Method for monitoring linear engineering risk reduction based on drone technology

Abstract

The application relates to the technical field of monitoring management, and provides a linear engineering risk reduction monitoring method based on unmanned aerial vehicle technology. Based on phase change data in geological data of an engineering area, a partial derivative of a geological anomaly field is determined, a gradient tensor formed by the partial derivative is inverted to generate a distribution matrix representing a three-dimensional geological anomaly, so as to determine a region to be detected, polarization imaging data of the region to be detected are simulated to obtain polarization response output, model predicted light intensity and actually measured light intensity in the polarization response output are fitted to determine defect geometric parameters; a change characteristic parameter of a geological structure in the engineering area is determined through topographic data collected by a radar, so as to determine a local topographic index, generate a risk field distribution of the engineering area, and control the unmanned aerial vehicle to perform monitoring. The risk of local topography is evaluated to control the unmanned aerial vehicle to perform monitoring, comprehensive and accurate risk control of the engineering area is realized, and the efficiency and reliability of engineering safety monitoring are effectively improved.

Description

Technical Field

[0001] This application relates to the field of monitoring and management technology, and more specifically, to a monitoring method for linear engineering risk reduction based on unmanned aerial vehicle (UAV) technology. Background Technology

[0002] The risk reduction monitoring method for linear engineering projects based on UAV technology is mainly applied during the construction and operation phases of linear projects such as highways, railways, and pipelines. Linear projects typically feature long routes, large spans, and complex terrain, facing geological risks such as landslides, settlement, and cracks. Traditional monitoring methods are inefficient and costly, failing to meet the needs of risk reduction. UAV technology, however, is flexible and efficient, capable of carrying various sensors to achieve rapid and comprehensive monitoring, providing a new solution for risk reduction in linear engineering projects.

[0003] However, this method also has some technical problems, such as complex data processing and analysis, low accuracy in identifying and processing geological anomalies, which in turn leads to low reliability in risk assessment and prediction. Summary of the Invention

[0004] This application provides a monitoring method for linear engineering risk reduction based on UAV technology, which can at least partially solve the problem of low reliability in risk assessment and prediction.

[0005] Other features and advantages of this application will become apparent from the following detailed description, or may be learned in part from practice of this application.

[0006] According to one aspect of this application, a monitoring method for linear engineering risk reduction based on unmanned aerial vehicle (UAV) technology is provided, comprising: acquiring geological data of an engineering area using sensing devices mounted on the UAV; determining partial derivatives of a geological anomaly field based on phase change data in the geological data, inverting the gradient tensor formed by the partial derivatives to generate a distribution matrix characterizing three-dimensional geological anomalies; determining a region to be detected based on the distribution matrix, simulating polarization imaging data of the region to be detected acquired by an imaging device to obtain a polarization response output, fitting the model-predicted light intensity and the measured light intensity of the polarization response output to determine defect geometric parameters; determining characteristic parameters of geological structure changes in the engineering area using geomorphological data collected by radar, and determining a local geomorphological index based on the characteristic parameters and model parameters determined by the defect geometric parameters; determining the risk field distribution of the engineering area based on the local geomorphological index, and controlling the UAV to monitor the engineering area based on the risk field distribution.

[0007] In this application, based on the aforementioned scheme, the step of acquiring geological data of the engineering area through the sensing device carried by the UAV includes: controlling the UAV to cruise along a flight path covering the engineering area, acquiring geological data of the engineering area through the sensing device carried by the UAV; performing noise filtering on the geological data to generate first data, aligning the coordinate system in the first data, and generating preprocessed geological data.

[0008] In this application, based on the aforementioned scheme, the step of determining the partial derivatives of the geological anomaly field based on the phase change data in the geological data, and inverting the gradient tensor formed by the partial derivatives to generate a distribution matrix characterizing the three-dimensional geological anomaly includes: determining the partial derivatives of the geological anomaly field based on the phase change data in the geological data; arranging the partial derivatives according to a set structure based on the correspondence between the partial derivatives and each element in the distribution matrix to generate a gradient tensor characterizing the localized anomaly; and inverting the geological data and the gradient tensor to generate a distribution matrix characterizing the three-dimensional geological anomaly.

[0009] In this application, based on the aforementioned scheme, determining the partial derivative of the geological anomaly field based on phase change data in geological data includes: determining the partial derivative of the geological anomaly field based on phase change data in geological data. for:

[0010]

[0011] in, Represents the distribution matrix B exist i Components in direction j The partial derivatives; h Represents the quantum effect constant. This represents the phase change data caused by the deflection of the quantum spin direction. Represents the electron gyromagnetic constant. Indicates the sensing area, Indicates the sampling interval. Indicates the relevant time.

[0012] In this application, based on the aforementioned scheme, the step of determining the region to be detected according to the distribution matrix, simulating the polarization imaging data of the region to be detected acquired by the imaging device to obtain the polarization response output, and fitting the model-predicted light intensity and the measured light intensity of the polarization response output to determine the defect geometric parameters includes: determining the region to be detected according to the distribution matrix; acquiring the polarization imaging data of the region to be detected through the imaging device; determining the polarization response output under different incident angles and wavelengths based on the polarization imaging data and preset model parameters; and fitting the model-predicted light intensity of the polarization response output and the measured light intensity acquired by the polarization imager to determine the defect geometric parameters.

[0013] In this application, based on the aforementioned scheme, the step of determining the variation characteristic parameters of the geological structure in the engineering area using geomorphological data collected by radar, and determining the local geomorphological index based on the variation characteristic parameters and the model parameters determined by the defect geometric parameters, includes: acquiring geomorphological data of the area to be detected by radar; determining the variation characteristic parameters of the geological structure in the area to be detected in the constructed regional grid based on the geomorphological data; determining the quality index and singular spectrum intensity of the geomorphological model based on the defect geometric parameters; and determining the local geomorphological index corresponding to the local area through index calculation based on the variation characteristic parameters, the quality index, and the singular spectrum intensity.

