Suspicious object monitoring method, system, equipment and medium based on tethered drone

Abstract

The suspicious object monitoring method, system, equipment and medium based on tethered drones relate to the field of drone control technology. The method includes: obtaining point cloud data of the target monitoring area collected by the tethered drone, and generating a three-dimensional map based on the point cloud data; determining the monitoring coverage area corresponding to multiple groups of different tether rope lengths and hovering heights under the preset obstacle avoidance safety distance conditions according to the obstacle position information in the three-dimensional map; determining the target monitoring coverage area with the largest coverage area, and using the corresponding tether rope length and hovering height as the target tethering parameters; dividing it into multiple monitoring sub-areas based on the target tethering parameters and the three-dimensional map, and determining the drone hovering position corresponding to each monitoring sub-area; when the position of the suspicious target is within any monitoring sub-area, controlling the tethered drone to move to the corresponding drone hovering position for monitoring. The implementation of the technical solution provided by the present application achieves the effect of improving the efficiency of suspicious object monitoring.

Description

Technical Field

[0001] The present application relates to the field of drone control technology, and in particular to a suspicious object monitoring method, system, equipment and medium based on a tethered drone. Background Art

[0002] With the rapid development of intelligent security technology, drones have been widely used in the field of security monitoring due to their advantages of flexibility and wide field of vision. Especially in the security monitoring of large venues, industrial parks and other areas, tethered drones have become an important monitoring method due to their long-lasting power supply and controllable position.

[0003] At present, the existing suspicious object monitoring methods based on tethered drones usually adopt the method of hovering at a fixed position or cruising along a preset path to monitor suspicious objects. However, in practical applications, since there are often a variety of complexly distributed obstacles in the area to be monitored, when a suspicious target appears in the area and needs to be monitored, only using a fixed method for monitoring is often easily interfered by obstacles, thereby reducing the efficiency of suspicious object monitoring. Summary of the invention

[0004] The present application provides a suspicious object monitoring method, system, electronic device and storage medium based on a tethered drone, which can improve the efficiency of suspicious object monitoring.

[0005] In a first aspect, the present application provides a suspicious object monitoring method based on a tethered drone, comprising:

[0006] Acquire point cloud data of the target monitoring area collected by the tethered drone, and generate a three-dimensional map of the target monitoring area based on the point cloud data;

[0007] Determine, based on the obstacle position information in the three-dimensional map, monitoring coverage areas corresponding to a plurality of different tether rope lengths and hovering heights under a preset obstacle avoidance safety distance condition;

[0008] Determine the target monitoring coverage area with the largest coverage area in each of the monitoring coverage areas, and use the mooring rope length and hovering height corresponding to the target monitoring coverage area as target mooring parameters;

[0009] Based on the target tethering parameters and the three-dimensional map, the target monitoring coverage area is divided into a plurality of monitoring sub-areas, and the hovering position of the drone corresponding to each monitoring sub-area is determined;

[0010] The position of the suspicious target is obtained. When the position of the suspicious target is located in any of the monitoring sub-areas, the tethered UAV is controlled to move to the corresponding UAV hovering position, and the suspicious target is monitored.

[0011] In a second aspect of the present application, a suspicious object monitoring system based on a tethered drone is provided, the system comprising:

[0012] A data acquisition module, used to acquire point cloud data of a target monitoring area collected by a tethered UAV, and generate a three-dimensional map of the target monitoring area based on the point cloud data;

[0013] An area determination module, used to determine the monitoring coverage areas corresponding to a plurality of groups of different tether rope lengths and hovering heights under a preset obstacle avoidance safety distance condition according to the obstacle position information in the three-dimensional map;

[0014] A hovering position determination module is used to determine the target monitoring coverage area with the largest coverage area in each of the monitoring coverage areas, and use the mooring rope length and hovering height corresponding to the target monitoring coverage area as target mooring parameters; based on the target mooring parameters and the three-dimensional map, the target monitoring coverage area is divided into multiple monitoring sub-areas, and the drone hovering position corresponding to each of the monitoring sub-areas is determined;

[0015] The suspicious target monitoring module is used to obtain the position of the suspicious target. When the position of the suspicious target is within any of the monitoring sub-areas, the tethered drone is controlled to move to the corresponding drone hovering position and monitor the suspicious target.

[0016] In a third aspect of the present application, an electronic device is provided, comprising a memory, a processor, and a program stored in the memory and executable on the processor, wherein the program can implement a suspicious object monitoring method based on a tethered drone when loaded and executed by the processor.

[0017] In a fourth aspect of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the processor implements a suspicious object monitoring method based on a tethered drone.

[0018] In summary, one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0019] By adopting the above technical solution, the point cloud data of the target monitoring area collected by the tethered drone is obtained, and an accurate three-dimensional map is generated based on the point cloud data, which fully reflects the terrain and obstacle distribution in the monitoring area. By using the obstacle position information in the three-dimensional map, under the preset obstacle avoidance safety distance condition, multiple groups of different tether rope lengths and hovering heights are evaluated and determined, thereby correspondingly generating multiple monitoring coverage areas. By comparing the coverage area of ​​each monitoring coverage area, the target monitoring coverage area with the largest coverage area can be intelligently selected, and its corresponding tether rope length and hovering height are used as the optimized target tethering parameters. Further, based on the target tethering parameters and the three-dimensional map, the target monitoring coverage area is subdivided into multiple monitoring sub-areas, and the optimal hovering position of the drone in each sub-area is accurately determined. This detailed division not only improves the accuracy of monitoring, but also enhances the system's adaptability to complex environments. Finally, by obtaining the location information of the suspicious target in real time, and automatically controlling the tethered drone to move to the corresponding hovering position for monitoring when the target is located in any monitoring sub-area, efficient and timely monitoring of the suspicious target is achieved. This technical solution significantly improves the efficiency of tethered UAVs in monitoring suspicious objects in complex environments by dynamically optimizing the monitoring coverage area and hovering position and combining three-dimensional environmental perception with an intelligent response mechanism. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 It is a flow chart of a suspicious object monitoring method based on a tethered drone provided in an embodiment of the present application;

[0021] Figure 2 It is a structural schematic diagram of a suspicious object monitoring system based on a tethered drone provided in an embodiment of the present application;

[0022] Figure 3 It is a structural schematic diagram of an electronic device provided in an embodiment of the present application.

