area, and updates the three-dimensional model and hidden danger data in real time; when a hidden danger change is detected, an early warning mechanism is automatically triggered, and early warning information is pushed through a visual interface or a mobile terminal. The application provides an intelligent and efficient blasting safety monitoring and early warning system, and improves the safety of blasting engineering.

Description

Technical Field

[0001] The present invention relates to the field of computer vision technology, and particularly to a blasting hazard detection system based on UAV tomography. Background Art

[0002] Blasting hazard detection is a very important task, which mainly conducts a comprehensive safety inspection on the sites, equipment and operation procedures involved in blasting operations, aiming to discover and eliminate potential safety hazards and ensure the safety and controllability of blasting operations. This work is usually responsible for professional blasting engineers or safety management personnel, involving detailed inspections of various aspects such as blasting materials, blasting environment and operation specifications. Blasting operations are usually applied in fields such as mine exploitation, tunnel construction and water conservancy projects, and the geological conditions in these areas are complex and variable. The stability of rock masses is directly related to blasting safety, so it is necessary to conduct in-depth research on the structure, fracture distribution, mechanical properties, etc. of rock masses. Traditional geological exploration mainly relies on means such as manual drilling and ground penetrating radar, but these methods have problems such as low efficiency, high cost and limited coverage. In recent years, with the progress of rock mechanics theory and detection technology, high-precision geological modeling and hazard identification have become possible.

[0003] Prior Art One, Application No.: CN 202411019273.0 discloses a method for quickly detecting unexploded detonators at the post-blasting operation site, including a sticker-type wireless transmission device and a mobile detector supporting the wireless transmission device. The wireless transmission device is an RFID electronic tag; the wireless transmission device is loaded into the blasting hole together with the detonator. After blasting, if the detonator is successfully detonated, the sensor of the accompanying wireless transmission device will be damaged; if the detonator fails to blast, the sensor of the accompanying wireless transmission device will remain intact; the unexploded detonator receives the signal sent by the wireless transmission device through the mobile detector, so as to locate and find it at the blasting operation site. Although all detonator data can be completely collected, the accurate information such as the number and landing point of unexploded detonators can be safely and quickly detected, avoiding the safety hazards brought by the uncertainty of detecting unexploded detonators, and it is convenient to carry and operate, and is suitable for harsh environments such as chamber excavation; however, the installation of the wireless transmission device is relatively complex, and due to the effect of the detonator, it will cause certain interference to the operation of the wireless transmission device, and there are certain safety hazards for the detection of detonation.

[0004] Prior art two, application number: CN202310106600.5, discloses a mechanized combined drilling and blasting mining process and system for gypsum mines, including: selecting a target gypsum mining area, investigating and eliminating safety hazards at the top and bottom loose rocks of the target gypsum mining area; determining the working face of the target gypsum mining area after eliminating safety hazards, opening slotting holes on the working face, and blasting the slotting holes into slotted tunnels; obtaining a first hydraulic crushing device, positioning the crushing head of the first hydraulic crushing device perpendicular to the gypsum surface near the slotted tunnel, opening the crushing device until the gypsum is crushed, stopping the crushing operation, and obtaining collapsed gypsum rock; screening gypsum rock with a particle size larger than a preset threshold, and using the first hydraulic crushing device to perform secondary crushing on the screened rock until the particle size of the secondary crushed gypsum rock is smaller than the preset threshold. Although this reduces the use of civilian explosives, significantly weakens the harmful effects of various blasting methods, greatly improves safety, and significantly increases production efficiency, it still relies too heavily on the experience of professional personnel, which not only fails to improve the safety of blasting but also cannot effectively improve mining efficiency.

[0005] Existing technology three, application number: CN201810059286.9, discloses a method for dispatching and commanding blasting safety alerts using unmanned aerial vehicles (UAVs). This method utilizes the remote control and aerial hovering camera / photography functions of UAVs to collect real-time global or local image data of the blasting area and its surrounding environment. It enables real-time analysis of pre-blasting alert effects, monitoring of the blasting area's alert status, recording of the blasting process, inspection of post-blasting environmental impacts, identification and detection of misfires, and remote-controlled collection of misfire information, thereby achieving dispatching and commanding blasting safety alerts. While using UAV technology for dispatching and commanding blasting safety alerts improves the timeliness, safety, and reliability of blasting safety alerts and hazard identification and handling in blasting alerts, blasting area inspections, and misfire detection, ultimately achieving safe and efficient detonation and eliminating post-blast hazards, it focuses on many aspects and does not prioritize post-blast investigation, resulting in a need for further improvement in the accuracy of hazard identification.

[0006] Current technologies 1, 2, and 3 suffer from limited technical means for identifying blasting hazards and lack intelligent methods, resulting in poor accuracy and insufficient safety hazard reduction. Therefore, this invention provides a blasting hazard identification system based on unmanned aerial vehicle (UAV) tomographic scanning. Summary of the Invention

[0007] To address the aforementioned technical problems, this invention provides a blasting hazard investigation system based on unmanned aerial vehicle (UAV) tomography, comprising:

[0008] The data acquisition component is responsible for performing 3D modeling and tomographic scanning of the blasting target area, generating high-resolution point cloud data and tomographic images;

[0009] The risk assessment component is responsible for analyzing point cloud data and fault images to automatically identify potential hazards such as rock mass loosening, crack propagation, and geological fault blasting.

[0010] The dynamic early warning component is responsible for long-term monitoring of key blasting areas. UAVs regularly scan the blasting target area and update the 3D model and hazard data in real time.

[0011] Optional, data acquisition components, including:

[0012] The signal transmission module is responsible for transmitting signals, calculating the round-trip time difference of the signal, and capturing subtle undulations and structural features of the target area.

[0013] The dynamic scanning module is responsible for enabling the UAV to scan at a fixed altitude and along a fixed path according to a dynamic scanning strategy.

[0014] The image generation module is responsible for generating high-resolution point cloud data. The point cloud data is processed and tomographic images are generated through tomographic scanning.

[0015] Optional, dynamic scanning module, including:

[0016] The basic parameter calculation submodule is responsible for calculating the distance from the UAV to the explosion target area and the ambient temperature parameters.

[0017] The flight altitude adjustment submodule is responsible for calculating the impact of angle adjustment based on the signal reflection intensity and average value, and adjusting the altitude in combination with the effect of temperature on the speed of sound.

[0018] The integrated adjustment submodule is responsible for integrating the calculation results, as well as the effects of the square root of the signal strength ratio and the sine function, to derive the new flight altitude of the UAV after adjustment.

[0019] Optionally, derive the expression for the new flight altitude of the drone after adjustment:

[0020] ;

[0021] In the formula, This is the new flight altitude of the drone after adjustments; This is the drone's current flight altitude; It is the current signal reflection intensity; It is the average value of the signal reflection intensity over a period of time, used to eliminate environmental noise; It is the distance from the drone to the target area, which can be calculated using the round-trip time difference t of the signal and the speed of light c. It is the scanning angle of the drone; It is the arctangent function, used to adjust the height based on the signal reflection intensity and distance; It is a cosine function, used to take into account the effect of angle adjustment; It is the Boltzmann constant, used to calculate the effect of temperature on the speed of sound; It is the ambient temperature; It is a logarithmic function used to calculate the logarithmic value of the signal strength ratio; It is the base of the natural logarithm; It is the square root function, used to calculate the square root value of the signal strength ratio; It is a sine function used to take into account the effects of angle adjustment.