[0014] In this application, based on the aforementioned scheme, after determining the geological structure variation characteristic parameters of the engineering area using geomorphological data collected by radar, and determining the local geomorphological index based on the variation characteristic parameters and the model parameters determined by the defect geometric parameters, the method further includes: comparing the local geomorphological index with a preset first threshold in the risk assessment model to determine the risk level corresponding to the geomorphological index; comparing the variation characteristic parameters with a preset second threshold in the risk assessment model to determine the stability level of the engineering area; and generating an engineering geological assessment report based on the risk level and the stability level.

[0015] In this application, based on the aforementioned scheme, after generating the engineering geological assessment report based on the risk level and the stability level, the method further includes: using visualization tools to visualize the engineering geological assessment report in the form of charts.

[0016] In this application, based on the aforementioned scheme, the step of determining the risk field distribution of the engineering area based on the local topographic index and controlling the UAV to monitor the engineering area based on the risk field distribution includes: generating the risk field distribution of the engineering area based on the local topographic index; planning and generating the UAV's cruise route based on the risk field distribution; and controlling the UAV to monitor the engineering area based on the cruise route.

[0017] In this application, based on the aforementioned scheme, after planning and generating the drone's cruise route according to the risk field distribution, the method further includes: displaying the risk field distribution and the cruise route on the interface of the control platform according to a set display method.

[0018] According to one aspect of this application, a computer-readable medium is provided having a computer program stored thereon, which, when executed by a processor, implements the monitoring method for linear engineering risk reduction based on UAV technology as described above.

[0019] According to one aspect of this application, an electronic device is provided, comprising: one or more processors; and a storage device for storing one or more programs, which, when executed by the one or more processors, cause the one or more processors to implement the monitoring method for linear engineering risk reduction based on UAV technology as described in the above embodiments.

[0020] According to one aspect of this application, a computer program product or computer program is provided, comprising computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the monitoring method for linear engineering risk reduction based on unmanned aerial vehicle (UAV) technology provided in the various alternative implementations described above.

[0021] In the technical solution of this application, geological risks are accurately identified by efficiently collecting geological data through unmanned aerial vehicles (UAVs) and combining this with geological anomaly analysis and defect geometric parameter determination. Risks are assessed using radar geomorphological data and local geomorphological indices, and finally, UAV monitoring is controlled based on the risk field distribution. This achieves comprehensive and accurate risk management of the engineering area, effectively improving the efficiency and reliability of engineering safety monitoring.

[0022] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description

[0023] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application. It is obvious that the drawings described below are merely some embodiments of this application, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort.

[0024] Figure 1 The flowchart illustrating a monitoring method for linear engineering risk reduction based on UAV technology in one embodiment of this application is shown.

[0025] Figure 2 The flowchart illustrating the generation of the distribution matrix is ​​shown in one embodiment of this application.

[0026] Figure 3 The illustration shows a schematic diagram of a monitoring device for linear engineering risk reduction based on drone technology in one embodiment of this application.

[0027] Figure 4 A schematic diagram of the structure of a computer system suitable for implementing the electronic device of the present application is shown. Detailed Implementation

[0028] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided to make this application more comprehensive and complete, and to fully convey the concept of the exemplary embodiments to those skilled in the art.

[0029] Furthermore, the described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. Numerous specific details are provided in the following description to give a thorough understanding of embodiments of this application. However, those skilled in the art will recognize that the technical solutions of this application can be practiced without one or more of the specific details, or other methods, components, apparatuses, steps, etc., can be employed. In other instances, well-known methods, apparatuses, implementations, or operations are not shown or described in detail to avoid obscuring various aspects of this application.

[0030] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0031] The flowcharts shown in the accompanying drawings are merely illustrative and do not necessarily include all content and operations / steps, nor do they necessarily have to be performed in the described order. For example, some operations / steps can be broken down, while others can be combined or partially combined; therefore, the actual execution order may change depending on the specific circumstances.

[0032] The implementation details of the technical solution of this application are described below:

[0033] Figure 1 A flowchart illustrating a monitoring method for linear engineering risk reduction based on unmanned aerial vehicle (UAV) technology according to an embodiment of this application is shown. (Refer to...) Figure 1As shown, the monitoring method for linear engineering risk reduction based on UAV technology includes at least steps S110 to S150, which are described in detail below:

[0034] The S110 uses sensors mounted on a drone to acquire geological data of the engineering area.

[0035] In one embodiment of this application, geological data of the engineering area is acquired by a sensing device carried by a drone, wherein the sensing device includes: a positioning module, radar, a multispectral camera, an inertial navigation unit, and a quantum magnetometer array, etc.

[0036] During the flight of the drone, the sensing equipment scans or photographs the engineering area according to the preset route to obtain geological data, and sends it to the ground control station or data processing center through the data transmission system. Finally, the data is preprocessed and analyzed to extract useful geological information, providing data support for subsequent risk assessment, stability analysis and other tasks.

[0037] In one embodiment of this application, geological data of the engineering area is acquired using sensing devices mounted on a drone, including:

[0038] Control the drone to cruise along the flight path covering the engineering area, and acquire geological data of the engineering area through the sensing equipment carried by the drone;

[0039] The geological data is subjected to noise filtering to generate first data. The coordinate system in the first data is aligned to generate preprocessed geological data.

[0040] In one embodiment of this application, a drone equipped with a quantum magnetometer array cruises along a preset flight path that covers the linear engineering area to be monitored, such as the engineering area along pipelines, railways or highways, thereby achieving high-density scanning of the engineering area and improving the spatial resolution of geological anomaly fields.

[0041] During flight, magnetic field measurements are performed using quantum spin states. When at a specific energy level, the spin direction is deflected by an external magnetic field. By detecting the phase difference and deflection caused by this spin direction deflection, the strength of the external magnetic field can be accurately measured. Quantum magnetometers utilize quantum coherent superposition states to improve measurement sensitivity. When in a coherent superposition state, the response to magnetic fields is more sensitive, enabling higher precision measurements. Quantum magnetometers, using quantum coherent superposition states for measurement, possess strong anti-interference capabilities and can operate normally in complex electromagnetic environments.