[0023] Description of reference numerals: 300, electronic device; 301, processor; 302, communication bus; 303, user interface; 304, network interface; 305, memory. DETAILED DESCRIPTION

[0024] In order to enable technicians in this field to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments.

[0025] In the description of the embodiments of the present application, words such as "for example" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "for example" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "for example" or "for example" is intended to present related concepts in a specific way.

[0026] In the description of the embodiments of the present application, the meaning of the term "multiple" refers to two or more. For example, multiple systems refer to two or more systems, and multiple screen terminals refer to two or more screen terminals. In addition, the terms "first" and "second" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance or implicitly indicating the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. The terms "include", "comprise", "have" and their variations all mean "including but not limited to", unless otherwise specifically emphasized.

[0027] The present application embodiment provides a suspicious object monitoring method based on a tethered drone. In one embodiment, please refer to Figure 1 , Figure 1 The flowchart of the suspicious object monitoring method based on a tethered drone provided in the embodiment of the present application can be implemented by a computer program, which can be integrated into an application or run as an independent tool application. The method can also be implemented by a single-chip microcomputer or run on a suspicious object monitoring system based on a tethered drone based on a von Neumann system. Specifically, the method can include the following steps:

[0028] Step 101: Obtain point cloud data of the target monitoring area collected by the tethered drone, and generate a three-dimensional map of the target monitoring area based on the point cloud data.

[0029] The target monitoring area refers to a specific spatial range where suspicious objects need to be monitored, usually an area within a certain range around the tethered point of the tethered drone. This area can be a large venue, industrial park, or other area that requires security monitoring, and its range is limited by the tethered parameters of the tethered drone, such as the length of the tethered rope and the hovering height.

[0030] Point cloud data refers to the spatial data set obtained after the laser radar sensor carried by the tethered drone scans the target monitoring area. Each data point contains spatial position information (X, Y, Z coordinates) and reflection intensity information. These data points together constitute a three-dimensional point set that can characterize environmental characteristics such as terrain undulations, obstacle shapes, and spatial distribution in the target monitoring area.

[0031] A three-dimensional map is a digital map model formed by processing point cloud data, dividing the target monitoring area into grid units, and classifying these grid units into ground units and obstacle units. The three-dimensional map contains ground elevation information and obstacle spatial distribution information, where ground units represent passable areas and obstacle units represent areas that need to be avoided. This three-dimensional map model provides basic spatial information support for subsequent tasks such as determining tethering parameters, dividing monitoring sub-areas, and planning monitoring paths.

[0032] Specifically, in order to accurately grasp the environmental information of the target monitoring area, the tethered drone is first controlled to fly above the target monitoring area, and the point cloud data of the target monitoring area is collected by the laser radar sensor carried. The point cloud data contains a large amount of spatial coordinate point information, each coordinate point has corresponding three-dimensional spatial position information and reflection intensity information, and can more completely characterize the environmental characteristics such as topography and obstacle distribution in the target monitoring area. In order to facilitate the processing and analysis of the collected point cloud data, the target monitoring area is divided into several grid units with preset grid specifications. The grid specifications can be set according to the actual monitoring needs, for example, it can be set to 1 meter × 1 meter. Subsequently, the average height value of the data points in each grid unit is calculated based on the point cloud data, and each grid unit is divided into a ground unit and an obstacle unit by setting a height threshold. Specifically, when the average height value of the data points in the grid unit is lower than the preset height threshold, the grid unit is judged as a ground unit; when the average height value of the data points in the grid unit is higher than the preset height threshold, the grid unit is judged as an obstacle unit. After completing the classification of the grid units, the ground unit and the obstacle unit are combined to generate a three-dimensional map of the target monitoring area. The 3D map not only contains ground elevation information, but also identifies the spatial distribution of obstacles, providing an important reference for determining the monitoring parameters of the tethered drone and planning the monitoring path. The 3D map constructed in this way has the characteristics of accurate data and complete information, which can help the system accurately identify the distribution of obstacles in the monitoring area, thus laying the foundation for safe and efficient monitoring of suspicious objects.

[0033] Based on the above embodiment, as an optional embodiment, in step 101: generating a three-dimensional map of the target monitoring area according to the point cloud data, this step may also include the following steps:

[0034] Step 201 divides the target monitoring area into a plurality of grid units of preset grid specifications; based on the point cloud data, calculates the average height value of the data points in each grid unit.

[0035] Specifically, in order to achieve efficient processing and analysis of point cloud data, it is necessary to first divide the target monitoring area into grid units of uniform specifications. In specific implementation, a suitable grid size can be selected, such as a square grid of 1 meter × 1 meter or 0.5 meter × 0.5 meter, to divide the entire target monitoring area into a regular grid array. After the grid division is completed, each data point in the point cloud data is mapped to the corresponding grid cell according to its plane coordinates (X, Y). For each grid cell, the Z coordinate values ​​of all data points in the cell are extracted, and the arithmetic mean is calculated as the average height value of the grid cell. This grid processing method not only simplifies the data structure, but also can effectively filter the noise in the point cloud data, providing a reliable elevation information basis for subsequent ground and obstacle identification.

[0036] Step 202: Divide each grid unit into ground units and obstacle units according to the average height value of the data points in each grid unit.

[0037] Specifically, in order to distinguish between ground areas and obstacle areas, it is necessary to analyze and judge the average height value of each grid unit. First, a suitable height threshold is determined through statistical analysis, which can be set based on the terrain characteristics of the target monitoring area and the actual monitoring needs. When the average height value of the grid unit is less than the threshold, it is marked as a ground unit; when the average height value is greater than or equal to the threshold, it is marked as an obstacle unit. In practical applications, in order to improve the accuracy of classification, the height difference of adjacent grid cells can be combined for auxiliary judgment. For example, when the height difference between a grid cell and the surrounding ground cells exceeds a preset value, it is judged as an obstacle unit. This classification method based on height threshold can effectively identify obstacles such as buildings and trees in the monitoring area, and provide accurate obstacle distribution information for subsequent monitoring planning.