[0022] Optional, an image generation module, including:

[0023] The weighted average calculation submodule is responsible for performing weighted average calculations on the signal parameters to generate a preliminary reflection intensity value.

[0024] The standard deviation calculation submodule is responsible for calculating the standard deviation of the reflection intensity, which reflects the fluctuation and local changes of the signal;

[0025] The result aggregation calculation submodule is responsible for multiplying the weighted average result by the standard deviation to generate the final tomographic image.

[0026] Optionally, the expression for the tomographic image of the image generation module is:

[0027] ;

[0028] In the formula, This represents the weighting coefficient, used to adjust the degree of influence of different parameters on the final result. The larger the weight, the more significant the influence of the corresponding parameter on the result; Indicates the reflected intensity of the signal, used to indicate the location. The intensity of the reflected signal received at the location; The time difference is used to represent the time delay from signal transmission to reception, and is usually related to the length of the signal propagation path. This represents a time parameter, used to describe the time characteristics of a signal, which may be related to the signal's propagation speed or attenuation characteristics; Represents rock mechanical property parameters, indicating the location. The mechanical properties of the rock are used to correct signal attenuation; The parameters of the layering structure are indicated by their location. The geological stratification structure at the location is used to adjust the signal reflection characteristics; This represents the signal's timing parameters, which may be related to the signal's propagation time or temporal resolution. These parameters represent the bedding structure and are related to the angle or direction of geological bedding. These parameters represent the mechanical properties of rocks and are related to their physical properties. It represents the ratio of reflection intensity to time difference, used to express the reflection intensity per unit time. This represents the initial reflection intensity value generated by the weighted average. This represents the final generated tomographic image, used to indicate the location. The reflection intensity value at that location.

[0029] Optional risk assessment components include:

[0030] The feature extraction module is responsible for aligning and integrating the data from lidar and multispectral sensors in the spatiotemporal dimension, decomposing the raw data into feature components of different scales to obtain geological features;

[0031] The hazard identification module is responsible for classifying hazards into three categories: rock loosening, crack propagation, and geological faults. It displays different types of hazards in a three-dimensional space to form a hazard distribution map.

[0032] The report generation module is responsible for assessing the risk level of each hazard area based on the hazard distribution map, and the generated risk level report is presented visually.

[0033] Optionally, the crack propagation detection submodule of the hazard detection module includes:

[0034] The quantum state feature detection unit is responsible for establishing a quantum sensor network in direct contact with the crack edge, using quantum effects to detect the crack edge and sense changes in the microscopic quantum state; analyzing the quantum state features of the crack edge, converting the quantum state features into identifiable information, and encoding and decoding the quantum state;

[0035] The quantum state feature mapping unit is responsible for converting the non-classical properties of quantum states into image data and mapping the analyzed feature information onto the three-dimensional image of the crack. It uses the feature information of quantum states to dynamically track the crack edge and predicts the crack's propagation trend and speed by monitoring the changes in the quantum state at the crack edge in real time, thus achieving real-time monitoring of the crack's dynamic behavior.

[0036] The quantum image segmentation unit is responsible for segmenting the crack region and constructing the segmented crack region into an independent layer.

[0037] Optionally, the quantum state feature detection unit encodes and decodes the quantum state to obtain microscopic physical phenomena including changes in reflectivity and phase shifts.

[0038] Optionally, the quantum image segmentation units are constructed as independent layers containing all quantum state feature information of the crack.

[0039] The data acquisition component of this invention, using a drone equipped with a lidar or multispectral sensor, can quickly and efficiently perform 3D modeling and fault scanning of the blasting target area; it generates high-resolution point cloud data and fault images, providing accurate basic data for blasting hazard investigation and analysis; the application of drone technology makes data acquisition more flexible, covering complex terrain and dangerous areas, and reducing manual intervention. The risk assessment component intelligently analyzes the point cloud data and fault images, automatically identifying hazards such as rock loosening, crack propagation, and geological faults; combined with a geomechanical model, it performs quantitative risk assessment of hazards, generating hazard distribution maps and risk level reports; utilizing artificial intelligence improves the accuracy and efficiency of hazard identification. The dynamic early warning component performs long-term monitoring of key blasting areas; the drone periodically scans and updates the 3D model and hazard data in real time; when changes in hazards are detected (such as crack propagation, rock displacement, etc.), an early warning mechanism is automatically triggered; early warning information is pushed through a visual interface or mobile device, achieving real-time monitoring and rapid response.

[0040] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description and the accompanying drawings.

[0041] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0042] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0043] Figure 1 This is a block diagram of the blasting hazard investigation system based on UAV tomography in Embodiment 1 of the present invention;

[0044] Figure 2 This is a block diagram of the data acquisition component in Embodiment 2 of the present invention;

[0045] Figure 3 This is a block diagram of the dynamic scanning module in Embodiment 3 of the present invention;

[0046] Figure 4 This is a block diagram of the image generation module in Embodiment 4 of the present invention;

[0047] Figure 5 This is a block diagram of the risk assessment component in Embodiment 5 of the present invention;

[0048] Figure 6 This is a block diagram of the hazard identification module in Embodiment 6 of the present invention;

[0049] Figure 7 This is a block diagram of the rock mass loosening identification submodule in Embodiment 7 of the present invention;

[0050] Figure 8 This is a block diagram of the crack propagation identification submodule in Embodiment 8 of the present invention. Detailed Implementation

[0051] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0052] The terminology used in the embodiments of this application is for the purpose of describing particular embodiments only and is not intended to limit the embodiments of this application. The singular forms “a,” “the,” and “the” used in the embodiments of this application are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any or all possible combinations of one or more associated listed items.

[0053] In the following description, when referring to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application. In the description of this application, it should be understood that the terms "first," "second," "third," etc., are used only to distinguish similar objects and are not necessarily used to describe a specific order or sequence, nor should they be construed as indicating or implying relative importance. Those skilled in the art can understand the specific meaning of the above terms in this application according to the specific circumstances.

[0054] Example 1: As Figure 1 As shown, this embodiment of the invention provides a blasting hazard investigation system based on UAV tomographic scanning, comprising:

[0055] The data acquisition component is responsible for performing 3D modeling and tomographic scanning of the blasting target area using a drone equipped with lidar or multispectral sensors, generating high-resolution point cloud data and tomographic images.

[0056] The risk assessment component is responsible for analyzing point cloud data and fault images, automatically identifying blasting hazards such as rock mass loosening, crack propagation, and geological faults, and conducting risk assessments in conjunction with geomechanical models to generate hazard distribution maps and risk level reports.

[0057] The dynamic early warning component is responsible for long-term monitoring of key blasting areas. UAVs regularly scan the blasting target area and update the 3D model and hazard data in real time. When changes in hazards are detected (such as crack expansion, rock displacement, etc.), the early warning mechanism is automatically triggered and the early warning information is pushed through a visual interface or mobile device.