[0042] In addition, the inertial navigation unit and positioning module work simultaneously, synchronously recording geomagnetic data, UAV attitude information, and position information. Accelerometers and gyroscopes are used to measure the UAV's acceleration and angular velocity, and position, attitude, and geological data are obtained through integration calculations.

[0043] After acquiring the geological data for the project, noise filtering is performed on the raw data collected by the quantum magnetometer to remove high-frequency noise and low-frequency drift, yielding the first data. Then, the position and attitude information provided by the inertial navigation unit and positioning module, along with the geological data, are aligned with the first data from the quantum magnetometer to ensure spatial and temporal consistency. Preprocessing the raw data, including noise filtering, sensor calibration, and coordinate alignment, improves data quality and provides a reliable data foundation for subsequent anomaly field reconstruction.

[0044] S120, Based on the phase change data in the geological data, determine the partial derivatives of the geological anomaly field, invert the gradient tensor formed by the partial derivatives, and generate a distribution matrix characterizing the three-dimensional geological anomaly.

[0045] In one embodiment of this application, phase change information is first extracted from geological data, and the partial derivatives of the geological anomaly field in each direction are calculated to construct a gradient tensor. Then, the gradient tensor is mathematically processed by an inversion algorithm to invert the spatial location and magnetic characteristics of the underground geological body. Finally, a distribution matrix is ​​generated to intuitively show the spatial distribution of geomagnetic anomalies, providing a basis for geological interpretation and resource exploration.

[0046] like Figure 2 As shown, in one embodiment of this application, based on the phase change data in the geological data, the partial derivatives of the geological anomaly field are determined, and the gradient tensor formed by the partial derivatives is inverted to generate a distribution matrix characterizing the three-dimensional geological anomaly, including:

[0047] S210, based on phase change data in geological data, determine the partial derivatives of the geological anomaly field;

[0048] S220, Based on the correspondence between the partial derivatives and the elements in the distribution matrix, the partial derivatives are arranged according to the set structure to generate a gradient tensor that characterizes the location anomaly.

[0049] S230, Invert the geological data and the gradient tensor to generate a distribution matrix characterizing three-dimensional geological anomalies.

[0050] In practical applications, geological anomalies such as underground cavities and pipeline leaks can cause changes in the geomagnetic field, forming a geological anomaly field. The gradient tensor of the geological anomaly describes the rate of change and direction of the geological anomaly field in space. By first calculating the partial derivatives of the geological anomaly field and combining them to generate the gradient tensor of the geological anomaly field, and then performing an inversion to convert the gradient tensor into a three-dimensional distribution matrix of the geological anomaly, the visualization of the geological anomaly can be achieved.

[0051] In one embodiment of this application, the partial derivatives of a geological anomaly field are determined based on phase change data in geological data. for:

[0052]

[0053] in, Represents the distribution matrix B exist i Components in direction j The partial derivatives; h Represents the quantum effect constant. Indicates the direction of quantum spin ( i,j Phase change data caused by deflection represents the electron gyromagnetic constant, and represents the ratio between the electron's magnetic moment and angular momentum. Indicates the sensing area, Indicates the sampling interval. Indicates the relevant time.

[0054] In one embodiment of this application, the obtained partial derivatives correspond to each element in the gradient tensor. The partial derivatives are arranged according to a set structure to generate a gradient tensor representing the localization anomaly. G for:

[0055]

[0056] in, Represent the distribution matrix respectively B The partial derivatives with respect to the components (x, y, z) in each direction (x, y, z). Through the above process, the gradient tensor is generated. G It is the gradient of the distribution matrix, a 3×3 matrix that contains information on the rate of change of the geological anomaly field in three spatial directions (x, y, z).

[0057] In one embodiment of this application, after generating the gradient tensor, the measurement value of the geomagnetic anomaly gradient tensor and its corresponding location information can be obtained by using a quantum magnetometer array, inertial navigation unit, and positioning module mounted on a UAV. Using these measurement values ​​and location information, as well as other geological data, the gradient tensor is... GThree-dimensional inversion calculations are performed to determine the magnetization values ​​within the underground space. These magnetization values ​​are then arranged into a matrix according to their spatial location to generate a distribution matrix. B .

[0058] The above process, by determining the partial derivatives of the geological anomaly field based on phase change data in geological data, enables a quantitative description of the spatial variation characteristics of geological anomalies. By inverting the gradient tensor formed by the partial derivatives to generate a three-dimensional geological anomaly distribution matrix, three-dimensional visualization of geological anomalies is achieved. This facilitates the intuitive and accurate identification of the location, extent, and morphology of geological anomalies. This process fully utilizes the high-sensitivity measurement capabilities of the quantum magnetometer array and the computational power of three-dimensional inversion, achieving precise reconstruction of the geological anomaly field.

[0059] S130, the area to be detected is determined according to the distribution matrix, and the polarization imaging data of the area to be detected is simulated by the imaging device to obtain the polarization response output. The model predicted light intensity and the measured light intensity of the polarization response output are fitted to determine the defect geometric parameters.

[0060] In one embodiment of this application, the distribution matrix is ​​first analyzed to identify geomagnetic or geological anomaly areas as the areas to be detected. Polarization imaging data of the area is acquired using an imaging device. Then, the data is simulated using simulation software to obtain polarization response output. Finally, the model-predicted light intensity is fitted with the measured light intensity. The model parameters are adjusted by an optimization algorithm to make the prediction results consistent with the measured results, thereby determining the defect geometric parameters characterizing the geological anomaly.

[0061] In one embodiment of this application, the region to be detected is determined based on the distribution matrix. Polarization response output is obtained by simulating the polarization imaging data of the region to be detected acquired by the imaging device. The model-predicted light intensity and the measured light intensity of the polarization response output are fitted together to determine the defect geometric parameters, including:

[0062] The region to be detected is determined based on the distribution matrix, and polarization imaging data of the region to be detected is acquired by an imaging device.