[0038] Step 203: Combine the ground units and obstacle units to generate a three-dimensional map of the target monitoring area.

[0039] Specifically, in order to construct a complete three-dimensional map, it is necessary to organically combine the classified ground units and obstacle units. In the specific implementation, a three-dimensional coordinate system is first established, and the center point coordinates and height information of each grid unit are mapped to the coordinate system. For the ground unit, its actual elevation value is retained; for the obstacle unit, in addition to retaining the height information, its obstacle attributes need to be marked. The area between the grid units is filled by the spatial interpolation algorithm to generate continuous ground undulations and obstacle contours. Finally, all the information is integrated to form a complete three-dimensional map data structure, which contains not only spatial geometric information but also unit attribute information. This three-dimensional map construction method can truly reflect the spatial characteristics of the target monitoring area and provide comprehensive environmental information support for the system to plan the monitoring path of the drone and determine the optimal monitoring position.

[0040] Step 102: According to the obstacle position information in the three-dimensional map, determine the monitoring coverage areas corresponding to multiple groups of different tether rope lengths and hovering heights under the preset obstacle avoidance safety distance condition.

[0041] Among them, obstacle location information refers to the spatial location data marked as obstacle units in the three-dimensional map, including the plane coordinates (X, Y) and height value (Z) of each obstacle unit. This information describes the spatial distribution characteristics of all obstacles (such as buildings, trees, equipment, etc.) in the target monitoring area, which is used for subsequent obstacle avoidance analysis and monitoring area planning.

[0042] The preset obstacle avoidance safety distance condition refers to the minimum safety distance requirement that a tethered drone needs to maintain from obstacles when performing monitoring tasks. This safety distance is a threshold parameter determined based on factors such as the physical size, flight characteristics, and safety margin of the tethered drone.

[0043] The tether length refers to the actual length of the tether rope of the tethered drone, which determines the spatial range in which the tethered drone can move. The tether length is an adjustable parameter, and its value needs to meet the monitoring requirements while considering the performance limitations of the tethered drone.

[0044] The hovering height refers to the vertical distance of the tethered drone relative to the ground. It is an important working parameter for tethered drones when performing monitoring tasks, and directly affects the monitoring viewing angle and coverage. The choice of hovering height needs to comprehensively consider factors such as obstacle avoidance requirements, monitoring effect, and tether length.

[0045] The monitoring coverage area refers to the ground area that the tethered drone can actually monitor under a specific combination of tether rope length and hovering height. This area is determined by the field of view of the tethered drone's monitoring equipment (such as a camera), the hovering position, and the occlusion effect of obstacles. The calculation of the monitoring coverage area needs to take into account the spatial distribution of obstacles. Determining the actual visible ground area through algorithms such as ray tracing is an important indicator for evaluating the monitoring effect.

[0046] Specifically, after completing the construction of the three-dimensional map of the target monitoring area, in order to ensure the safety and monitoring effect of the tethered drone during the monitoring process, it is necessary to comprehensively consider the impact of obstacle distribution and tethering parameters on the monitoring coverage area. First, the spatial coordinate information of the obstacle unit is extracted from the three-dimensional map, including the plane position and height information of each obstacle unit, and this information is integrated to form an obstacle position distribution map for subsequent safety distance analysis and monitoring area planning.

[0047] When considering the obstacle avoidance safety requirements, an obstacle safety envelope is constructed with each obstacle unit as the center according to the preset obstacle avoidance safety distance. The safety envelope actually forms a three-dimensional safety buffer zone to ensure that the tethered drone maintains a sufficient safety distance from the obstacle. The preset obstacle avoidance safety distance can be set according to the size, flight characteristics and actual monitoring requirements of the tethered drone, for example, it can be set to 3 meters or 5 meters. This method of constructing a safety envelope can effectively prevent the tethered drone from colliding with obstacles during the monitoring process. In order to determine the appropriate tethering parameters, the maximum tethering rope length is first set according to the performance parameters of the tethered drone, and multiple candidate tethering rope length values ​​are generated according to the preset rope length interval (for example, 5 meters). For each candidate tethering rope length, combined with the constructed obstacle safety envelope, the possible safe hovering height range of the drone is calculated. Specifically, by analyzing the spatial relationship between the spherical surface determined by the tethering rope length and the obstacle safety envelope, the height interval where the collision may occur is excluded, and the safe hovering height range of the drone is obtained.

[0048] After determining the safe hovering height range, samples are taken within the range at preset height intervals (e.g., 2 meters) to obtain multiple specific hovering height values. For each combination of tether length and hovering height, the corresponding monitoring coverage area is calculated based on the reference viewing angle range of the tethered drone (e.g., the camera's field of view). During the calculation process, the occlusion effect of obstacles needs to be considered, and the actual visible ground area range is determined through algorithms such as ray tracing.

[0049] Based on the above embodiment, as an optional embodiment, in step 102: determining the monitoring coverage areas corresponding to multiple groups of different tether rope lengths and hovering heights under the preset obstacle avoidance safety distance condition according to the obstacle position information in the three-dimensional map, this step may also include the following steps:

[0050] Step 301: extracting the spatial coordinates of each obstacle unit in the obstacle position information, and establishing an obstacle position distribution map based on the spatial coordinates of each obstacle unit.

[0051] Specifically, in order to accurately express the spatial distribution of obstacles, it is necessary to extract the spatial information of all obstacle units from the three-dimensional map. In the specific implementation, first traverse all grid units in the three-dimensional map, filter out the grids marked as obstacle units, and record the center point coordinates (X, Y) and height value (Z) of each obstacle unit. Subsequently, these spatial coordinate information are integrated into a unified three-dimensional coordinate system to construct an obstacle position distribution map. This distribution map not only contains the planar distribution information of the obstacles, but also records their height information, providing complete spatial reference data for subsequent obstacle avoidance analysis.

[0052] Step 302: In the obstacle position distribution map, with each obstacle as the center, an obstacle safety envelope is constructed according to a preset obstacle avoidance safety distance.

