Unmanned aerial vehicle modeling and video monitoring fusion method, device, equipment and medium

Abstract

The invention relates to the technical field of intelligent monitoring, and relates to an unmanned aerial vehicle modeling and video monitoring fusion method and device, equipment and a medium. The method comprises the following steps: collecting information of existing and planned monitoring points in a target monitoring area; wherein the information of the monitoring point location comprises hanging height, longitude and latitude, ideal inclination angle, direction angle and the like; arranging an unmanned aerial vehicle flight task according to a monitoring demand, and collecting three-dimensional live-action data of a target area; performing information verification and geometric positioning on the three-dimensional live-action data, and establishing a simulation model; wherein the information verification comprises verification of machine parameters, longitudes and latitudes, altitudes and rotation angles; the simulation model and a visual range algorithm are combined for simulation, and a coverage effect picture is generated; wherein the longitude, latitude, hanging height and the like of a monitoring point location are considered in simulation; outputting and displaying a layout list of the monitoring point locations according to the coverage effect picture; wherein the layout list comprises longitude and latitude, hanging height, horizontal and vertical direction angles and an actual inclination angle.

Description

Technical Field

[0001] The present invention relates to the field of intelligent monitoring technology, and in particular to a method, device, equipment and medium for integrating unmanned aerial vehicle modeling and video monitoring. Background Art

[0002] The national standard G 37300-2018 "Specifications for the Collection of Video and Image Information in Key Public Security Areas" defines in detail the collection locations in key public areas and key industries involving public areas, and stipulates the basic requirements for collection. Video and image information must meet the needs of target identification, cover the monitoring range, take into account large-scale macro observation and fixed monitoring of specific target areas, combine time-sharing scanning with real-time tracking of specific targets, ensure seamless connection when multiple cameras monitor in different areas, and form effective coverage of the entire monitoring area. In addition, for the video and image information of target identification, it is clearly stipulated that if the physical features of a person need to be identified, the vertical pixel number of the target shall not be less than 200. The layout of video and image acquisition equipment in public areas needs to comprehensively consider the actual scene characteristics, perception needs, such as background environment, vehicles, personnel, behavior, events, etc., as well as factors such as equipment options and security levels, and formulate targeted deployment plans.

[0003] However, with the in-depth expansion of video surveillance construction and application, problems such as insufficient overall planning, irregular networking and sharing, and lack of management mechanisms have become increasingly apparent.

[0004] In the overall planning stage of video surveillance systems, scientifically and accurately evaluating the construction coverage effect in complex scenarios has become a key issue that needs to be solved. For a long time, due to the lack of effective tools and methods, the point planning and optimization of large-scale video surveillance projects such as smart parks, smart communities, safe cities, and the "Xueliang Project" often rely too much on subjective experience and judgment, and cannot accurately quantify the distribution of blind spots and the potential for point optimization. Summary of the invention

[0005] The present invention provides a method, device, equipment and medium for integrating drone modeling and video surveillance. The method relies on drone oblique photography to collect three-dimensional real-scene data of complex scenes to perform high-precision three-dimensional modeling. On the basis of modeling, the configuration parameters of existing and planned monitoring equipment are input to perform three-dimensional simulation, and a coverage effect diagram based on spatial visibility after simulation planning is generated. The method mainly focuses on high-density coverage scenes such as complex traffic arteries, key safety protection areas, and important protection units. Such scenes are basically within the low-altitude airspace with a vertical height of less than 300 meters. Drone aerial photography modeling is used to achieve accurate restoration of complex on-site environments, so that planners can intuitively preview the coverage effect after dense point deployment.