[0063] Based on the polarization imaging data and preset model parameters, the polarization response output under different incident angles and wavelengths is determined;

[0064] The model-predicted light intensity output by the polarization response and the measured light intensity obtained by the polarization imager are fitted together to determine the defect geometric parameters.

[0065] In one embodiment of this application, a detection area is precisely defined on the surface of the object to be detected according to the distribution matrix, ensuring the targeting and efficiency of subsequent polarization imaging detection.

[0066] In one embodiment of this application, the region to be detected is determined based on a distribution matrix. For example, the engineering area is divided according to abnormal data in the distribution matrix to determine the region to be detected. Determining the region to be detected based on the distribution matrix narrows the detection range and improves the targeting and efficiency of the detection.

[0067] In one embodiment of this application, imaging data is acquired through an imaging device. For example, a six-band polarization imager is used to collect polarization imaging data of the detection area in the wavelength range of 400-1000nm to obtain polarization information under different wavelengths and incident angles, providing a rich data foundation for subsequent processing.

[0068] In one embodiment of this application, polarization response outputs under different incident angles and wavelengths are determined using polarization imaging data within the positioning area and pre-set model parameters. for:

[0069]

[0070] in, Indicates the angle of incidence The corresponding polarization state transformation rules, This represents the input polarization response. d This indicates the crack depth in polarization imaging data. This represents the wavelength of light in polarization imaging data. The relative permittivity of the material in the model parameters reflects the intensity of the interaction between electromagnetic waves and matter. The polarization response state calculated by the above process reflects the change in polarization state after the interaction between light and material, enabling the quantification of the differences in polarization response between the crack and the background at different wavelengths and angles through the model.

[0071] In one embodiment of this application, the measured light intensity is obtained by actual measurement using a polarization imager. Then, using a polarization scattering model, based on the current crack width... and depth d The estimated values ​​are used to determine the model predicted light intensity of the polarization response output at different wavelengths and incident angles. The difference between the measured light intensity and the model-predicted light intensity is calculated. Based on this difference, least squares fitting is performed to generate fitting parameters. for:

[0072]

[0073] in, Indicates the wavelength of light. Indicates the angle of incidence. Indicates the wavelength of light and angle of incidence The model predicts light intensity. Indicates the wavelength of light and angle of incidence The measured light intensity at that time Indicates the crack width. The fourth spatial derivative of the crack width is represented by... This represents the preset regularization coefficient.

[0074] The process involves iterative optimization based on the fitted parameters, aiming to minimize these parameters while continuously adjusting the crack width and depth. This yields the final defect geometry parameters, namely the optimized crack width and depth, demonstrating high accuracy and stability.

[0075] In addition, to facilitate user understanding and use of the detection results, the joint distribution function is visualized in the form of charts or images. This allows users to intuitively see the distribution of crack width and depth.

[0076] The above process simulates the polarization imaging data of the area to be detected acquired by the imaging device to obtain the polarization response output. Then, the model-predicted light intensity and the measured light intensity are fitted to accurately determine the geometric parameters of the defect. This achieves high-precision surface defect detection in the magnetic anomaly location area and improves the accuracy and reliability of the detection results.

[0077] S140, using the geomorphological data collected by radar to determine the variation characteristic parameters of the geological structure in the engineering area, and based on the variation characteristic parameters and the model parameters determined by the defect geometric parameters, to determine the local geomorphological index.

[0078] In one embodiment of this application, geomorphological data of the engineering area is collected using remote sensing equipment such as radar. Geological structural variation parameters, such as deformation rate and crack propagation degree, are extracted from this geomorphological data. Simultaneously, model parameters, such as mass index and singular spectrum intensity, are determined based on the defect geometric parameters generated in the preceding steps. Finally, these parameters are input into a multifractal spectrum risk assessment model to calculate a local geomorphological index, which is used to assess the geological structural stability of the engineering area.

[0079] In one embodiment of this application, the variation characteristic parameters of the geological structure in the engineering area are determined by geomorphological data collected by radar, and a local geomorphological index is determined based on the variation characteristic parameters and model parameters determined by the defect geometric parameters, including:

[0080] The terrain data of the area to be detected is acquired by radar, and the geological structure variation characteristic parameters of the area to be detected are determined in the constructed regional grid based on the terrain data.

[0081] The quality index and singular spectrum intensity of the landform model are determined based on the aforementioned defect geometric parameters.

[0082] Based on the aforementioned characteristic parameters, quality index, and singular spectrum intensity, the local geomorphic index corresponding to a local area is determined through index calculation.

[0083] In one embodiment of this application, geomorphological data of the area to be detected is acquired by radar, and a regional grid is constructed based on a preset grid scale. Within the regional grid, characteristic parameters of the geological structure variation of the area to be detected at different scales are determined. for:

[0084]

[0085] in, This represents the probability distribution of terrain data within the grid. r Indicates the grid scale used to construct the grid. q This represents the variable parameters. The calculation is performed using the sliding window technique. Understanding the spatiotemporal evolution patterns helps monitor changes in geological structures and promptly identify potential unstable areas.

[0086] In one embodiment of this application, after obtaining the defect geometric parameters, the mass index and singular spectrum intensity of the geomorphological model are determined based on the defect geometric parameters. The mass index and singular spectrum intensity reflect the density distribution characteristics and complexity of the fractal structure, respectively. Specifically, when the defect geometric parameters indicate a large number of cracks in the geological structure, a larger mass index is generated; when the defect geometric parameters indicate significant instability in the geological structure, such as larger crack widths or depths, a correspondingly larger singular spectrum intensity value is generated to indicate increased complexity of the geological structure, thereby more accurately assessing the stability of the geological structure.