[0053] Specifically, in order to ensure that the tethered drone maintains a safe distance from obstacles, it is necessary to construct a safety envelope based on the obstacle location distribution map. In specific implementation, the center point of each obstacle unit is used as a reference, and the preset obstacle avoidance safety distance (for example, 3 meters) is expanded outward to form a three-dimensional safety buffer area. For adjacent obstacle units, their safety envelopes may overlap. At this time, the overlapping areas need to be merged to form a continuous safety envelope. This method of constructing a safety envelope can effectively visualize the dangerous area and provide intuitive spatial constraints for the subsequent determination of safe monitoring positions.

[0054] Step 303: Generate multiple candidate mooring rope lengths according to the preset maximum mooring rope length and the preset rope length interval; determine the safe hovering height range of the drone corresponding to each candidate mooring rope length in the obstacle safety envelope.

[0055] Specifically, in order to determine the appropriate combination of tethered parameters, it is necessary to first generate a series of feasible tethered rope length values. In specific implementation, the maximum tethered rope length (for example, 50 meters) is set based on the performance parameters of the tethered drone, and sampling is performed at equal intervals according to the preset rope length interval (for example, 5 meters) to generate a sequence of candidate tethered rope lengths. For each candidate tethered rope length, the possible safe hovering height range is determined by analyzing the spatial relationship between the sphere with the tethered point as the center and the tethered rope length as the radius and the obstacle safety envelope. This process is actually to solve the height interval where the sphere and the safety envelope do not intersect, which needs to be solved by numerical calculation methods. This systematic parameter generation method ensures that all possible safe tethered parameter combinations can be taken into account.

[0056] Step 304: within the safe hovering height range of each UAV, a plurality of hovering heights are sampled at preset height intervals, and a monitoring coverage area is determined based on each hovering height and a reference viewing angle range of the tethered UAV.

[0057] Specifically, in order to obtain a specific monitoring plan, each safe hovering height range needs to be refined. During the specific implementation, each safe height range is sampled according to the preset height interval (for example, 2 meters) to obtain a series of specific hovering height values. For each hovering height, the theoretical monitoring coverage range is calculated based on the benchmark viewing angle range of the monitoring equipment carried by the tethered drone (for example, the field of view angle of the camera). During the calculation process, it is necessary to consider the occlusion effect of obstacles, and analyze the visibility from the hovering position to the ground through a ray tracing algorithm to determine the actual monitoring coverage area. This refined coverage area calculation method can accurately evaluate the effects of different monitoring positions and provide a reliable basis for the subsequent selection of optimal monitoring parameters.

[0058] Step 103: Determine the target monitoring coverage area with the largest coverage area in each monitoring coverage area, and use the mooring rope length and hovering height corresponding to the target monitoring coverage area as target mooring parameters.

[0059] The target monitoring coverage area refers to the area with the largest actual coverage area among all possible monitoring coverage area solutions. Specifically, it is the maximum effective monitoring range that can be achieved through a specific combination of tether length and hovering height while meeting the preset obstacle avoidance safety distance.

[0060] The target mooring parameters refer to the parameter combination of the mooring rope length and the hovering height corresponding to the target monitoring coverage area. This set of parameters is the optimal parameter value that can achieve the maximum monitoring coverage area among multiple sets of candidate parameters.

[0061] Specifically, in order to maximize the monitoring efficiency, it is necessary to select the optimal tethering parameter combination from these candidate solutions. In the specific implementation, the area of ​​each monitoring coverage area is first calculated, and the number of visible ground units is counted by the grid counting method, and the actual coverage area value of each monitoring coverage area is obtained by combining the actual area of ​​the grid units. In the calculation process, it is necessary to accurately identify and exclude the areas blocked by obstacles, and only calculate the effective area that can actually be monitored. By comparing and analyzing the area values ​​of all monitoring coverage areas, the area with the largest coverage area is selected as the target monitoring coverage area, and the corresponding tethering rope length and hovering height combination of the area is determined as the target tethering parameter. This parameter optimization method based on maximizing the coverage area not only ensures the maximum monitoring range under the premise of meeting safety requirements, but also provides a clear basis for parameter selection, so that the system can quickly determine the optimal working state. At the same time, since the target tethering parameter is obtained after comprehensively considering multiple factors such as obstacle distribution, safety distance and monitoring effect, it has strong practicality and reliability, and can guide the tethered drone to take the optimal working position during the actual monitoring process.

[0062] Step 104: Based on the target tethering parameters and the three-dimensional map, the target monitoring coverage area is divided into a plurality of monitoring sub-areas, and the drone hovering position corresponding to each monitoring sub-area is determined.

[0063] Among them, the monitoring sub-area refers to the smaller area unit obtained by dividing the target monitoring coverage area according to specific rules. Each monitoring sub-area is an independent spatial range that needs to be monitored by the system. Its size and shape are determined based on multiple factors, including the field of view characteristics of the monitoring equipment carried by the tethered drone, the expected monitoring accuracy requirements, and the distribution of obstacles.

[0064] The drone hovering position refers to the optimal spatial position point that the tethered drone needs to maintain when monitoring each sub-area, expressed in three-dimensional coordinates (X, Y, Z). This position point is a spatial point that can achieve the best monitoring effect for a specific monitoring sub-area through calculation under the constraints of the target tethering parameters (determined tethering rope length and hovering height). Each monitoring sub-area corresponds to a dedicated drone hovering position, which needs to meet the tethering rope length constraint, safety obstacle avoidance requirements, and ensure effective monitoring coverage of the sub-area.

[0065] Specifically, in order to improve the accuracy and efficiency of monitoring, it is necessary to plan and organize the determined target monitoring coverage area more carefully. After determining the target tethering parameters, the target monitoring coverage area is first divided into sub-areas based on the obstacle distribution characteristics in the three-dimensional map. In the specific implementation, the regional segmentation algorithm is used to divide the target monitoring coverage area into multiple monitoring sub-areas of appropriate size, taking into account the spatial distribution characteristics of obstacles, the field of view characteristics of the monitoring equipment, and the expected monitoring accuracy requirements. In the division process, it is necessary to ensure that the area and shape of each sub-area are suitable for efficient monitoring by tethered drones, and at the same time, it is necessary to consider the appropriate overlap between adjacent sub-areas to avoid monitoring blind spots. Subsequently, for each monitoring sub-area, the optimal drone hovering position is calculated based on the target tethering parameters (i.e., the determined tethering rope length and hovering height). When calculating the hovering position, it is necessary to comprehensively consider the geometric center of the sub-area, the distribution of obstacles, and the constraints of the tethering rope length, and determine the hovering point coordinates that can achieve the best monitoring effect for the sub-area through the optimization algorithm. This sub-area-based refined monitoring planning method not only improves the systematicness and integrity of monitoring, but also provides clear working path guidance for tethered drones to ensure the orderly progress of the monitoring process. In this way, the system can achieve efficient coverage of the target area while ensuring the quality of monitoring, providing a spatial reference basis for the subsequent monitoring task execution.