[0006] The present invention provides a method for integrating drone modeling and video monitoring, comprising:

[0007] Collect information on existing and planned monitoring points in the target monitoring area; the information on monitoring points includes hanging height, longitude and latitude, ideal tilt angle, direction angle, field of view angle, focal length, and sensor size;

[0008] Arrange UAV flight missions according to monitoring needs and collect 3D real-life data of the target area; UAV flight missions include flight range, route planning, and flight parameter settings;

[0009] The three-dimensional real scene data is verified and geometrically positioned to establish a simulation model; the information verification includes the verification of machine parameters, longitude and latitude, altitude and rotation angle;

[0010] The simulation model is combined with the visual range algorithm to generate a coverage effect diagram; the simulation takes into account the latitude and longitude, hanging height, actual tilt angle, horizontal and vertical viewing angles, and minimum and maximum visual distances of the monitoring point;

[0011] Output and display the layout list of monitoring points according to the coverage effect diagram; the layout list includes longitude and latitude, hanging height, horizontal and vertical direction angles and actual tilt angle.

[0012] Preferably, arranging the UAV flight mission according to the monitoring requirements and collecting the three-dimensional real scene data of the target area includes:

[0013] Using drones equipped with high-resolution cameras and combining them with the precise positioning information provided by the Beidou satellite system, we implement multi-angle oblique photography to collect three-dimensional real-scene data of the target area.

[0014] Preferably, the flight parameters include: flight altitude, heading overlap rate, lateral overlap rate, inclination and flight speed.

[0015] Preferably, the step of performing information verification and geometric positioning on the three-dimensional real scene data and establishing a simulation model comprises:

[0016] The 3D real-scene data is classified by region and the posture information is verified. The 3D real-scene data is geometrically positioned using aerial triangulation. Control points are added according to accuracy requirements to establish a simulation model.

[0017] Preferably, the simulation model is combined with the visual range algorithm to generate a coverage effect diagram, which includes:

[0018] Integrate the simulation model with the calculation results of the visibility range algorithm to perform simulation, including the visibility range parameters of existing and planned monitoring points;

[0019] The visual range algorithm is specifically as follows: based on the focal length of the camera, target surface size, target surface pixel density, actual tilt angle, monitoring target size, physical characteristics of the monitoring point hanging height, and geographical location information of the monitoring point's latitude and longitude, field of view angle, and azimuth angle, the visual range of the existing site is accurately calculated.

[0020] Preferably, the outputting and displaying the layout list of monitoring points according to the coverage effect diagram includes:

[0021] After the simulation of the coverage effect diagram is completed, a detailed list of monitoring point layouts is output, listing the key parameters of longitude and latitude, hanging height, horizontal and vertical angles, and actual tilt angles, along with equipment configuration recommendations and installation instructions.

[0022] Preferably, a drone modeling and video surveillance fusion device comprises:

[0023] The monitoring point collection module is used to collect information about existing and planned monitoring points in the target monitoring area; the monitoring point information includes hanging height, longitude and latitude, ideal tilt angle, direction angle, field of view angle, focal length, and sensor size;

[0024] The UAV acquisition module is used to arrange UAV flight missions according to monitoring requirements and collect 3D real-life data of the target area; UAV flight missions include flight range, route planning, and flight parameter settings;

[0025] The simulation model building module is used to perform information verification and geometric positioning on the three-dimensional real scene data to establish a simulation model; wherein the information verification includes verification of machine parameters, longitude and latitude, altitude and rotation angle;

[0026] The simulation operation module is used to combine the simulation model with the visual range algorithm to generate a coverage effect diagram; the simulation takes into account the latitude and longitude, hanging height, actual tilt angle, horizontal and vertical viewing angles, and minimum and maximum visual distances of the monitoring point;

[0027] The results application module is used to output and display the layout list of monitoring points based on the coverage effect diagram; the layout list includes longitude and latitude, hanging height, horizontal and vertical direction angles and actual tilt angle.

[0028] Preferably, a device comprises a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor implements the method for fusion of drone modeling and video surveillance when executing the computer program.

[0029] Preferably, a computer-readable storage medium includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the method for fusion of drone modeling and video surveillance.