[0058] The working principle and beneficial effects of the above technical solution are as follows: In this embodiment, the data acquisition component uses a drone equipped with a lidar or multispectral sensor to perform 3D modeling and fault scanning of the blasting target area, generating high-resolution point cloud data and fault images. The risk assessment component analyzes the point cloud data and fault images, automatically identifying blasting hazards such as rock loosening, crack propagation, and geological faults, and performs risk assessment in conjunction with a geomechanical model, generating a hazard distribution map and risk level report. The dynamic early warning component conducts long-term monitoring of key blasting areas, with the drone periodically scanning the blasting target area and updating the 3D model and hazard data in real time. When changes in hazards are detected (such as crack propagation or rock displacement), an early warning mechanism is automatically triggered, and early warning information is pushed through a visual interface or mobile terminal. The data acquisition component of the above solution, using a drone equipped with a lidar or multispectral sensor, can quickly and efficiently perform 3D modeling and fault scanning of the blasting target area; generate high-resolution point cloud data and fault images, providing accurate basic data for blasting hazard investigation and analysis; the application of drone technology makes data acquisition more flexible, capable of covering complex terrain and dangerous areas, and reducing manual intervention. Significance: It provides high-precision 3D models and geological fault information of the blasting area, laying a data foundation for risk assessment and dynamic monitoring; it improves the efficiency and safety of data acquisition and reduces the potential risks of manual operation; it provides a scientific basis for the design, construction, and safety management of blasting projects. The risk assessment component intelligently analyzes point cloud data and fault images, automatically identifying potential hazards such as rock loosening, crack propagation, and geological faults; combined with geomechanical models, it quantitatively assesses the risks of hazards, generating hazard distribution maps and risk level reports; it utilizes artificial intelligence to improve the accuracy and efficiency of hazard identification. Significance: It can detect potential risks in the blasting area in advance, providing a scientific basis for engineering decisions; through hazard distribution maps and risk level reports, it helps managers intuitively understand the risk distribution and severity, and formulate targeted countermeasures; it reduces the accident risk of blasting projects and ensures the safety of personnel and equipment. The dynamic early warning component conducts long-term monitoring of key blasting areas, with drones periodically scanning and updating the 3D model and hazard data in real time; when changes in hazards are detected (such as crack propagation, rock displacement, etc.), an early warning mechanism is automatically triggered; early warning information is pushed through a visual interface or mobile terminal, achieving real-time monitoring and rapid response. Significance: It enables dynamic monitoring and real-time early warning of blasting areas, allowing for timely detection of potential hazards and the implementation of countermeasures; it improves the safety management level of blasting projects and reduces the probability of accidents; through a visual interface and mobile push notifications, it facilitates managers to monitor the safety status of blasting areas anytime, anywhere, thereby improving management efficiency.

[0059] In summary, the three components of this embodiment together constitute an intelligent and efficient blasting safety monitoring and early warning system. The data acquisition component provides the system with high-precision basic data, the risk assessment component identifies potential hazards and quantifies risks through intelligent analysis, and the dynamic early warning component enables real-time monitoring and rapid response. This not only improves the safety of blasting projects but also provides a scientific basis and technical support for project management, possessing significant engineering practical significance and social value.

[0060] Example 2: As Figure 2 As shown, based on Embodiment 1, the data acquisition component provided in this embodiment of the invention includes:

[0061] The signal transmission module is responsible for transmitting signals at high frequency from the lidar or multispectral sensor. After the signal comes into contact with the ground surface or rock mass in the explosion target area, it is reflected back to the sensor. The time difference of the round trip of the signal is calculated to capture the subtle undulations and structural features of the target area.

[0062] The dynamic scanning module is responsible for scanning the UAV at a fixed altitude and along a fixed path according to the dynamic scanning strategy, and automatically adjusting the flight altitude and scanning angle based on real-time feedback signals.

[0063] The image generation module is responsible for optimizing point cloud data using algorithms, taking into account signal reflection intensity and time difference, and also combining geological bedding structures and rock mechanical properties. Through the fusion of multi-dimensional data, high-resolution point cloud data is generated. The point cloud data is then processed and fault images are generated through fault scanning.

[0064] The working principle and beneficial effects of the above technical solution are as follows: In this embodiment, the signal transmission module, using a lidar or multispectral sensor, transmits signals at a high frequency. After the signal contacts the surface or rock mass of the explosion target area, it is reflected back to the sensor. The round-trip time difference of the signal is calculated to capture the subtle undulations and structural features of the target area. The dynamic scanning module, according to a dynamic scanning strategy, allows the UAV to scan at a fixed altitude and path, while automatically adjusting its flight altitude and scanning angle based on real-time feedback signals. The image generation module uses algorithms to optimize point cloud data, considering the signal reflection intensity and time difference, and also combining geological stratification and rock mechanics properties. Through the fusion of multi-dimensional data, high-resolution point cloud data is generated. The point cloud data is processed and fault images are generated through fault scanning. The signal transmission module of the above solution transmits laser or multispectral signals at a high frequency; captures the subtle undulations and structural features of the target area with high precision; and measures the round-trip time difference of the reflected signal. The signal transmission module improves the resolution and precision of data acquisition, enabling the acquisition of microscopic structural information of the surface or rock mass; through time difference measurement, the distance between the signal and the target area can be calculated, achieving precise positioning in three-dimensional space; and providing high-quality data as a foundation for data processing and analysis. The dynamic scanning module dynamically adjusts the UAV's flight altitude and scanning angle according to a strategy; it provides real-time feedback signals and automatically optimizes the scanning path; and it controls the UAV to perform continuous scanning at a fixed altitude and path. By dynamically adjusting flight parameters, the dynamic scanning module improves data acquisition efficiency and coverage, reducing omissions and duplicate scans; real-time feedback signals make scanning more flexible, automatically optimizing the path based on terrain changes and improving the uniformity of data acquisition; continuous scanning obtains continuous data sequences, facilitating post-processing and analysis. The image generation module uses algorithms to optimize point cloud data; it fuses multi-dimensional data to generate high-resolution point clouds; and, combined with geological theory, it generates fault images through fault scanning. The image generation module improves the accuracy and reliability of point cloud data through optimized algorithms; multi-dimensional data fusion provides a more comprehensive reflection of the target area's characteristics; and fault images visually display geological structures, facilitating analysis and interpretation.

[0065] In summary, the three modules in this embodiment work together to make the data acquisition process more efficient, precise, and comprehensive. High-quality data provides a solid foundation for geological analysis and applications.

[0066] Example 3: As Figure 3 As shown, based on Embodiment 2, the dynamic scanning module provided in this embodiment of the invention includes:

[0067] The basic parameter calculation submodule is responsible for calculating parameters such as the distance between the UAV and the explosion target area and the ambient temperature.

[0068] The flight altitude adjustment submodule is responsible for adjusting the UAV's flight altitude using calculated basic parameters and signal reflection intensity; it calculates the impact of angle adjustment based on signal reflection intensity and average value, and adjusts the altitude in conjunction with the effect of temperature on the speed of sound.

[0069] The integrated adjustment submodule is responsible for integrating the calculation results, as well as the effects of the square root of the signal strength ratio and the sine function, to derive the new flight altitude of the UAV after adjustment.