[0087] In one embodiment of this application, after generating the variation characteristic parameters, the mass index, and the singular spectral intensity, the local geomorphic index corresponding to a local area is determined based on the above parameters through index calculation. for:

[0088]

[0089] in, q This represents a variable parameter with a value range from -2 to 2, used to adjust the weights for different fractal dimensions; express q Quality index under variables express q Singular spectral intensity under variables, express q The characteristic parameters of change under the variable, Represents the initial feature parameters. This indicates the preset model parameters.

[0090] The above process involves using radar to collect geomorphic data to determine the characteristic parameters of geological structure changes, and combining this with model parameters determined by defect geometric parameters to calculate a local geomorphic index. This comprehensively reflects the geological structure characteristics and stability of the engineering area. The local geomorphic index provides an important basis for risk assessment and helps to quantitatively evaluate the geological risks of the engineering area.

[0091] In one embodiment of this application, after determining the variation characteristic parameters of the geological structure in the engineering area using geomorphological data collected by radar, and determining the local geomorphological index based on the variation characteristic parameters and model parameters determined by the defect geometric parameters, the method further includes:

[0092] The local geomorphic index is compared with a preset first threshold in the risk assessment model to determine the risk level corresponding to the geomorphic index.

[0093] The stability level of the engineering area is determined by comparing the changed characteristic parameters with a preset second threshold in the risk assessment model.

[0094] An engineering geological assessment report is generated based on the risk level and the stability level.

[0095] Specifically, this embodiment employs a multifractal spectrum risk assessment model, comprehensively considering the complexity and nonlinear characteristics of the geological structure. The stability of the geological structure is quantitatively assessed using fractal theory, and the calculated local geomorphological index is used to evaluate its stability. Based on the characteristics of the geological structure and the assessment requirements, a first threshold is set in the risk assessment model. When the local geomorphological index exceeds the first threshold, it indicates poor geological structure stability and potential risk. The risk level corresponding to the current geomorphological index is determined and marked, such as low risk, medium risk, or high risk, providing a basis for subsequent risk management and response measures.

[0096] In addition, monitoring changing characteristic parameters The spatiotemporal evolution attribute, when At that time, a risk warning is triggered; among them, This represents the fractal dimension under steady-state conditions, which is the second threshold preset in the risk assessment model. It indicates a significant change in the stability of the geological structure, potentially leading to geological disasters such as landslides and subsidence. Based on the comparison results, the stability level of the engineering area is determined, such as stable, basically stable, or unstable, thereby enabling timely monitoring of the geological dynamics of the engineering area.

[0097] In addition, in one embodiment of this application, the generated risk assessment results and risk warning information are integrated to form a complete engineering geological assessment report. This report integrates key information such as risk level, stability level, and data, models, and thresholds used in the assessment process. The assessment report is then visualized using a geographic information system or other visualization tools, in the form of charts, images, etc. This helps users intuitively understand the stability of the geological structure and provides support for decision-making.

[0098] The above process, from acquiring and processing input data, to constructing a multifractal spectrum risk assessment model, then to calculating the generalized fractal dimension and analyzing its spatiotemporal evolution, and finally to risk assessment and early warning, as well as the generation and visualization of output results, provides detailed information about geological structural defects through defect geometric parameters, while local geomorphological indices quantify the overall impact of these defects on the stability of the geological structure. By comprehensively considering these two aspects of information, a quantitative assessment of the stability of the geological structure is achieved, improving the accuracy and reliability of the assessment results.

[0099] S150, Based on the local geomorphological index, determine the risk field distribution of the engineering area, and control the UAV to monitor the engineering area based on the risk field distribution.

[0100] In one embodiment of this application, based on the local geomorphological index assessment results, areas with different risk levels are divided, a risk field distribution map is generated, and then a drone monitoring route is planned according to the map to ensure that high-risk areas are monitored in a focused manner. Finally, the drone flight control system controls the drone to monitor the engineering area according to the planned route and receives monitoring data in real time so as to grasp the geological conditions of the engineering area in a timely manner.

[0101] In one embodiment of this application, determining the risk field distribution of the engineering area based on the local topographic index, and controlling a drone to monitor the engineering area based on the risk field distribution, includes:

[0102] Based on the local geomorphological index, the risk field distribution of the engineering area is generated;

[0103] Based on the risk field distribution, a flight route for the drone is generated;

[0104] The drone is controlled to monitor the engineering area based on the stated cruise route.

[0105] In one embodiment of this application, geomorphic data of the engineering area, including information such as terrain elevation, slope and surface roughness, is collected, and this data is processed and analyzed using a geographic information system to calculate a local geomorphic index. Finally, combined with the geological structure stability assessment results, a risk field distribution that intuitively displays the risk level of different areas is generated, providing a basis for subsequent UAV cruise route planning.

[0106] Based on the risk field distribution, the system generates the drone's cruise route, analyzes the risk field distribution, identifies high-risk and low-risk areas, and then plans a cruise route that covers all high-risk areas and, as far as possible, low-risk areas, based on the drone's performance parameters and monitoring requirements. Finally, the route is optimized to ensure that the drone completes the monitoring task efficiently and safely.

[0107] The drone is controlled by a cruise route to monitor the engineering area. The planned cruise route is imported into the drone flight control system, and monitoring parameters such as camera shooting frequency and sensor sampling frequency are set to ensure that the drone accurately acquires monitoring data. Finally, the drone is launched and monitors the engineering area according to the planned route. The drone's flight status and monitoring data are monitored in real time to ensure the smooth progress of the monitoring mission.

[0108] In one embodiment of this application, the method further includes: displaying the risk field distribution and the cruise route on the interface of the control platform, based on a set display method. Specifically, using front-end visualization technology, according to a set display format such as a two-dimensional map, three-dimensional model, or chart, the risk field distribution is rendered with different colors or transparency to show areas of different risk levels, and the cruise route is marked with lines or arrows and displays key node information. Simultaneously, the interface is ensured to support interactive operations such as zooming, panning, and rotating, so that users can intuitively view and analyze the risk status of the engineering area and the drone monitoring path. Displaying the risk field distribution and cruise route on the control platform interface provides an intuitive way to display monitoring information, helping monitoring personnel to grasp the monitoring progress and results in real time and adjust monitoring strategies accordingly.