[0030] Compared with the prior art, the present invention has the following beneficial effects:

[0031] The present invention discloses a method, device, equipment and medium for integrating drone modeling and video surveillance. The method relies on drone oblique photography to collect three-dimensional real-scene data of complex scenes to perform high-precision three-dimensional modeling. On the basis of modeling, the configuration parameters of existing and planned monitoring equipment are input to perform three-dimensional simulation, and a coverage effect diagram based on spatial visibility after simulation planning is generated. The method mainly focuses on high-density coverage scenes such as complex traffic arteries, key safety protection areas, and important protection units. Such scenes are basically within the low-altitude airspace with a vertical height of less than 1,000 meters. Drone aerial photography modeling is used to achieve accurate restoration of complex on-site environments, so that planners can intuitively preview the coverage effect after dense point deployment. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 It is a flowchart of a method for integrating drone modeling and video surveillance provided by an embodiment of the present invention;

[0033] Figure 2 It is a module schematic diagram of a drone modeling and video monitoring fusion device provided by an embodiment of the present invention;

[0034] Figure 3 is a schematic diagram of a high-precision target area photo provided by an embodiment of the present invention;

[0035] Figure 4 It is the intention of the high-precision three-dimensional model of the target research area provided by the embodiment of the present invention;

[0036] Figure 5 is a schematic diagram of a sight range calculation tool provided by an embodiment of the present invention;

[0037] Figure 6 It is a schematic diagram of a simulation of a three-dimensional coverage effect of a current point provided by an embodiment of the present invention;

[0038] Figure 7 It is a schematic diagram of a simulation of a three-dimensional coverage effect of a planning point provided by an embodiment of the present invention;

[0039] Figure 8 It is a schematic diagram of the coverage effect of the monitoring network provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0040] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0041] See also Figure 1 , a method for integrating drone modeling and video surveillance, comprising:

[0042] S1: Collect information on existing and planned monitoring points in the target monitoring area; the information on the monitoring points includes hanging height, longitude and latitude, ideal tilt angle, direction angle, field of view angle, focal length, and sensor size;

[0043] S2: Arrange UAV flight missions according to monitoring requirements and collect 3D real-life data of the target area; UAV flight missions include flight range, route planning, and flight parameter settings;

[0044] S3: Perform information verification and geometric positioning on the three-dimensional real scene data to establish a simulation model; wherein the information verification includes verification of machine parameters, longitude and latitude, altitude and rotation angle;

[0045] S4: Combining the simulation model with the visual range algorithm to perform simulation and generate a coverage effect diagram; the simulation takes into account the latitude and longitude, hanging height, actual tilt angle, horizontal and vertical viewing angles, and minimum and maximum visual distances of the monitoring point;

[0046] S5: Output and display a layout list of monitoring points according to the coverage effect diagram; wherein the layout list includes longitude and latitude, hanging height, horizontal and vertical direction angles and actual tilt angle.

[0047] In step S1, priority is given to comprehensively sorting out information on existing and planned monitoring points in the target monitoring area, including collecting key parameters such as hanging height, longitude and latitude, ideal tilt angle, azimuth angle, field of view angle, focal length, sensor size, etc. At the same time, the equipment configuration of new points is pre-set, and the sensor specifications, focal length, field of view angle and other factors are clarified to provide accurate input data for subsequent modeling and simulation.

[0048] Based on the physical characteristics of the camera such as focal length and viewing angle, and the installation environment such as height and obstructions, the monitoring coverage effect can be accurately simulated. Through visual analysis, the coverage of existing points can be intuitively displayed and quantitatively analyzed to find blind spots and redundancies, providing a basis for the scientific site selection of new points. The system supports users to interactively adjust camera parameters and provide real-time feedback on monitoring coverage effects, which improves the flexibility and accuracy of planning.

[0049] Preferably, in step S2, arranging the UAV flight mission according to the monitoring requirements and collecting the three-dimensional real scene data of the target area includes:

[0050] Using drones equipped with high-resolution cameras and combining them with the precise positioning information provided by the Beidou satellite system, we implement multi-angle oblique photography to collect three-dimensional real-scene data of the target area.

[0051] Preferably, the flight parameters include: flight altitude, heading overlap rate, lateral overlap rate, inclination and flight speed.