[0070] in,

[0071] ;

[0072] In the formula, This is the new flight altitude of the drone after adjustments; This is the drone's current flight altitude; It is the current signal reflection intensity; It is the average value of the signal reflection intensity over a period of time, used to eliminate environmental noise; It is the distance from the drone to the target area, which can be calculated using the round-trip time difference t of the signal and the speed of light c. It is the scanning angle of the drone; It is the arctangent function, used to adjust the height based on the signal reflection intensity and distance; It is a cosine function, used to take into account the effect of angle adjustment; It is the Boltzmann constant, used to calculate the effect of temperature on the speed of sound; It is the ambient temperature; It is a logarithmic function used to calculate the logarithmic value of the signal strength ratio; It is the base of the natural logarithm; It is the square root function, used to calculate the square root value of the signal strength ratio; It is a sine function used to take into account the effects of angle adjustment.

[0073] The working principle and beneficial effects of the above technical solution are as follows: The basic parameter calculation submodule of this embodiment calculates parameters such as the distance from the UAV to the explosion target area and the ambient temperature; the flight altitude adjustment submodule adjusts the UAV's flight altitude using the calculated basic parameters and signal reflection intensity; based on the signal reflection intensity and average value, the influence of angle adjustment is calculated, and the altitude is adjusted in conjunction with the influence of temperature on the speed of sound; the comprehensive adjustment submodule combines the calculation results, as well as the influence of the square root value of the signal strength ratio and the sine function, to obtain the new flight altitude of the UAV after adjustment. The basic parameter calculation submodule of the above solution provides necessary data support for the flight adjustment of the UAV by calculating basic parameters such as the distance from the UAV to the target area and the ambient temperature. Significance: Accurate environmental parameters are key to ensuring the safe and effective execution of tasks by the UAV; distance calculation helps the UAV plan its flight path and avoid obstacles; and temperature parameters are related to the calculation of the speed of sound, thus affecting signal propagation and the UAV's reaction speed. The flight altitude adjustment submodule adjusts the UAV's flight altitude using the calculated basic parameters and signal reflection intensity, calculates the difference between the signal reflection intensity and the average value, thereby calculating the influence of angle adjustment on flight altitude, and further adjusts the flight altitude in conjunction with the influence of temperature on the speed of sound. Significance: Precise adjustment of flight altitude not only improves the safety of UAV flight but also enhances mission efficiency and accuracy. Analysis of signal reflection intensity helps UAVs identify and avoid obstacles, while considering the effect of temperature on the speed of sound ensures accurate mission execution under various environmental conditions. The integrated adjustment submodule, combining calculation results and the influence of the square root of the signal strength ratio and the sine function, yields the new adjusted flight altitude for the UAV. Significance: By comprehensively considering multiple factors (such as signal strength and temperature), UAV flight adjustments become more refined and intelligent. The introduction of the square root of the signal strength ratio and the sine function may be to account for the uneven distribution of signal strength in different directions and the influence of environmental factors on signal propagation, thereby achieving more precise altitude adjustment.

[0074] In summary, the collaborative operation of the sub-modules in this embodiment enables the UAV to automatically adapt to complex and ever-changing environments, improving flight safety and mission execution accuracy. This is of great significance for the application of UAVs in fields such as search and rescue, environmental monitoring, and logistics delivery. Through the application of these technologies, UAVs can more flexibly cope with various challenges and achieve more efficient and intelligent flight control.

[0075] Example 4: Figure 4 As shown, based on Embodiment 2, the image generation module provided in this embodiment of the invention includes:

[0076] The weighted average calculation submodule is responsible for performing weighted average calculations on parameters such as signal reflection intensity, time difference, geological structure, and rock mechanical properties to generate a preliminary reflection intensity value.

[0077] The standard deviation calculation submodule is responsible for calculating the standard deviation of the reflection intensity, which reflects the fluctuation and local changes of the signal;

[0078] The result synthesis calculation submodule is responsible for multiplying the weighted average result by the standard deviation to generate the final tomographic image; combining the global features and local changes of the signal, it generates a high-resolution image.

[0079] The expression for the tomographic image in the image generation module is as follows:

[0080] ;

[0081] In the formula, This represents the weighting coefficient, used to adjust the degree of influence of different parameters on the final result. The larger the weight, the more significant the influence of the corresponding parameter on the result; Indicates the reflected intensity of the signal, used to indicate the location. The intensity of the reflected signal received at the location; The time difference is used to represent the time delay from signal transmission to reception, and is usually related to the length of the signal propagation path. This represents a time parameter, used to describe the time characteristics of a signal, which may be related to the signal's propagation speed or attenuation characteristics; Represents rock mechanical property parameters, used to indicate the location The mechanical properties of the rock (such as elastic modulus, density, etc.) are used to correct signal attenuation; Describes the layering structure parameters, used to indicate the location The geological stratification structure at the location is used to adjust the signal reflection characteristics; This represents the signal's timing parameters, which may be related to the signal's propagation time or temporal resolution. These parameters represent the bedding structure and may be related to the angle or direction of geological bedding. These parameters represent the mechanical properties of rocks and may be related to the physical properties of rocks (such as porosity and permeability). It represents the ratio of reflection intensity to time difference, used to express the reflection intensity per unit time. This represents the initial reflection intensity value generated by the weighted average. This represents the final generated tomographic image, used to indicate the location. The first part calculates the reflection intensity value at a given location. The weighted average part generates a preliminary reflection intensity value by integrating parameters such as reflection intensity, time difference, rock mechanical properties, and bedding structure. Its function is to extract global features of the signal and optimize the influence of parameters through weight allocation, ensuring the accuracy and physical meaning of the calculation results. The standard deviation part quantifies the fluctuation and local variation of reflection intensity. By calculating the deviation between the reflection intensity and the preliminary reflection intensity value, the standard deviation reflects the local details and anomalous areas of the signal, enhancing the image's detail information and providing local features for subsequent image generation. By considering both signal reflection intensity and time difference, as well as geological structure and rock mechanical properties, a high-resolution fault image is ultimately generated. This method allows for more efficient processing of point cloud data and the generation of more accurate and detailed images.

[0082] The working principle and beneficial effects of the above technical solution are as follows: The weighted average calculation submodule of this embodiment performs weighted average calculation on parameters such as signal reflection intensity, time difference, geological structure, and rock mechanical properties to generate a preliminary reflection intensity value; the standard deviation calculation submodule calculates the standard deviation of the reflection intensity, reflecting the fluctuation and local changes of the signal; the result synthesis calculation submodule multiplies the weighted average result with the standard deviation to generate the final fault image; combining the global features and local changes of the signal, a high-resolution image is generated. The weighted average calculation submodule of the above solution performs weighted average calculation on multiple key parameters such as signal reflection intensity, time difference, geological structure, and rock mechanical properties to generate a preliminary reflection intensity value; through the allocation of weights, it ensures that important parameters have a greater impact on the results, thereby improving the accuracy of the calculation; combining parameters such as rock mechanical properties and bedding structure, it performs attenuation correction and structural adjustment on the signal to ensure that the physical meaning of the signal is reasonably reflected. The significance of this approach: The weighted average calculation submodule extracts global features from complex signals, providing fundamental data for image generation. Through weighted averaging, it effectively reduces the impact of noise and outliers, improving data reliability and stability. Combined with geological and rock mechanics properties, it ensures that the generated reflection intensity values ​​have clear physical meaning, providing a scientific basis for geological analysis. The standard deviation calculation submodule quantifies signal fluctuations and local variations by calculating the standard deviation of reflection intensity, reflecting detailed information in the image. Standard deviation reveals the dispersion of data distribution, helping to identify anomalous or highly variable areas in the signal. As an indicator of local variation, standard deviation can be combined with the weighted average results to further enhance image detail and resolution. The significance of this approach: The standard deviation calculation submodule captures local variations in the signal, providing detailed information for image generation. Through standard deviation calculation, anomalous areas in the signal can be quickly identified, providing important references for geological analysis. Combining standard deviation and weighted average results, high-resolution images can be generated, meeting the needs of refined geological analysis. The results synthesis and calculation submodule multiplies the weighted average result by the standard deviation, combining global features and local variations of the signal to generate the final fault image. Through synthesis and calculation, it balances global consistency and local detail, avoiding excessive smoothing or sharpening. The final generated image has high resolution, clearly displaying fault structure and geological features. Significance: The results synthesis and calculation submodule generates high-quality fault images, providing reliable data support for blasting hazard investigation; by combining global features and local variations, the generated image possesses both overall consistency and rich detail information; high-resolution fault images provide important data for engineering planning and other fields, and have broad application value.