[0109] The above process generates a risk field distribution for the engineering area based on local topographic indices, achieving spatial visualization of the risks in the engineering area and intuitively displaying the risk levels of different regions. By planning the drone's patrol route according to the risk field distribution and controlling the drone for monitoring, high-risk areas are given priority monitoring, improving the targeting and efficiency of monitoring and helping to promptly identify potential risks.

[0110] In this technical solution, geological data of the engineering area is acquired using sensing equipment mounted on a drone. Based on phase change data in the geological data, the partial derivatives of the geological anomaly field are determined. The gradient tensor formed by the partial derivatives is inverted to generate a distribution matrix characterizing the three-dimensional geological anomaly. The area to be detected is determined according to the distribution matrix. The polarization imaging data of the area to be detected acquired by the imaging equipment is simulated to obtain the polarization response output. The model predicted light intensity and the measured light intensity of the polarization response output are fitted to determine the defect geometric parameters. The change characteristic parameters of the geological structure in the engineering area are determined using geomorphological data collected by radar. Based on the change characteristic parameters and the model parameters determined by the defect geometric parameters, a local geomorphological index is determined. The risk field distribution of the engineering area is determined based on the local geomorphological index, and the drone is controlled to monitor the engineering area based on the risk field distribution. By efficiently collecting geological data by drones, combining geological anomaly analysis and defect geometric parameter determination, geological risks are accurately identified. By using radar geomorphological data and local geomorphological indices to assess risks, and finally controlling drone monitoring based on the risk field distribution, comprehensive and accurate risk management of the engineering area is achieved, effectively improving the efficiency and reliability of engineering safety monitoring.

[0111] The following describes embodiments of the monitoring device for linear engineering risk reduction based on UAV technology of this application, which can be used to execute the monitoring method for linear engineering risk reduction based on UAV technology in the above embodiments of this application. It is understood that the monitoring device for linear engineering risk reduction based on UAV technology can be a computer program (including program code) running on a computer device; for example, the monitoring device for linear engineering risk reduction based on UAV technology is an application software. The monitoring device for linear engineering risk reduction based on UAV technology can be used to execute the corresponding steps in the method provided in the embodiments of this application. For details not disclosed in the embodiments of the monitoring device for linear engineering risk reduction based on UAV technology of this application, please refer to the embodiments of the monitoring method for linear engineering risk reduction based on UAV technology described above in this application.

[0112] Figure 3 A block diagram of a monitoring device for linear engineering risk reduction based on unmanned aerial vehicle (UAV) technology according to an embodiment of this application is shown.

[0113] Reference Figure 3 As shown, a monitoring device for linear engineering risk reduction based on unmanned aerial vehicle (UAV) technology according to an embodiment of this application includes:

[0114] The acquisition unit 310 is used to acquire geological data of the engineering area through the sensing equipment carried by the UAV;

[0115] Anomaly unit 320 is used to determine the partial derivatives of the geological anomaly field based on the phase change data in the geological data, and to invert the gradient tensor formed by the partial derivatives to generate a distribution matrix characterizing the three-dimensional geological anomaly.

[0116] The parameter unit 330 is used to determine the area to be detected based on the distribution matrix, simulate the polarization imaging data of the area to be detected acquired by the imaging device to obtain the polarization response output, and perform fitting processing on the model predicted light intensity and the measured light intensity of the polarization response output to determine the defect geometric parameters.

[0117] Index unit 340 is used to determine the variation characteristic parameters of the geological structure in the engineering area through the geomorphological data collected by radar, and to determine the local geomorphological index based on the variation characteristic parameters and the model parameters determined by the defect geometric parameters.

[0118] The control unit 350 is used to determine the risk field distribution of the engineering area based on the local topographic index, and to control the UAV to monitor the engineering area based on the risk field distribution.

[0119] In this application, based on the aforementioned scheme, the step of acquiring geological data of the engineering area through the sensing device carried by the UAV includes: controlling the UAV to cruise along a flight path covering the engineering area, acquiring geological data of the engineering area through the sensing device carried by the UAV; performing noise filtering on the geological data to generate first data, aligning the coordinate system in the first data, and generating preprocessed geological data.

[0120] In this application, based on the aforementioned scheme, the step of determining the partial derivatives of the geological anomaly field based on the phase change data in the geological data, and inverting the gradient tensor formed by the partial derivatives to generate a distribution matrix characterizing the three-dimensional geological anomaly includes: determining the partial derivatives of the geological anomaly field based on the phase change data in the geological data; arranging the partial derivatives according to a set structure based on the correspondence between the partial derivatives and each element in the distribution matrix to generate a gradient tensor characterizing the localized anomaly; and inverting the geological data and the gradient tensor to generate a distribution matrix characterizing the three-dimensional geological anomaly.

[0121] In this application, based on the aforementioned scheme, determining the partial derivative of the geological anomaly field based on phase change data in geological data includes: determining the partial derivative of the geological anomaly field based on phase change data in geological data. for:

[0122]

[0123] in, Represents the distribution matrix B exist iComponents in direction j The partial derivatives; h Represents the quantum effect constant. This represents the phase change data caused by the deflection of the quantum spin direction. Represents the electron gyromagnetic constant. Indicates the sensing area, Indicates the sampling interval. Indicates the relevant time.

[0124] In this application, based on the aforementioned scheme, the step of determining the region to be detected according to the distribution matrix, simulating the polarization imaging data of the region to be detected acquired by the imaging device to obtain the polarization response output, and fitting the model-predicted light intensity and the measured light intensity of the polarization response output to determine the defect geometric parameters includes: determining the region to be detected according to the distribution matrix; acquiring the polarization imaging data of the region to be detected through the imaging device; determining the polarization response output under different incident angles and wavelengths based on the polarization imaging data and preset model parameters; and fitting the model-predicted light intensity of the polarization response output and the measured light intensity acquired by the polarization imager to determine the defect geometric parameters.