[0052] In the above scheme, in-depth analysis and flight planning of the geographical environment are carried out, topographic map information is integrated, the flight range of the drone is determined according to the monitoring needs, and external factors such as drone type and flight rules are comprehensively considered to formulate flight mission arrangements, including drone preparation, route planning and flight parameter settings, such as flight altitude, heading overlap rate, side overlap rate, inclination, flight speed, etc., to ensure the integrity and accuracy of data collection. In strict compliance with aviation management regulations, drones are equipped with high-resolution cameras, combined with the precise positioning information provided by the Beidou satellite system, and multi-angle oblique photography is implemented to collect high-precision three-dimensional real-life data of the target area.

[0053] Preferably, in step S3, the three-dimensional real scene data is subjected to information verification and geometric positioning to establish a simulation model, including:

[0054] The 3D real-scene data is classified by region and the posture information is verified. The 3D real-scene data is geometrically positioned using aerial triangulation. Control points are added according to accuracy requirements to establish a simulation model.

[0055] In the above scheme, after the three-dimensional real-scene data is initially sorted, strict posture information verification is carried out according to regional classification, including camera parameters, longitude and latitude, altitude, rotation angle, etc., to ensure the accuracy of the data; the photo data is geometrically positioned using aerial triangulation technology, and control points are selectively added to enhance the accuracy of the model according to the model accuracy requirements.

[0056] During the model building process, the actual project needs and hardware resource limitations are taken into consideration, the coordinate system and model size are reasonably selected, and 3D reconstruction is started. The output format is usually general formats such as OBJ and OSGB to facilitate subsequent processing and compatibility. After the modeling is completed, strict model preview and quality review are carried out to ensure that the model faithfully reflects the on-site environment and lay a solid foundation for subsequent simulation.

[0057] Preferably, in step S4, the simulation model is combined with the visual range algorithm to generate a coverage effect diagram, which includes:

[0058] Integrate the simulation model with the calculation results of the visibility range algorithm to perform simulation, including the visibility range parameters of existing and planned monitoring points;

[0059] The visual range algorithm is specifically as follows: based on the focal length of the camera, target surface size, target surface pixel density, actual tilt angle, monitoring target size, physical characteristics of the monitoring point hanging height, and geographical location information of the monitoring point's latitude and longitude, field of view angle, and azimuth angle, the visual range of the existing site is accurately calculated.

[0060] In the above scheme, the visible range algorithm is further extended to calculate the ideal hanging height and visible range of the newly built monitoring points, providing accurate data input for the simulation; in the simulation process, the constructed simulation model is integrated with the calculation results of the visible range algorithm, including the visible range parameters of existing and planned monitoring points; the simulation fully considers the multi-dimensional parameters of the monitoring points, such as longitude and latitude, hanging height, actual tilt angle, horizontal and vertical viewing angle, minimum and maximum visible distance, etc., to generate a coverage effect diagram, clearly showing visible, invisible areas and overlapping monitoring areas; by batch importing and processing point layer data, fast comparison of different planning schemes is achieved, which is convenient for dynamic adjustment and optimization.

[0061] In one embodiment of the present application, in addition to the three-dimensional model, the visual domain simulation under the 3D view involves the following seven parameters, namely, direction angle, tilt, offset, horizontal angle, vertical angle, minimum range, and maximum range. The specific parameter descriptions are shown in the following table:

[0062]

[0063] The interactive viewshed simulation tool is designed to achieve efficient real-time interactive analysis in a 3D view. The tool uses the observation point as the viewpoint of the virtual camera and uses a rasterization-based method to determine the visibility of each element in the scene. The calculation results are then projected back to the original 3D view in a color-coded manner, intuitively showing the visibility status of each area. The specific analyses involved include: viewshed analysis, occlusion analysis, and precise visual range analysis;

[0064] View analysis is the core of the three-dimensional visual field simulation of video surveillance. It is based on the principle of geometric optics and calculates the spatial area covered by the line of sight from the surveillance camera position without being blocked by any object. View calculation involves algorithms such as projective geometry, spatial geometric transformation, and ray-object intersection detection in linear algebra. In the three-dimensional model, one or more groups of rays are emitted to represent the line of sight of the camera, and their intersection points with the scene model are determined to determine the boundary of the visual field.