[0083] In summary, the weighted average calculation submodule in this embodiment extracts global features and optimizes parameters to ensure the reliability and physical meaning of the data; the standard deviation calculation submodule quantifies local changes, enhances details, and identifies abnormal areas; and the result synthesis calculation submodule combines global and local features to generate high-quality, high-resolution tomographic images. This not only improves the efficiency and accuracy of image generation but also provides strong technical support for the investigation of blasting hazards, possessing significant scientific and application value.

[0084] Example 5: Figure 5 As shown, based on Embodiment 1, the risk assessment component provided in this embodiment of the invention includes:

[0085] The feature extraction module is responsible for aligning and integrating the data from lidar and multispectral sensors in the spatiotemporal dimension, decomposing the raw data into feature components of different scales, removing high-frequency noise and low-frequency redundant information to obtain geological features; at the same time, it dynamically adjusts the filtering parameters through a quantum heuristic optimization algorithm.

[0086] The hazard identification module is responsible for classifying hazards into three categories: rock loosening, crack propagation, and geological faults. The identified hazard features are embedded through a high-dimensional manifold and nonlinearly mapped and integrated in three-dimensional space. Different types of hazards are displayed in layers in three-dimensional space to form a hazard distribution map. The hazard distribution map reflects the type, severity, and dynamic change trend of the hazard through quantum state encoding.

[0087] The report generation module is responsible for assessing the risk level of each hazard area based on the hazard distribution map. The generated risk level report is presented visually and includes the hazard distribution map, the risk level distribution map, and text descriptions.

[0088] In this process, the data from lidar and multispectral sensors need to be aligned and integrated in both spatiotemporal dimensions. Assuming the lidar data is... Multispectral sensor data is ,in Let t represent spatial coordinates and t represent time. The formulas for aligning and integrating, representing spectral wavelengths, are as follows:

[0089] ;

[0090] In the formula, This represents the integrated spatiotemporal data; This represents the spatiotemporal alignment weighting function, used to adjust the data contribution under different wavelengths and time delays; This represents a time delay variable used to align timestamps from different sensors. Indicates the spectral wavelength, used to represent different bands in multispectral sensors;

[0091] After alignment, the raw data needs to be decomposed at multiple scales to extract geological features. A method combining wavelet transform and quantum heuristic optimization algorithms is employed, as shown in the following formula:

[0092] ;

[0093] In the formula, This represents the feature component at the k-th scale; This represents the wavelet basis function, used to extract features at different scales; This represents the integration domain, used to represent the spatial and temporal range. This represents the integration variable, used to represent the spatial and temporal offsets, respectively.

[0094] Quantum heuristic formula for optimizing filter parameters, assuming the filter parameters are... The optimization objective is to minimize the energy of noise and redundant information, as expressed by the following formula:

[0095] ;

[0096] In the formula, This represents the filtering parameters at the k-th scale; Indicates the feature components after filtering; This represents the regularization parameter, used to control the smoothness of the filtering parameters; The gradient of the filter parameters is used to represent the trend of parameter changes.

[0097] Final extracted geological features The representation is obtained by weighted combination of multi-scale feature components:

[0098] ;

[0099] In the formula, Indicates the extracted geological features; This represents the weight coefficient for the k-th scale; This represents the standard deviation of the characteristic distribution at the k-th scale;

[0100] Formulas for removing high-frequency noise and low-frequency redundant information:

[0101] ;

[0102] In the formula, This represents the geological features after removing noise and redundant information. This indicates a low-pass filter used to remove low-frequency redundant information; This indicates a high-pass filter, used to remove high-frequency noise; , These represent the weight coefficients of the filter. The above formulas constitute the core computational framework of the feature extraction module, which can efficiently process lidar and multispectral sensor data, extract high-quality geological features, and provide a solid foundation for hazard identification and risk assessment.

[0103] The working principle and beneficial effects of the above technical solution are as follows: The feature extraction module of this embodiment aligns and integrates the data from lidar and multispectral sensors in the spatiotemporal dimension, decomposes the original data into feature components of different scales, removes high-frequency noise and low-frequency redundant information, and obtains geological features; at the same time, it dynamically adjusts the filtering parameters through a quantum heuristic optimization algorithm; the hazard identification module classifies hazards into three categories: rock loosening, crack propagation, and geological faults, embeds the identified hazard features through high-dimensional manifolds, performs nonlinear mapping and integration in three-dimensional space, and displays different types of hazards in three-dimensional space in layers to form a hazard distribution map. The hazard distribution map reflects the type, severity, and dynamic change trend of the hazard through quantum state encoding; the report generation module, based on the hazard distribution map, performs risk level assessment on each hazard area, and the generated risk level report is presented visually, including the hazard distribution map, the risk level distribution map, and text descriptions. The feature extraction module of the above scheme solves the problem of fusing multi-source heterogeneous data by spatiotemporally aligning the data from lidar and multispectral sensors, ensuring data integrity and consistency. It decomposes the original data into feature components of different scales, effectively removing high-frequency noise and low-frequency redundant information while retaining key geological features. Through a quantum-heuristic optimization algorithm, it dynamically adjusts filtering parameters to ensure the accuracy and adaptability of feature extraction, avoiding the limitations of fixed parameters in traditional methods. The significance is as follows: multi-scale decomposition and dynamic optimization significantly improve the accuracy and robustness of geological feature extraction; the introduction of a quantum-heuristic optimization algorithm combines traditional signal processing with quantum computing, providing a high-quality data foundation for subsequent hazard identification; and accurate feature extraction reduces noise interference in hazard identification, lowering the false positive rate. The hazard identification module categorizes hazards into three main types: rock loosening, crack propagation, and geological faults. Through high-dimensional manifold embedding technology, it performs nonlinear mapping and integration in three-dimensional space, achieving precise classification and location of hazards. Different types of hazards are displayed hierarchically in three-dimensional space, forming a hazard distribution map. Quantum state encoding visually reflects the type, severity, and dynamic trends of hazards. Regular scanning by drones and real-time quantum computing technology dynamically update the hazard distribution map, ensuring data timeliness and accuracy. Significance achieved: High-dimensional manifold embedding technology enables precise location and classification of hazards, providing a scientific basis for subsequent risk assessment; three-dimensional hierarchical display and quantum state encoding visually present the distribution and severity of hazards, facilitating quick understanding of hazard situations by decision-makers; dynamic tracking and updating enable real-time monitoring of hazards, significantly improving the safety of blasting operations.The report generation module, based on the hazard distribution map and incorporating a quantum geomechanical model and the dynamic changing trends of the hazards, assesses the risk level of each hazard area. Through quantum visualization technology, it generates a risk level report including a hazard distribution map, a risk level distribution map, and detailed textual descriptions. When dynamic changes in a hazard are detected, the system automatically generates an early warning signal through a quantum state triggering mechanism and pushes it to the mobile terminals of relevant personnel via quantum communication technology. Significance achieved: The risk level assessment and visualization report provide a scientific basis for blasting operations, helping decision-makers formulate more reasonable blasting plans; the dynamic early warning mechanism enables real-time response to changes in hazards, significantly reducing the risks of blasting operations; the introduction of quantum visualization and quantum communication technologies enhances the intuitiveness and real-time nature of the report, providing an innovative solution for the safety management of blasting operations.