[0066] Based on the above embodiment, as an optional embodiment, in step 104: based on the target mooring parameters and the three-dimensional map, the target monitoring coverage area is divided into multiple monitoring sub-areas. This step may also include the following steps:

[0067] Step 401: Calculate the activity range of the tethered UAV based on the tethered rope length and hovering height determined by the target tethering parameters.

[0068] Specifically, in order to determine the actual movable space of the tethered UAV, it is necessary to calculate the activity range based on the optimized target tethering parameters. In specific implementation, a spherical space range is drawn with the tethering point as the center of the sphere and the tethering rope length determined in the target tethering parameters as the radius; then, based on the determined hovering height, it intersects with the spherical space to obtain a circular horizontal activity plane. This plane is the theoretical activity range of the tethered UAV at a specific hovering height. This activity range calculation method based on geometric constraints can clearly define the spatial activity boundary of the tethered UAV under given parameters, providing basic constraints for subsequent monitoring planning.

[0069] Step 402: Within the activity range, determine the effective monitoring area that the tethered drone can reach based on the obstacle distribution in the three-dimensional map.

[0070] Specifically, to ensure the feasibility of the monitoring plan, it is necessary to identify the areas that can actually be used for monitoring within the theoretical activity range. In the specific implementation, the obtained activity range is first overlaid with the obstacle distribution information in the three-dimensional map for analysis, and the areas occupied or blocked by obstacles are excluded. At the same time, the preset obstacle avoidance safety distance requirements are considered, and the areas that do not meet the safety requirements are further eliminated. Through this spatial analysis, the range of areas where the tethered drone can actually perform effective monitoring is finally determined. This method of determining the effective monitoring area not only ensures the safety of the monitoring plan, but also improves the accuracy and practicality of subsequent monitoring planning.

[0071] Step 403: Divide the effective monitoring area into multiple monitoring sub-areas according to a preset area overlap ratio.

[0072] Specifically, in order to achieve systematic coverage of the effective monitoring area, reasonable sub-area division is required. In specific implementation, the ideal coverage range of a single monitoring is first calculated according to the characteristics of the monitoring equipment carried by the tethered drone (such as the camera field of view), and then the overlapping band width between adjacent sub-areas is determined according to the preset area overlap rate (for example, 20%). Based on these parameters, a specific area division algorithm (such as grid division or sector division) is used to divide the effective monitoring area into multiple monitoring sub-areas of appropriate size. In the division process, priority is given to reducing the number of sub-areas while ensuring that each sub-area can be effectively covered by the tethered drone. This sub-area division method that takes the overlap rate into consideration not only ensures the continuity and integrity of monitoring, but also avoids the waste of resources caused by repeated monitoring, making the entire monitoring process more efficient.

[0073] Based on the above embodiment, as an optional embodiment, in step 104: determining the hovering position of the drone corresponding to each monitoring sub-area, this step may also include the following steps:

[0074] Step 404: Obtain the geometric center of each monitoring sub-area; for each monitoring sub-area, construct a fan-shaped search space with the geometric center as the starting point, and generate multiple grid search points with equal spacing in the fan-shaped search space.

[0075] Specifically, in order to determine the optimal hovering position for each monitoring sub-area, it is necessary to first establish a suitable position search mechanism. In specific implementation, the system first obtains the geometric center coordinates of each monitoring sub-area by calculation and uses it as the initial reference point for the search. Subsequently, taking the geometric center as the starting point, a fan-shaped search space is constructed in combination with the activity characteristics of the tethered drone and the field of view of the monitoring equipment. In this fan-shaped search space, evenly distributed grid search points are generated at a predetermined spacing (such as 1 meter). This grid search method based on the geometric center not only ensures the rationality of the search range, but also provides sufficient candidate positions for subsequent optimization selection.

[0076] Step 405: Obtain the monitoring coverage and obstacle avoidance degree of the tethered UAV at each grid search point; perform weighted calculations on the monitoring coverage and obstacle avoidance degree corresponding to each grid search point to obtain a corresponding monitoring effect evaluation value.

[0077] Specifically, in order to comprehensively evaluate the monitoring effect of each candidate hovering position, a scientific evaluation index system needs to be established. In specific implementation, for each grid search point, the coverage degree of the tethered drone on the monitoring sub-area at this point is first calculated, that is, the monitoring coverage. This indicator reflects the area ratio of the sub-area that can be effectively monitored from this position; at the same time, the distance relationship between this position and the surrounding obstacles is calculated, that is, the obstacle avoidance degree. This indicator reflects the safety level of the hovering position. Subsequently, these two indicators are multiplied by the corresponding weight coefficients (such as monitoring coverage weight 0.6, obstacle avoidance weight 0.4), and the comprehensive monitoring effect evaluation value of the grid search point is obtained by weighted summation. This multi-index weighted evaluation method can comprehensively consider the monitoring effect and safety requirements, and provide an objective basis for selecting the optimal hovering position.

[0078] Step 406: The grid search point with the highest monitoring effect evaluation value is used as the hovering position of the UAV in the corresponding monitoring sub-area.

[0079] Specifically, in order to determine the final drone hovering position for each monitoring sub-area, it is necessary to optimize based on the aforementioned evaluation results. During specific implementation, the system compares and analyzes the monitoring effect evaluation values ​​of all grid search points in the monitoring sub-area, selects the grid search point with the highest evaluation value, and determines its three-dimensional coordinates as the drone hovering position corresponding to the monitoring sub-area. This method based on evaluation value optimization ensures that the selected hovering position can achieve the best monitoring effect while ensuring safety, and provides an accurate spatial positioning reference for subsequent actual monitoring tasks. The hovering position determined in this way not only meets the monitoring needs of the system, but also ensures the safety and efficiency of drone operations.