[0125] In this application, based on the aforementioned scheme, the step of determining the variation characteristic parameters of the geological structure in the engineering area using geomorphological data collected by radar, and determining the local geomorphological index based on the variation characteristic parameters and the model parameters determined by the defect geometric parameters, includes: acquiring geomorphological data of the area to be detected by radar; determining the variation characteristic parameters of the geological structure in the area to be detected in the constructed regional grid based on the geomorphological data; determining the quality index and singular spectrum intensity of the geomorphological model based on the defect geometric parameters; and determining the local geomorphological index corresponding to the local area through index calculation based on the variation characteristic parameters, the quality index, and the singular spectrum intensity.

[0126] In this application, based on the aforementioned scheme, after determining the geological structure variation characteristic parameters of the engineering area using geomorphological data collected by radar, and determining the local geomorphological index based on the variation characteristic parameters and the model parameters determined by the defect geometric parameters, the method further includes: comparing the local geomorphological index with a preset first threshold in the risk assessment model to determine the risk level corresponding to the geomorphological index; comparing the variation characteristic parameters with a preset second threshold in the risk assessment model to determine the stability level of the engineering area; and generating an engineering geological assessment report based on the risk level and the stability level.

[0127] In this application, based on the aforementioned scheme, after generating the engineering geological assessment report based on the risk level and the stability level, the method further includes: using visualization tools to visualize the engineering geological assessment report in the form of charts.

[0128] In this application, based on the aforementioned scheme, the step of determining the risk field distribution of the engineering area based on the local topographic index and controlling the UAV to monitor the engineering area based on the risk field distribution includes: generating the risk field distribution of the engineering area based on the local topographic index; planning and generating the UAV's cruise route based on the risk field distribution; and controlling the UAV to monitor the engineering area based on the cruise route.

[0129] In this application, based on the aforementioned scheme, after planning and generating the drone's cruise route according to the risk field distribution, the method further includes: displaying the risk field distribution and the cruise route on the interface of the control platform according to a set display method.

[0130] In this technical solution, geological data of the engineering area is acquired using sensing equipment mounted on a drone. Based on phase change data in the geological data, the partial derivatives of the geological anomaly field are determined. The gradient tensor formed by the partial derivatives is inverted to generate a distribution matrix characterizing the three-dimensional geological anomaly. The area to be detected is determined according to the distribution matrix. The polarization imaging data of the area to be detected acquired by the imaging equipment is simulated to obtain the polarization response output. The model predicted light intensity and the measured light intensity of the polarization response output are fitted to determine the defect geometric parameters. The change characteristic parameters of the geological structure in the engineering area are determined using geomorphological data collected by radar. Based on the change characteristic parameters and the model parameters determined by the defect geometric parameters, a local geomorphological index is determined. The risk field distribution of the engineering area is determined based on the local geomorphological index, and the drone is controlled to monitor the engineering area based on the risk field distribution. By efficiently collecting geological data by drones, combining geological anomaly analysis and defect geometric parameter determination, geological risks are accurately identified. By using radar geomorphological data and local geomorphological indices to assess risks, and finally controlling drone monitoring based on the risk field distribution, comprehensive and accurate risk management of the engineering area is achieved, effectively improving the efficiency and reliability of engineering safety monitoring.

[0131] Figure 4 A schematic diagram of the structure of a computer system suitable for implementing the electronic device of the present application is shown.

[0132] It should be noted that the computer system of the electronic device in this embodiment is only an example and should not impose any limitations on the function and scope of use of the embodiments of this application.

[0133] In this embodiment, the computer system includes a central processing unit 401, which can perform various appropriate actions and processes based on a program stored in a read-only memory 402 or a program loaded from a storage section 408 into a random access memory 403, such as executing the monitoring method for linear engineering risk reduction based on UAV technology described in the above embodiment. The random access memory 403 also stores various programs and data required for system operation. The central processing unit 401, the read-only memory 402, and the random access memory 403 are interconnected via a bus 404. An input / output interface 405 is also connected to the bus 404.

[0134] The following components are connected to the input / output interface 405: an input section 406 including a keyboard, mouse, etc.; an output section 407 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 408 including a hard disk, etc.; and a communication section 409 including a network interface card such as a LAN (Local Area Network) card, modem, etc. The communication section 409 performs communication processing via a network such as the Internet. A drive 410 is also connected to the input / output interface 405 as needed. A removable medium 411, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on the drive 410 as needed so that computer programs read from it can be installed into the storage section 408 as needed.

[0135] Specifically, according to embodiments of this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program including a computer program for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 409, and / or installed from removable medium 411. When the computer program is executed by central processing unit 401, it performs various functions defined in the system of this application.

[0136] It should be noted that the computer-readable medium shown in the embodiments of this application can be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. A computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, portable compact disc read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this application, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this application, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying a computer-readable computer program. The transmitted data signal can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. The computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The computer program contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to wireless, wired, etc., or any suitable combination thereof.

[0137] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. Each block in a flowchart or block diagram may represent a module, segment, or portion of code, which contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0138] The units described in the embodiments of this application can be implemented in software or hardware, and the described units can also be located in a processor. The names of these units do not necessarily limit the specific unit itself.

[0139] According to one aspect of this application, a computer program product or computer program is provided, comprising computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the methods provided in the various alternative implementations described above.

[0140] In another aspect, this application also provides a computer-readable medium, which may be included in the electronic device described in the above embodiments; or it may exist independently and not assembled into the electronic device. The computer-readable medium carries one or more programs, which, when executed by the electronic device, cause the electronic device to implement the monitoring method for linear engineering risk reduction based on UAV technology described in the above embodiments.

[0141] It should be noted that although several modules or units for the device used to perform actions have been mentioned in the detailed description above, this division is not mandatory. In fact, according to the embodiments of this application, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0142] Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of this application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, touch terminal, or network device, etc.) to execute the methods according to the embodiments of this application.