[0065] Occlusion refers to the phenomenon that an object blocks the line of sight. Occlusion analysis aims to identify which objects block the line of sight of the target area and the degree of occlusion. This is usually achieved by calculating the intersection of the line of sight with the surface of each object in the scene model and determining the distance relationship between the intersection and the starting point of the line of sight. Occlusion analysis helps to identify monitoring blind spots and guide the optimal layout of monitoring points.

[0066] The precise visual range analysis algorithm model based on the physical parameters of the camera (focal length, aperture, field of view, lens resolution, pixel size, working distance, field of view, depth of field, optical magnification, etc.) and functional requirements (motor vehicle license plate recognition, motor vehicle basic feature recognition, non-motor vehicle license plate recognition, non-motor vehicle rider face recognition, pedestrian face recognition, pedestrian body recognition) is as follows:

[0067] Analysis of the relationship between monitoring distance and focal length:

[0068] When the installation height H1 and the pitch angle A are constant, the actual measured distance L from the collection object is:

[0069]

[0070] If the measured distance L and pitch angle A from the monitored object remain constant, the installation height H1 is specifically:

[0071] H1=H2+L*tanA-200

[0072] A——The pitch angle between the optical axis and the horizontal line, in degrees (°);

[0073] L——The measured distance from the monitored object, in millimeters (mm);

[0074] H1——installation height, in millimeters (mm);

[0075] H2——average height, in millimeters (mm). This application uses the standard value of 1700mm.

[0076] Analysis of the relationship between pixel number, focal length and monitoring distance:

[0077]

[0078] Under the same conditions, the width and height of each pixel are consistent, and we get:

[0079]

[0080] After derivation, we get:

[0081]

[0082] The height suitable for installing monitoring is:

[0083]

[0084] in,

[0085] f——focal length of the lens, in millimeters (mm);

[0086] L——The measured distance between the acquisition device and the acquisition object, in millimeters (mm);

[0087] h——The measured horizontal width of the target surface of the image sensor of the acquisition equipment (mm). The horizontal width of the target surface varies with different target surface models.

[0088] X——The measured horizontal width of the field of view around the acquisition object, in millimeters (mm);

[0089] n——Number of horizontal pixels of the image in the field of view around the acquisition object;

[0090] D——The measured distance between the eyes of the subject, in millimeters (mm). The distance between the eyes of women is mostly 56-64mm, and the distance between the eyes of men is mostly 60-70mm. This article takes the empirical value of 62mm;

[0091] m——the number of pixels between the two eyes of the face image of the collected object;

[0092] The three-dimensional spatial visual domain simulation method of video surveillance makes full use of modern three-dimensional modeling technology and advanced interactive simulation tools. By fine-tuning a series of key parameters, it realizes the accurate simulation and visualization of the visual domain of monitoring equipment in complex urban environments. This method not only helps to intuitively evaluate the coverage effect of the existing monitoring network, but also assists decision makers in optimizing the layout of monitoring points during the planning and design stage, ensuring the all-round and multi-level coverage of the monitoring system, thereby improving the overall efficiency of the urban security monitoring system.

[0093] Preferably, in step S5, the outputting and displaying the layout list of monitoring points according to the coverage effect diagram includes:

[0094] After the simulation of the coverage effect diagram is completed, a detailed list of monitoring point layouts is output, listing the key parameters of longitude and latitude, hanging height, horizontal and vertical angles, and actual tilt angles, along with equipment configuration recommendations and installation instructions.

[0095] In the above scheme, after the simulation is completed, a detailed list of monitoring point layout is output, listing key parameters such as longitude and latitude, hanging height, horizontal and vertical angles, actual tilt angle, etc., and equipment configuration suggestions and installation instructions are also provided to provide direct guidance for actual deployment; the generated effect map and demonstration materials intuitively display the monitoring network coverage effect, which is convenient for reporting and communication. Given the quantitative characteristics of the planning process, the generated data and results can be seamlessly connected to the video monitoring platform, supporting subsequent data analysis, performance evaluation and system optimization, and providing continuous data support for long-term operation and maintenance.