[0104] In summary, this embodiment achieves accurate identification, dynamic monitoring, and scientific assessment of blasting hazards. The feature extraction module improves data quality and reduces the false positive rate, providing a high-quality data foundation for hazard identification. The hazard identification module enables precise location and classification of hazards, and significantly improves the safety of blasting operations through three-dimensional hierarchical display and dynamic monitoring. The report generation module provides a scientific basis for blasting operations through risk level assessment and visual reports, and achieves real-time response to changes in hazards through a dynamic early warning mechanism.

[0105] Example 6: As Figure 6 As shown, based on Embodiment 5, the hazard identification module provided in this embodiment of the invention includes:

[0106] The rock mass loosening identification submodule is responsible for identifying local density variation regions in point cloud data through fractal geometry and chaotic dynamics analysis; combining multi-scale entropy analysis of fault images, it quantifies the degree of loosening inside the rock mass and dynamically tracks the boundaries and depth of loosened regions.

[0107] The crack propagation identification submodule is responsible for capturing the quantum state features (such as reflectivity phase changes) of crack edges using quantum images, and dynamically predicting the extension direction and propagation rate of cracks by combining spatiotemporal correlation analysis of point cloud data; and accurately separating the crack region from the complex background through quantum image segmentation to form an independent feature layer.

[0108] The geological fault identification submodule is responsible for identifying the nonlinear characteristics of stratigraphic dislocation and fracture regions based on the nonlinear dynamic model of fault images; reconstructing the three-dimensional structure of the fault through quantum interpolation; and evaluating its potential impact on blasting safety by combining stress field simulation in quantum mechanics.

[0109] The working principle and beneficial effects of the above technical solution are as follows: The rock mass loosening identification submodule of this embodiment identifies local density variation regions in point cloud data through fractal geometry and chaotic dynamics analysis; combined with multi-scale entropy analysis of fault images, it quantifies the loosening degree inside the rock mass and dynamically tracks the boundary and depth of the loosening region; The crack propagation identification submodule uses quantum images to capture the quantum state characteristics (such as reflectivity phase change) of crack edges, and combines spatiotemporal correlation analysis of point cloud data to dynamically predict the extension direction and propagation rate of cracks; it accurately separates the crack region from the complex background through quantum image segmentation to form an independent feature layer; The geological fault identification submodule identifies the nonlinear characteristics of stratigraphic dislocation and fracture regions based on the nonlinear dynamic model of fault images; it reconstructs the three-dimensional structure of the fault through quantum interpolation, and combines stress field simulation in quantum mechanics to evaluate its potential impact on blasting safety. The rock mass loosening identification submodule of the above scheme utilizes fractal geometry and chaotic dynamics analysis to capture areas of abnormal density changes in point cloud data. Through multi-scale entropy analysis of fault images, it quantifies the degree of loosening within the rock mass and dynamically tracks the boundaries and depth of loosened areas. Significance: Rock mass loosening is a significant precursor to geological disasters. Early identification of loosened areas provides early warning information for engineering safety, preventing accidents such as collapses and landslides caused by rock mass instability. Simultaneously, the dynamic tracking function provides precise data support for engineering remediation, helping to develop more effective reinforcement plans. The crack propagation identification submodule can monitor the dynamic changes of cracks in real time and predict their development trends, providing a scientific basis for engineering safety assessments. Precise crack separation technology also provides a reliable data foundation for repair and reinforcement work. The geological fault identification submodule can accurately identify the three-dimensional structure and stress distribution of faults, providing scientific guidance for blasting design and construction, preventing engineering accidents caused by fault activity. Simultaneously, stress field simulation technology provides important support for long-term monitoring and early warning of geological disasters.

[0110] In summary, this embodiment utilizes multidisciplinary technologies to achieve comprehensive monitoring and analysis of rock mass conditions. It not only enables the early detection of potential safety hazards but also provides precise data support for engineering management, significantly improving the construction safety and stability of rock mass engineering and reducing the incidence of engineering accidents.

[0111] Example 7: As Figure 7 As shown, based on Example 6, the rock mass loosening identification submodule provided in this embodiment of the invention includes:

[0112] The fractal geometry analysis unit is responsible for calculating the fractal dimension of point cloud data and identifying the self-similarity characteristics of the rock mass surface and interior. By analyzing the dynamic changes of point cloud data, it monitors key parameters such as the rate of change and fluctuation range to identify density variation regions that exhibit nonlinear characteristics.

[0113] The rock mass loosening quantization unit is responsible for multi-scale decomposition of fault images and extraction of entropy distribution characteristics at different scales; combined with density variation regions in point cloud data, it obtains the degree of loosening inside the rock mass and quantifies its spatial distribution and intensity; it quantifies the degree of loosening of the rock mass into a probability density function and generates a distribution map of the degree of loosening inside the rock mass;

[0114] The boundary and tracking unit is responsible for using the analysis results of fractal geometry to identify the boundary features of the loosened area and updating the boundary contour in real time by using dynamic threshold segmentation; combining the spatial distribution characteristics of point cloud data and multi-scale entropy analysis of tomographic images, it calculates the depth distribution of the loosened area, reconstructs the three-dimensional structure of the loosened area, and dynamically updates its depth changes.