[0080] Step 105: Obtain the position of the suspicious target. When the position of the suspicious target is within any monitoring sub-area, control the tethered UAV to move to the corresponding UAV hovering position and monitor the suspicious target.

[0081] Among them, the location of the suspicious target refers to the real-time location coordinate information of the target that needs to be focused on and monitored in three-dimensional space. This location information can be obtained in a variety of ways, such as detection data from ground sensor networks, target recognition results from surrounding monitoring equipment, or target positioning information provided by other collaborative monitoring systems. The location of the suspicious target is usually expressed in the form of three-dimensional coordinates (X, Y, Z) to accurately describe the spatial location of the suspicious target in the monitoring area.

[0082] Specifically, in order to achieve timely and effective monitoring of suspicious targets, the system needs to establish a set of fast-response target monitoring mechanisms. In practical applications, the system first obtains the real-time position coordinates of the suspicious target through a sensor network or other detection equipment, and then determines the spatial position relationship between the position information and the divided monitoring sub-areas to determine the specific monitoring sub-areas where the suspicious target is located. When the system determines that the suspicious target is located in a certain monitoring sub-area, it immediately calls the pre-planned drone hovering position coordinates of the sub-area, and controls the tethered drone to move to the hovering position according to the optimal path. In the process of controlling the movement of the drone, the system needs to consider the tether rope length constraints and obstacle avoidance requirements in real time to ensure the safety of the flight process. When the tethered drone reaches the designated hovering position, the suspicious target is accurately monitored by the monitoring equipment carried (such as high-definition cameras, infrared sensors, etc.). This fast-response monitoring method based on a preset hovering position not only greatly reduces the response time of the system and avoids the delay caused by real-time calculation of the optimal monitoring position, but also makes full use of the monitoring position optimized in the early stage to ensure the reliability of the monitoring effect. In this way, the system can quickly locate and continuously track suspicious targets while ensuring the quality of monitoring, providing strong support for the timely discovery and handling of potential threats.

[0083] Based on the above embodiment, as an optional embodiment, in step 105: obtaining the suspicious target location, after this step, the following steps may also be included:

[0084] Step 107: When the suspicious target is located in the obstacle-blocked area in the three-dimensional map, a local coordinate system is established with the suspicious target position as the center.

[0085] Specifically, in order to handle the situation where a suspicious target is blocked by an obstacle, the system needs to establish an observation space with the target as the core. In specific implementation, when the system detects that a suspicious target is located in an area blocked by an obstacle, a local coordinate system is established with the real-time position coordinates of the suspicious target as the origin. This target-centered local coordinate system enables the system to more intuitively analyze the spatial relationship around the target, providing a convenient spatial reference framework for subsequent determination of the optimal observation direction.

[0086] Step 108: In the local coordinate system, multiple observation directions that avoid occlusion are determined based on the spatial position of the obstacle; and multiple temporary monitoring candidate positions are generated by combining the target tethering parameters and the observation direction.

[0087] Specifically, in order to find a suitable drone monitoring position, it is necessary to comprehensively analyze the spatial environment around the target. In specific implementation, the system first analyzes the positional relationship between obstacles and suspicious targets in the established local coordinate system based on the spatial distribution information of obstacles in the three-dimensional map, and determines several potential observation directions that can avoid obstacle obstruction (for example, sampling a direction every 30 degrees on the horizontal plane). Then, for each observation direction, the system combines the target tethering parameters (including tethering rope length and hovering height) to calculate the feasible monitoring space that meets the tethering constraints, and generates multiple temporary monitoring candidate positions in these spaces. This candidate position generation method based on directional sampling ensures that the system can fully consider all possible observation angles and provides a wealth of alternatives for selecting the optimal monitoring position.

[0088] Step 109: Select a position that meets the preset obstacle avoidance requirements and has the largest visible range for suspicious targets from the candidate temporary monitoring positions as the temporary monitoring position, and control the tethered drone to move to the temporary monitoring position for supplementary monitoring.

[0089] Specifically, in order to achieve effective monitoring of obstructed targets, it is necessary to select the optimal temporary monitoring position from the generated candidate positions. In specific implementation, the system first verifies the feasibility of each temporary monitoring candidate position to check whether it meets the preset obstacle avoidance requirements (such as the minimum safe distance from obstacles). For candidate positions that meet the obstacle avoidance requirements, the visible range of the suspicious target from this position is further calculated, that is, the target exposure area that can be observed from this position. The system selects the position with the largest visible range as the final temporary monitoring position, and controls the tethered drone to move to this position through a safe path for supplementary monitoring. This position selection method based on visible range optimization not only ensures the safety of the monitoring position, but also maximizes the observation effect of the obstructed target and improves the system's adaptability to special situations. Through this dynamically adjusted monitoring strategy, the system can effectively cope with complex situations where the target is obscured and ensure the continuity and integrity of the monitoring task.

[0090] Reference Figure 2 , a suspicious object monitoring system based on a tethered drone is provided in an embodiment of the present application, the system comprises: a data acquisition module, an area determination module, a hovering position determination module, and a suspicious target monitoring module, wherein:

[0091] A data acquisition module is used to acquire point cloud data of the target monitoring area collected by the tethered UAV, and generate a three-dimensional map of the target monitoring area based on the point cloud data;

[0092] An area determination module is used to determine the monitoring coverage area corresponding to multiple groups of different tether rope lengths and hovering heights under a preset obstacle avoidance safety distance condition based on the obstacle location information in the three-dimensional map;

[0093] The hovering position determination module is used to determine the target monitoring coverage area with the largest coverage area in each monitoring coverage area, and use the mooring rope length and hovering height corresponding to the target monitoring coverage area as the target mooring parameters; based on the target mooring parameters and the three-dimensional map, the target monitoring coverage area is divided into multiple monitoring sub-areas, and the hovering position of the drone corresponding to each monitoring sub-area is determined;

[0094] The suspicious target monitoring module is used to obtain the position of the suspicious target. When the position of the suspicious target is within any monitoring sub-area, the tethered drone is controlled to move to the corresponding drone hovering position and monitor the suspicious target.