[0096] In summary, the simulation model proposed in this invention provides a comprehensive and accurate methodological framework and strong technical support for the layout planning of video surveillance systems through rigorous field data collection, precise internal processing, scientific algorithm calculation, real three-dimensional simulation analysis, and systematic output and application of results. This simulation model not only greatly improves the planning efficiency, but also ensures the scientificity and adaptability of the planning, which is of great significance to promoting the efficiency improvement of video surveillance systems and the effective use of resources.

[0097] In the above scheme, the present invention solves how to achieve refined three-dimensional space coverage simulation in high-density monitoring scenarios, such as major traffic routes and key safety areas, to ensure that the simulation results are close to reality;

[0098] Based on the physical characteristics of the camera such as focal length and viewing angle, and the installation environment such as height and obstructions, the monitoring coverage effect can be accurately simulated. Through visual analysis, the coverage of existing points can be intuitively displayed and quantitatively analyzed to find blind spots and redundancies, providing a basis for the scientific site selection of new points. The system supports users to interactively adjust camera parameters and provide real-time feedback on monitoring coverage effects, which improves the flexibility and accuracy of planning;

[0099] Preferably, see Figure 2 , a drone modeling and video monitoring fusion device, comprising:

[0100] The monitoring point collection module is used to collect information about existing and planned monitoring points in the target monitoring area; the monitoring point information includes hanging height, longitude and latitude, ideal tilt angle, direction angle, field of view angle, focal length, and sensor size;

[0101] The UAV acquisition module is used to arrange UAV flight missions according to monitoring requirements and collect 3D real-life data of the target area; UAV flight missions include flight range, route planning, and flight parameter settings;

[0102] The simulation model building module is used to perform information verification and geometric positioning on the three-dimensional real scene data to establish a simulation model; wherein the information verification includes verification of machine parameters, longitude and latitude, altitude and rotation angle;

[0103] The simulation operation module is used to combine the simulation model with the visual range algorithm to generate a coverage effect diagram; the simulation takes into account the latitude and longitude, hanging height, actual tilt angle, horizontal and vertical viewing angles, and minimum and maximum visual distances of the monitoring point;

[0104] The results application module is used to output and display the layout list of monitoring points based on the coverage effect diagram; the layout list includes longitude and latitude, hanging height, horizontal and vertical direction angles and actual tilt angle.

[0105] In an embodiment provided by the present invention, a main urban and rural road section of a city is taken as an example. The scene has the characteristics of large traffic volume, multiple elements, and diverse types. The main focus is on the background environment of motor vehicle lanes, non-motor vehicle lanes, and sidewalks, as well as human and vehicle targets.

[0106] Select the target coverage area. According to the project information, the main equipment selection involved in this case is as follows:

[0107]

[0108]

[0109] See also Figure 3 According to the flight mission plan, appropriate flight parameters were set, and drones equipped with Beidou high-precision positioning were used to conduct aerial photography of case scenes, obtaining 216 high-definition and high-precision photos of the target area.

[0110] See also Figure 4 Each aerial photo has high-precision position, altitude, and rotation data. The intersections that need to be studied are further selected and cropped. After aerial triangulation, 3D reconstruction, model calibration and other steps, high-precision 3D model data of the target study area is finally generated.

[0111] See also Figure 5 In order to facilitate engineering applications, this case made the precise viewing distance range calculation formula based on the selection of video surveillance equipment into a calculation tool. The precise theoretical distance was calculated by inputting equipment parameters. For zoom equipment, the visible distances at the minimum focal length and maximum focal length were calculated respectively as the minimum and maximum visible distances of the equipment.