[0115] The working principle and beneficial effects of the above technical solution are as follows: The fractal geometry analysis unit of this embodiment performs fractal dimension calculation on point cloud data to identify the self-similarity characteristics of the rock mass surface and interior; by analyzing the dynamic changes of point cloud data, it monitors the key parameters of change rate and fluctuation range to identify those density variation regions exhibiting nonlinear characteristics; the rock mass loosening quantification unit decomposes the fault image into multiple scales and extracts the distribution characteristics of entropy values ​​at different scales; combined with the density variation regions in the point cloud data, it obtains the degree of loosening inside the rock mass and quantifies its spatial distribution and intensity; it quantifies the degree of loosening of the rock mass into a probability density function and generates a distribution map of the degree of loosening inside the rock mass; the boundary and tracking unit uses the analysis results of fractal geometry to identify the boundary features of the loosened area and uses dynamic threshold segmentation to update the boundary contour in real time; combined with the spatial distribution characteristics of the point cloud data and the multi-scale entropy value analysis of the fault image, it calculates the depth distribution of the loosened area, reconstructs the three-dimensional structure of the loosened area, and dynamically updates its depth changes. The fractal geometry analysis unit of the above scheme identifies the self-similarity characteristics of the rock mass surface and interior by calculating the fractal dimension of the point cloud data, thereby capturing the complex structure and details of the rock mass. By monitoring the dynamic changes of the point cloud data, it can identify density variation regions with nonlinear characteristics, which is crucial for judging the stability of the rock mass. Significance: This method can more accurately identify potential unstable factors in the rock mass, providing a scientific basis for preventing geological disasters and ensuring the safety of personnel and equipment. The rock mass loosening quantification unit decomposes fault images at multiple scales and extracts the entropy distribution characteristics at different scales. Combined with the density variation regions in the point cloud data, it quantifies the degree of loosening inside the rock mass and forms a probability density function distribution map, which intuitively displays the spatial distribution and intensity of the loosening degree inside the rock mass. Significance: Quantifying and visualizing the degree of loosening of the rock mass can provide detailed and accurate rock mass state information for engineering design and construction, which helps to optimize construction plans and prevent disasters such as rock mass collapse. The boundary and tracking unit utilizes fractal geometry analysis to identify the boundary features of loosened areas and updates the boundary contours in real time using dynamic threshold segmentation technology. Combining the spatial distribution of point cloud data and multi-scale entropy analysis of fault images, it calculates the depth distribution of loosened areas and reconstructs their three-dimensional structure, while dynamically updating their depth changes. Significance: Dynamically tracking changes in loosened areas is crucial for timely adjustments to construction plans and addressing potential risks; real-time monitoring and analysis capabilities significantly improve the efficiency and speed of monitoring and responding to rock mass stability.

[0116] In summary, the rock mass loosening identification submodule of this embodiment, by comprehensively utilizing advanced technologies such as fractal geometric analysis, multi-scale decomposition, and probability density function quantization, can not only accurately assess the stability and loosening degree of rock mass, but also provide strong technical support for the prevention of geological disasters and engineering safety.

[0117] Example 8: As Figure 8 As shown, based on Embodiment 6, the crack propagation identification submodule provided in this embodiment of the invention includes:

[0118] The quantum state feature detection unit is responsible for establishing a quantum sensor network that is in direct contact with the crack edge. It uses quantum effects to detect the crack edge and sense changes in the microscopic quantum state; it analyzes the quantum state features of the crack edge, converts the quantum state features into identifiable information, encodes and decodes the quantum state, and obtains microscopic physical phenomena such as changes in reflectivity and phase shifts.

[0119] The quantum state feature mapping unit is responsible for converting the non-classical properties of quantum states into image data and mapping the analyzed feature information onto the three-dimensional image of the crack. It uses the feature information of quantum states to dynamically track the crack edge, and predicts the crack's propagation trend and speed by monitoring the changes in the quantum state at the crack edge in real time, thereby achieving real-time monitoring of the crack's dynamic behavior.

[0120] The quantum image segmentation unit is responsible for segmenting the crack region and constructing the segmented crack region into an independent layer containing all quantum state feature information of the crack.

[0121] The working principle and beneficial effects of the above technical solution are as follows: The quantum state feature detection unit in this embodiment establishes a quantum sensor network in direct contact with the crack edge, utilizes quantum effects to detect the crack edge, and senses changes in microscopic quantum states; it analyzes the quantum state features of the crack edge, converts the quantum state features into identifiable information, encodes and decodes the quantum state, and obtains microscopic physical phenomena including changes in reflectivity and phase shifts; the quantum state feature mapping unit converts the non-classical characteristics of the quantum state into image data, and maps the analyzed feature information onto a three-dimensional image of the crack; it uses the feature information of the quantum state to dynamically track the crack edge, and predicts the crack's propagation trend and speed by real-time monitoring of changes in the quantum state of the crack edge, thus achieving real-time monitoring of the crack's dynamic behavior; the quantum image segmentation unit segments the crack region, constructs the segmented crack region into an independent layer, and contains all the quantum state feature information of the crack. The quantum state feature detection unit of the above solution can provide in-depth information on the crack state, which is crucial for early crack identification and preventive maintenance; through high-sensitivity detection, problems can be detected in time before crack propagation causes structural damage, thereby reducing costs and improving safety. The quantum state feature mapping unit, by mapping quantum state features onto an image, allows for intuitive observation and understanding of crack conditions. This facilitates a better understanding of crack dynamics and provides more accurate data support for structural maintenance and safety assessment. Predicting crack propagation trends and speeds helps in taking preventative measures to avoid potential structural damage. The independent layers obtained by the quantum image segmentation unit facilitate further crack analysis and processing, such as crack morphology analysis and depth estimation. Segmentation techniques improve the accuracy and efficiency of crack analysis, enabling targeted repair and maintenance.

[0122] In summary, this embodiment improves the accuracy and real-time performance of crack identification by comprehensively utilizing quantum technology, which is of great significance for ensuring structural safety, extending service life, and reducing maintenance costs.

[0123] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of equivalents of this invention, this invention is also intended to include these modifications and variations.