[0095] Based on the above embodiment, the data acquisition module is also used to divide the target monitoring area into a number of grid units with preset grid specifications; based on the point cloud data, calculate the average height value of the data points in each grid unit; according to the average height value of the data points in each grid unit, divide each grid unit into ground units and obstacle units; combine the ground units and obstacle units to generate a three-dimensional map of the target monitoring area.

[0096] On the basis of the above embodiment, the area determination module is further used to extract the spatial coordinates of each obstacle unit in the obstacle position information, and establish an obstacle position distribution map based on the spatial coordinates of each obstacle unit; in the obstacle position distribution map, with each obstacle as the center, an obstacle safety envelope surface is constructed according to a preset obstacle avoidance safety distance; according to a preset maximum tethering rope length and a preset rope length interval, a plurality of candidate tethering rope lengths are generated; in the obstacle safety envelope surface, a safe hovering height range of the UAV corresponding to each candidate tethering rope length is determined; within the safe hovering height range of each UAV, a plurality of hovering heights are sampled at preset height intervals, and a monitoring coverage area is determined based on each hovering height and a reference viewing angle range of the tethered UAV.

[0097] On the basis of the above embodiment, the hovering position determination module is also used to calculate the activity range of the tethered UAV based on the tethering rope length and the hovering height determined by the target tethering parameters; within the activity range, the effective monitoring area that the tethered UAV can reach is determined according to the obstacle distribution in the three-dimensional map; and the effective monitoring area is divided into multiple monitoring sub-areas according to a preset area overlap rate.

[0098] Based on the above embodiments, the hovering position determination module is also used to obtain the geometric center of each monitoring sub-area; for each monitoring sub-area, a fan-shaped search space is constructed with the geometric center as the starting point, and a plurality of equally spaced grid search points are generated in the fan-shaped search space; the monitoring coverage and obstacle avoidance degree corresponding to the tethered UAV at each grid search point are obtained; the monitoring coverage and obstacle avoidance degree corresponding to each grid search point are weightedly calculated respectively to obtain the corresponding monitoring effect evaluation value; the grid search point with the highest monitoring effect evaluation value is used as the hovering position of the UAV in the corresponding monitoring sub-area.

[0099] Based on the above embodiment, the suspicious target monitoring module is also used to obtain image data collected by the tethered drone; identify the image data based on a preset suspicious behavior template library to determine the target area where the suspicious target exists; combine the position and direction parameters of the tethered drone to calculate the spatial projection position of the target area, and use the spatial projection position as the suspicious target position.

[0100] On the basis of the above embodiment, the suspicious target monitoring module is also used to establish a local coordinate system with the suspicious target position as the center when the suspicious target is located in the obstacle obstruction area in the three-dimensional map; in the local coordinate system, multiple observation directions that avoid obstruction are determined based on the spatial position of the obstacle; multiple temporary monitoring candidate positions are generated in combination with the target tethering parameters and the observation direction; from the temporary monitoring candidate positions, a position that meets the preset obstacle avoidance requirements and has the largest visible range for the suspicious target is selected as the temporary monitoring position, and the tethered drone is controlled to move to the temporary monitoring position for supplementary monitoring.

[0101] It should be noted that: when the device provided in the above embodiment realizes its function, only the division of the above functional modules is used as an example. In actual application, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the device and method embodiments provided in the above embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0102] The present application also discloses an electronic device. Figure 3 , Figure 3 The electronic device 300 may include: at least one processor 301 , at least one network interface 304 , a user interface 303 , a memory 305 , and at least one communication bus 302 .

[0103] The communication bus 302 is used to realize the connection and communication between these components.

[0104] The user interface 303 may include a display interface and a camera interface. Optionally, the user interface 303 may also include a standard wired interface and a wireless interface.

[0105] The network interface 304 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0106] Among them, the processor 301 may include one or more processing cores. The processor 301 uses various interfaces and lines to connect various parts in the entire server, and executes various functions of the server and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory 305, and calling data stored in the memory 305. Optionally, the processor 301 can be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), and programmable logic array (Programmable Logic Array, PLA). The processor 301 can integrate one or a combination of a central processing unit (Central Processing Unit, CPU), a graphics processing unit (Graphics Processing Unit, GPU) and a modem. Among them, the CPU mainly processes the operating system, user interface diagrams and applications, etc.; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 301, and it can be implemented separately through a chip.

[0107] Among them, the memory 305 may include a random access memory (Random Access Memory, RAM) and may also include a read-only memory (Read-Only Memory). Optionally, the memory 305 includes a non-transitory computer-readable storage medium. The memory 305 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 305 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 305 may optionally also be at least one storage device located away from the aforementioned processor 301. Refer to Figure 3 , the memory 305 as a computer storage medium may include an operating system, a network communication module, a user interface module, and an application program for a suspicious object monitoring method based on a tethered drone.

[0108] exist Figure 3 In the electronic device 300 shown, the user interface 303 is mainly used to provide an input interface for the user and obtain the data input by the user; and the processor 301 can be used to call the application program stored in the memory 305 for a suspicious object monitoring method based on a tethered drone. When executed by one or more processors 301, the electronic device 300 executes one or more methods in the above-mentioned embodiments. It should be noted that for the aforementioned method embodiments, for the sake of simple description, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited to the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required for the present application.

[0109] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0110] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0111] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0112] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0113] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a memory and includes several instructions for a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the various embodiments of the present application. The aforementioned memory includes: various media that can store program codes, such as USB flash drives, mobile hard drives, magnetic disks or optical disks.

[0114] The above are only exemplary embodiments of the present disclosure and cannot be used to limit the scope of the present disclosure. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the disclosure of the specification and practice, those skilled in the art will easily think of other embodiments of the present disclosure.

[0115] This application is intended to cover any variation, use or adaptation of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the art not recorded in the present disclosure. The description and examples are to be regarded as exemplary only.