[0112] The constructed 3D real-scene model and the calculation results of the visible range algorithm (visible range of existing sites, recommended hanging height and visible range parameters of planned new sites) are integrated into the 3D space simulation system. During the simulation process, the multi-dimensional parameters such as the latitude and longitude, hanging height, actual tilt angle, horizontal viewing angle, vertical viewing angle, minimum / maximum visible distance of the monitoring point are fully considered to generate a schematic diagram of the coverage effect of the existing and planned monitoring points, clearly presenting the visible range, invisible range and overlapping monitoring area. By batch importing and processing point layer data, a rapid simulation comparison of multiple planning schemes can be achieved.

[0113] This case is a T-shaped intersection. The video image acquisition equipment deployed in this scene should achieve full video surveillance coverage of the motor vehicle lanes, non-motor vehicle lanes and sidewalks in the traffic section, and form a coverage connection with the surrounding major intersections and entrances and exits. In the face of perception needs including the acquisition of local environmental background elements, facial recognition of non-motor vehicle riders, facial recognition of pedestrians, and human body recognition of pedestrians, gun-type cameras can be used. In the face of perception needs including the acquisition of local environmental background elements, facial recognition of pedestrians, and human body recognition, dome cameras can be used to cover major activity areas and crowded areas.

[0114] Through aerial photography survey of the target area, there are currently 3 smart gun cameras and 1 new smart ball camera is planned. The current and planned point location information, angle, field of view and other equipment parameters can be obtained through on-site survey and project conditions. The minimum visible range and maximum visible range can be calculated through the previous step "Accurate visual range calculation tool based on video surveillance equipment selection". The specific point parameters are as follows:

[0115]

[0116] See also Figure 6 , input the specific point parameters into the simulation model, and the simulation effect of the current video surveillance equipment is shown in the figure below. At present, 3 smart gun cameras have achieved coverage of the main motor vehicle lanes, but lack environmental element acquisition, face recognition, and body recognition for non-motor vehicle lanes and sidewalks, which poses a great safety hazard. In this period, one new smart ball camera is planned to be added, located at the intersection of the T-shaped intersection, taking into account the corner perception needs of the campus scene in the lower right corner. The simulation of the three-dimensional coverage effect after the new planning point is added is shown in Figure 7 .

[0117] See also Figure 8 Based on the preset layout plan, a series of scene plan atlases are generated to intuitively show the coverage effect of the monitoring network. If there is a need for presentation and reporting, dynamic demonstration video materials can be customized.

[0118] This application has built a comprehensive video surveillance planning model, which provides a scientific and efficient methodology for the point planning of large-scale video surveillance systems through drone aerial photography, high-precision 3D modeling, accurate visual range measurement, simulation analysis, and intuitive display results. The innovation of this model lies in its ability to accurately simulate the visual range of monitoring equipment in complex urban environments, ensuring comprehensive coverage of the monitoring network, while optimizing resource allocation and improving the overall efficiency of the security monitoring system.

[0119] With the advancement of technology, more advanced algorithms and models can be further explored to improve the accuracy and efficiency of simulation, such as using machine learning and artificial intelligence technologies for data analysis and pattern recognition. In addition, the application scope of the model is expected to expand to more fields, such as traffic management, urban planning, etc., providing strong technical support for the construction of smart cities.

[0120] This application is not only innovative in theory, but also shows great application potential in practice, and is expected to promote a new wave of video surveillance technology and smart city development. At the same time, at a time when the low-altitude industry has become a new economic form, it provides a useful reference for further expanding the application solutions of drones in industry-level fields such as smart cities.

[0121] A device comprises a processor, a memory and a computer program stored in the memory and configured to be executed by the processor, wherein the processor implements the method for integrating drone modeling and video surveillance when executing the computer program.

[0122] A computer-readable storage medium, the computer-readable storage medium including a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the method for fusion of unmanned aerial vehicle modeling and video surveillance.

[0123] Preferably, the computer program can be divided into one or more modules / units (such as computer program, computer program), and the one or more modules / units are stored in the memory and executed by the processor to complete the present invention. The one or more modules / units can be a series of computer program instruction segments that can complete specific functions, and the instruction segments are used to describe the execution process of the computer program in the terminal device.