Claims

1. A blasting hazard investigation system based on unmanned aerial vehicle (UAV) tomography, characterized in that, Include: The data acquisition component is responsible for performing 3D modeling and tomographic scanning of the blasting target area, generating high-resolution point cloud data and tomographic images; The risk assessment component is responsible for analyzing point cloud data and fault images to automatically identify potential hazards such as rock mass loosening, crack propagation, and geological fault blasting. The dynamic early warning component is responsible for long-term monitoring of key blasting areas. UAVs regularly scan the blasting target area and update the 3D model and hazard data in real time. Risk assessment components include: The feature extraction module is responsible for aligning and integrating the data from lidar and multispectral sensors in the spatiotemporal dimension, decomposing the raw data into feature components of different scales to obtain geological features; The hazard identification module is responsible for classifying hazards into three categories: rock loosening, crack propagation, and geological faults. It displays different types of hazards in a three-dimensional space to form a hazard distribution map. The report generation module is responsible for assessing the risk level of each hazard area based on the hazard distribution map, and the generated risk level report is presented visually. Among these, the data from lidar and multispectral sensors need to be aligned and integrated in the spatiotemporal dimensions. The lidar data is... Multispectral sensor data is ,in Let t represent spatial coordinates and t represent time. The formulas for aligning and integrating, representing spectral wavelengths, are as follows: ； In the formula, This represents the integrated spatiotemporal data; This represents the spatiotemporal alignment weighting function, used to adjust the data contribution under different wavelengths and time delays; This represents a time delay variable used to align timestamps from different sensors. Indicates the spectral wavelength; After the raw data is aligned, geological features need to be extracted through multi-scale decomposition. A method combining wavelet transform and quantum heuristic optimization algorithm is used, as shown in the following formula: ； In the formula, This represents the feature component at the k-th scale; This represents the wavelet basis function, used to extract features at different scales; Represent the integration field; Represents the integral variable. Indicates the spatial offset. Indicates the time offset; Quantum heuristic formula for optimizing filter parameters, assuming the filter parameters are... The optimization objective is to minimize the energy of noise and redundant information, as expressed by the following formula: ； In the formula, This represents the filtering parameters at the k-th scale; Indicates the feature components after filtering; This represents the regularization parameter, used to control the smoothness of the filtering parameters; Represents the gradient of the filter parameters; Final extracted geological features The representation is obtained by weighted combination of multi-scale feature components: ； In the formula, Indicates the extracted geological features; This represents the weight coefficient for the k-th scale; This represents the standard deviation of the characteristic distribution at the k-th scale; Formulas for removing high-frequency noise and low-frequency redundant information: ； In the formula, This represents the geological features after removing noise and redundant information. This indicates a low-pass filter used to remove low-frequency redundant information; This indicates a high-pass filter, used to remove high-frequency noise; , Represents the weighting coefficients of the filter; The hazard identification module includes: The rock mass loosening identification submodule is responsible for identifying local density variation regions in point cloud data through fractal geometry and chaotic dynamics analysis; combining multi-scale entropy analysis of fault images, it quantifies the degree of loosening inside the rock mass and dynamically tracks the boundaries and depth of loosened regions. The crack propagation identification submodule is responsible for capturing the quantum state features of crack edges using quantum images, combining spatiotemporal correlation analysis of point cloud data, dynamically predicting the extension direction and propagation rate of cracks; and accurately separating the crack region from the complex background through quantum image segmentation to form an independent feature layer. The geological fault identification submodule is responsible for identifying the nonlinear characteristics of stratigraphic dislocation and fracture regions based on the nonlinear dynamic model of fault images; reconstructing the three-dimensional structure of the fault through quantum interpolation and combining it with stress field simulation in quantum mechanics to assess its potential impact on blasting safety. The rock mass loosening identification submodule includes: The fractal geometry analysis unit is responsible for calculating the fractal dimension of point cloud data and identifying the self-similarity characteristics of the rock mass surface and interior. By analyzing the dynamic changes of point cloud data, it monitors key parameters such as the rate of change and fluctuation range to identify density variation regions that exhibit nonlinear characteristics. The rock mass loosening quantization unit is responsible for multi-scale decomposition of fault images and extraction of entropy distribution characteristics at different scales; combined with density variation regions in point cloud data, it obtains the degree of loosening inside the rock mass and quantifies its spatial distribution and intensity; it quantifies the degree of loosening of the rock mass into a probability density function and generates a distribution map of the degree of loosening inside the rock mass; The boundary and tracking unit is responsible for using the analysis results of fractal geometry to identify the boundary features of the loosened area and updating the boundary contour in real time by using dynamic threshold segmentation; combining the spatial distribution characteristics of point cloud data and multi-scale entropy analysis of tomographic images, it calculates the depth distribution of the loosened area, reconstructs the three-dimensional structure of the loosened area, and dynamically updates its depth changes. The crack propagation identification submodule of the hazard identification module includes: The quantum state feature detection unit is responsible for establishing a quantum sensor network in direct contact with the crack edge, using quantum effects to detect the crack edge and sense changes in the microscopic quantum state; analyzing the quantum state features of the crack edge, converting the quantum state features into identifiable information, and encoding and decoding the quantum state; The quantum state feature mapping unit is responsible for converting the non-classical properties of quantum states into image data and mapping the analyzed feature information onto the three-dimensional image of the crack. It uses the feature information of quantum states to dynamically track the crack edge and predicts the crack's propagation trend and speed by monitoring the changes in the quantum state at the crack edge in real time, thus achieving real-time monitoring of the crack's dynamic behavior. The quantum image segmentation unit is responsible for segmenting the crack region and constructing the segmented crack region into an independent layer.

2. The blasting hazard investigation system based on UAV tomographic scanning as described in claim 1, characterized in that, The data acquisition component includes: The signal transmission module is responsible for transmitting signals, calculating the round-trip time difference of the signal, and capturing subtle undulations and structural features of the target area. The dynamic scanning module is responsible for enabling the UAV to scan at a fixed altitude and along a fixed path according to a dynamic scanning strategy. The image generation module is responsible for generating high-resolution point cloud data. The point cloud data is processed and tomographic images are generated through tomographic scanning.

3. The blasting hazard investigation system based on UAV tomographic scanning as described in claim 2, characterized in that, The dynamic scanning module includes: The basic parameter calculation submodule is responsible for calculating the distance from the UAV to the explosion target area and the ambient temperature parameters. The flight altitude adjustment submodule is responsible for calculating the impact of angle adjustment based on the signal reflection intensity and average value, and adjusting the altitude in combination with the effect of temperature on the speed of sound. The integrated adjustment submodule is responsible for integrating the calculation results, as well as the effects of the square root of the signal strength ratio and the sine function, to derive the new flight altitude of the UAV after adjustment.

4. The blasting hazard investigation system based on UAV tomographic scanning as described in claim 3, characterized in that, The expression for the new flight altitude of the drone after adjustment is derived as follows: ； In the formula, This is the new flight altitude of the drone after adjustments; This is the drone's current flight altitude; It is the current signal reflection intensity; It is the average value of the signal reflection intensity over a period of time, used to eliminate environmental noise; It is the distance from the drone to the target area of ​​the explosion, calculated using the round-trip time difference t of the signal and the speed of light c; It is the scanning angle of the drone; It is the arctangent function, used to adjust the height based on the signal reflection intensity and distance; It is a cosine function, used to take into account the effect of angle adjustment; It is the Boltzmann constant, used to calculate the effect of temperature on the speed of sound; It is the ambient temperature; It is a logarithmic function used to calculate the logarithmic value of the signal strength ratio; It is the base of the natural logarithm; It is the square root function, used to calculate the square root value of the signal strength ratio; It is a sine function used to take into account the effects of angle adjustment.

5. The blasting hazard investigation system based on UAV tomographic scanning as described in claim 2, characterized in that, Image generation module, including: The weighted average calculation submodule is responsible for performing weighted average calculations on the signal parameters to generate a preliminary reflection intensity value. The standard deviation calculation submodule is responsible for calculating the standard deviation of the reflection intensity, which reflects the fluctuation and local changes of the signal; The result aggregation calculation submodule is responsible for multiplying the weighted average result by the standard deviation to generate the final tomographic image.

6. The blasting hazard investigation system based on UAV tomographic scanning as described in claim 5, characterized in that, The expression for the tomographic image in the image generation module is: ； In the formula, This represents the weighting coefficient, which is used to adjust the degree of influence of different parameters on the final result. The larger the weight, the more significant the influence of the corresponding parameter on the result. Indicates the intensity of signal reflection; Indicates time difference; Indicates time parameters; Indicates rock mechanical property parameters; Indicates the parameters of the layered structure; This represents the signal's time parameter, which is related to the signal's propagation time or time resolution. These parameters represent the bedding structure and are related to the angle or direction of geological bedding. These parameters represent the mechanical properties of rocks and are related to their physical properties. This represents the ratio of reflection intensity to time difference. This represents the initial reflection intensity value generated by the weighted average. This represents the final generated tomographic image.

7. The blasting hazard investigation system based on UAV tomographic scanning as described in claim 1, characterized in that, The quantum state feature detection unit encodes and decodes the quantum state to obtain microscopic physical phenomena including changes in reflectivity and phase shifts.

8. The blasting hazard investigation system based on UAV tomographic scanning as described in claim 1, characterized in that, The quantum image segmentation unit is constructed into an independent layer, containing all quantum state feature information of the crack.

Images