Claims

1. A suspicious object monitoring method based on a tethered drone, characterized in that: include: Acquire point cloud data of the target monitoring area collected by the tethered drone, and generate a three-dimensional map of the target monitoring area based on the point cloud data; Determine, based on the obstacle position information in the three-dimensional map, monitoring coverage areas corresponding to a plurality of different tether rope lengths and hovering heights under a preset obstacle avoidance safety distance condition; Determine the target monitoring coverage area with the largest coverage area in each of the monitoring coverage areas, and use the mooring rope length and hovering height corresponding to the target monitoring coverage area as target mooring parameters; Based on the target tethering parameters and the three-dimensional map, the target monitoring coverage area is divided into a plurality of monitoring sub-areas, and the hovering position of the drone corresponding to each monitoring sub-area is determined; The position of the suspicious target is obtained. When the position of the suspicious target is located in any of the monitoring sub-areas, the tethered UAV is controlled to move to the corresponding UAV hovering position, and the suspicious target is monitored.

2. The suspicious object monitoring method based on a tethered drone according to claim 1 is characterized in that: The step of generating a three-dimensional map of the target monitoring area according to the point cloud data comprises: Dividing the target monitoring area into a plurality of grid units of preset grid specifications; Based on the point cloud data, calculating the average height value of the data points in each of the grid cells; Dividing each of the grid cells into ground cells and obstacle cells according to an average height value of the data points in each of the grid cells; The ground unit and the obstacle unit are combined to generate a three-dimensional map of the target monitoring area.

3. The suspicious object monitoring method based on a tethered drone according to claim 1 is characterized in that: The determining, based on the obstacle position information in the three-dimensional map, monitoring coverage areas corresponding to a plurality of different tether rope lengths and hovering heights under a preset obstacle avoidance safety distance condition includes: Extracting the spatial coordinates of each obstacle unit in the obstacle position information, and establishing an obstacle position distribution map based on the spatial coordinates of each obstacle unit; In the obstacle position distribution map, taking each obstacle as the center, an obstacle safety envelope is constructed according to a preset obstacle avoidance safety distance; Generate multiple candidate mooring rope lengths according to a preset maximum mooring rope length and a preset rope length interval; Determining the safe hovering height range of the UAV corresponding to each of the candidate tethering rope lengths in the obstacle safety envelope; Within the safe hovering height range of each drone, multiple hovering heights are sampled at preset height intervals, and a monitoring coverage area is determined based on each hovering height and a reference viewing angle range of the tethered drone.

4. The suspicious object monitoring method based on a tethered drone according to claim 1 is characterized in that: The target monitoring coverage area is divided into a plurality of monitoring sub-areas based on the target mooring parameters and the three-dimensional map, including: Calculating the activity range of the tethered drone based on the tethered rope length and hovering height determined by the target tethered parameters; Within the activity range, determining an effective monitoring area that the tethered drone can reach based on the obstacle distribution in the three-dimensional map; According to a preset area overlap rate, the effective monitoring area is divided into a plurality of monitoring sub-areas.

5. The suspicious object monitoring method based on a tethered drone according to claim 1 is characterized in that: The determining the hovering position of the drone corresponding to each monitoring sub-area includes: Obtaining the geometric center of each monitoring sub-area; For each monitoring sub-area, construct a fan-shaped search space with the geometric center as the starting point, and generate a plurality of grid search points with equal spacing in the fan-shaped search space; Obtaining the monitoring coverage and obstacle avoidance degree of the tethered UAV corresponding to each of the grid search points; Performing weighted calculations on the monitoring coverage and obstacle avoidance degree corresponding to each of the grid search points to obtain a corresponding monitoring effect evaluation value; The grid search point with the highest monitoring effect evaluation value is used as the hovering position of the UAV in the corresponding monitoring sub-area.

6. The suspicious object monitoring method based on a tethered drone according to claim 1 is characterized in that: The obtaining of the location of the suspicious target includes: Acquiring image data collected by the tethered drone; Identify the image data based on a preset suspicious behavior template library to determine a target area where a suspicious target exists; The spatial projection position of the target area is calculated in combination with the position and direction parameters of the tethered UAV, and the spatial projection position is used as the suspicious target position.

7. The suspicious object monitoring method based on a tethered drone according to claim 1 is characterized in that: After obtaining the location of the suspicious target, the method further includes: When the suspicious target is located in an obstacle-occluded area in the three-dimensional map, a local coordinate system is established with the suspicious target position as the center; In the local coordinate system, determining a plurality of observation directions that avoid occlusion based on the spatial position of the obstacle; generating a plurality of temporary monitoring candidate positions in combination with the target mooring parameters and the observation direction; A position that meets the preset obstacle avoidance requirements and has the largest visible range for the suspicious target is selected from the temporary monitoring candidate positions as the temporary monitoring position, and the tethered drone is controlled to move to the temporary monitoring position for supplementary monitoring.

8. A suspicious object monitoring system based on a tethered drone, characterized in that: The system comprises: A data acquisition module, used to acquire point cloud data of a target monitoring area collected by a tethered UAV, and generate a three-dimensional map of the target monitoring area based on the point cloud data; An area determination module, used to determine the monitoring coverage areas corresponding to a plurality of groups of different tether rope lengths and hovering heights under a preset obstacle avoidance safety distance condition according to the obstacle position information in the three-dimensional map; A hovering position determination module is used to determine the target monitoring coverage area with the largest coverage area in each of the monitoring coverage areas, and use the mooring rope length and hovering height corresponding to the target monitoring coverage area as target mooring parameters; based on the target mooring parameters and the three-dimensional map, the target monitoring coverage area is divided into multiple monitoring sub-areas, and the drone hovering position corresponding to each of the monitoring sub-areas is determined; The suspicious target monitoring module is used to obtain the position of the suspicious target. When the position of the suspicious target is within any of the monitoring sub-areas, the tethered drone is controlled to move to the corresponding drone hovering position and monitor the suspicious target.

9. An electronic device, characterized in that: It includes a processor, a memory, a user interface and a network interface, the memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device executes the suspicious object monitoring method based on a tethered drone as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores instructions, and when the instructions are executed, the suspicious object monitoring method based on a tethered drone as described in any one of claims 1 to 7 is executed.

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