[0143] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the embodiments disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein.

[0144] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this application is limited only by the appended claims.

Claims

1. A monitoring method for linear engineering risk reduction based on unmanned aerial vehicle (UAV) technology, characterized in that, include: The geological data of the engineering area is acquired through sensing devices carried by the UAV; wherein, the sensing devices include a quantum magnetometer array, used to measure phase change data caused by quantum spin direction deflection; the geological data includes position and attitude information synchronously recorded by an inertial navigation unit and a positioning module, used to align the coordinate system with the phase change data; Based on the phase change data in the geological data, the partial derivatives of the geological anomaly field are determined, and the gradient tensor formed by the partial derivatives is inverted to generate a distribution matrix characterizing the three-dimensional geological anomaly. The region to be detected is determined based on the distribution matrix. The polarization imaging data of the region to be detected is simulated by the imaging device to obtain the polarization response output. The model predicted light intensity and the measured light intensity of the polarization response output are fitted to determine the defect geometric parameters. The geological structure variation parameters in the engineering area are determined by using radar-collected geomorphological data, and local geomorphological indices are determined based on the variation parameters and model parameters determined by the defect geometric parameters. Based on the local geomorphological index, the risk field distribution of the engineering area is determined, and the UAV is controlled to monitor the engineering area based on the risk field distribution.

2. The monitoring method for linear engineering risk reduction based on UAV technology according to claim 1, characterized in that, The drone will be equipped with sensors to acquire geological data of the engineering area, including: Control the drone to cruise along the flight path covering the engineering area, and acquire geological data of the engineering area through the sensing equipment carried by the drone; The geological data is subjected to noise filtering to generate first data. The coordinate system in the first data is aligned to generate preprocessed geological data.

3. The monitoring method for linear engineering risk reduction based on UAV technology according to claim 1, characterized in that, Based on the phase variation data in the geological data, the partial derivatives of the geological anomaly field are determined. The gradient tensor formed by these partial derivatives is then inverted to generate a distribution matrix characterizing the three-dimensional geological anomaly, including: Based on phase variation data in geological data, the partial derivatives of the geological anomaly field are determined; Based on the correspondence between the partial derivatives and the elements in the distribution matrix, the partial derivatives are arranged according to the set structure to generate a gradient tensor that characterizes the location anomaly. The geological data and the gradient tensor are inverted to generate a distribution matrix characterizing three-dimensional geological anomalies.

4. The monitoring method for linear engineering risk reduction based on UAV technology according to claim 3, characterized in that, Based on phase variation data in geological data, the partial derivatives of the geological anomaly field are determined, including: Based on phase variation data in geological data, the partial derivatives of the geological anomaly field are determined. for: in, Represents the distribution matrix B exist i Components in direction j The partial derivatives; h Represents the quantum effect constant. This represents the phase change data caused by the deflection of the quantum spin direction. Represents the electron gyromagnetic constant. Indicates the sensing area, Indicates the sampling interval. Indicates the relevant time.

5. The monitoring method for linear engineering risk reduction based on UAV technology according to claim 1, characterized in that, The region to be detected is determined based on the distribution matrix. Polarization response output is obtained by simulating the polarization imaging data of the region to be detected acquired by the imaging device. The model-predicted light intensity and the measured light intensity of the polarization response output are fitted to determine the defect geometric parameters, including: The region to be detected is determined based on the distribution matrix, and polarization imaging data of the region to be detected is acquired by an imaging device. Based on the polarization imaging data and preset model parameters, the polarization response output under different incident angles and wavelengths is determined; The model-predicted light intensity output by the polarization response and the measured light intensity obtained by the polarization imager are fitted together to determine the defect geometric parameters.

6. The monitoring method for linear engineering risk reduction based on UAV technology according to claim 1, characterized in that, The geological structure variation parameters in the engineering area are determined by using radar-collected geomorphological data. Based on these variation parameters and model parameters determined by the defect geometry parameters, local geomorphological indices are determined, including: The terrain data of the area to be detected is acquired by radar, and the geological structure variation characteristic parameters of the area to be detected are determined in the constructed regional grid based on the terrain data. The quality index and singular spectrum intensity of the landform model are determined based on the aforementioned defect geometric parameters. Based on the aforementioned characteristic parameters, quality index, and singular spectrum intensity, the local geomorphic index corresponding to a local area is determined through index calculation.

7. The monitoring method for linear engineering risk reduction based on UAV technology according to claim 6, characterized in that, After determining the geological structure variation parameters in the engineering area using radar-collected geomorphological data, and based on these variation parameters and model parameters determined through the defect geometry parameters, the local geomorphological index is determined. The process also includes: The local geomorphic index is compared with a preset first threshold in the risk assessment model to determine the risk level corresponding to the geomorphic index. The stability level of the engineering area is determined by comparing the changed characteristic parameters with a preset second threshold in the risk assessment model. An engineering geological assessment report is generated based on the risk level and the stability level.

8. The monitoring method for linear engineering risk reduction based on UAV technology according to claim 7, characterized in that, After generating the engineering geological assessment report based on the risk level and the stability level, it also includes: The engineering geological assessment report is visualized in the form of charts using visualization tools.

9. The monitoring method for linear engineering risk reduction based on UAV technology according to claim 1, characterized in that, Based on the local geomorphological index, the risk field distribution of the engineering area is determined, and the UAV is controlled to monitor the engineering area based on the risk field distribution, including: Based on the local geomorphological index, the risk field distribution of the engineering area is generated; Based on the risk field distribution, a flight route for the drone is generated; The drone is controlled to monitor the engineering area based on the stated cruise route.

10. The monitoring method for linear engineering risk reduction based on UAV technology according to claim 9, characterized in that, After planning and generating the drone's cruise route based on the aforementioned risk field distribution, the process also includes: The risk field distribution and the cruise route are displayed on the control platform interface according to the set display method.

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