[0124] The processor may be a central processing unit (CPU), or other general-purpose processors, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor, or the processor may be any conventional processor. The processor is the control center of the terminal device, and various parts of the terminal device are connected using various interfaces and lines.

[0125] The memory mainly includes a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function, etc., and the data storage area can store related data, etc. In addition, the memory can be a high-speed random access memory, or a non-volatile memory, such as a plug-in hard disk, a smart memory card (SmartMedia Card, SMC), a secure digital (Secure Digital, SD) card, and a flash card (Flash Card), etc., or the memory can also be other volatile solid-state storage devices.

[0126] It should be noted that the above-mentioned terminal device may include, but is not limited to, a processor and a memory. Those skilled in the art will understand that the above-mentioned terminal device is merely an example and does not constitute a limitation on the terminal device. It may include more or fewer components, or a combination of certain components, or different components.

[0127] The above is a preferred embodiment of the present invention. It should be pointed out that a person skilled in the art can make several improvements and modifications without departing from the principle of the present invention. These improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for integrating drone modeling and video surveillance, characterized in that: include: Collect information on existing and planned monitoring points in the target monitoring area; the information on monitoring points includes hanging height, longitude and latitude, ideal tilt angle, direction angle, field of view angle, focal length, and sensor size; Arrange UAV flight missions according to monitoring needs and collect 3D real-life data of the target area; UAV flight missions include flight range, route planning and flight parameters; The three-dimensional real scene data is verified and geometrically positioned to establish a simulation model; the information verification includes the verification of machine parameters, longitude and latitude, altitude and rotation angle; The simulation model is combined with the visual range algorithm to generate a coverage effect diagram; the simulation takes into account the latitude and longitude, hanging height, actual tilt angle, horizontal and vertical viewing angles, and minimum and maximum visual distances of the monitoring point; Output and display the layout list of monitoring points according to the coverage effect diagram; the layout list includes longitude and latitude, hanging height, horizontal and vertical direction angles and actual tilt angle.

2. The method for integrating drone modeling and video surveillance according to claim 1, characterized in that: Arranging the UAV flight mission according to the monitoring requirements and collecting the three-dimensional real scene data of the target area includes: Using drones equipped with high-resolution cameras and combining them with the precise positioning information provided by the Beidou satellite system, we implement multi-angle oblique photography to collect three-dimensional real-scene data of the target area.

3. The method for integrating drone modeling and video surveillance according to claim 1 is characterized in that: The flight parameters include: flight altitude, heading overlap rate, lateral overlap rate, inclination and flight speed.

4. The method for integrating drone modeling and video surveillance according to claim 2 is characterized in that: The three-dimensional real scene data is verified for information and geometric positioning, and the simulation model is established, including: The 3D real-scene data is classified by region and the posture information is verified. The 3D real-scene data is geometrically positioned using aerial triangulation. Control points are added according to accuracy requirements to establish a simulation model.

5. The method for integrating drone modeling and video surveillance according to claim 4 is characterized in that: The simulation model is combined with the visual range algorithm to generate a coverage effect diagram, which includes: Integrate the simulation model with the calculation results of the visibility range algorithm to perform simulation, including the visibility range parameters of existing and planned monitoring points; The visual range algorithm is specifically as follows: based on the focal length of the camera, target surface size, target surface pixel density, actual tilt angle, monitoring target size, physical characteristics of the monitoring point hanging height, and geographical location information of the monitoring point's latitude and longitude, field of view angle, and azimuth angle, the visual range of the existing site is accurately calculated.

6. The method for integrating drone modeling and video surveillance according to claim 5 is characterized in that: The layout list of monitoring points output and displayed according to the coverage effect diagram includes: After the simulation of the coverage effect diagram is completed, a detailed list of monitoring point layouts is output, listing the key parameters of longitude and latitude, hanging height, horizontal and vertical angles, and actual tilt angles, along with equipment configuration recommendations and installation instructions.

7. A drone modeling and video surveillance fusion device, characterized in that: include: The monitoring point collection module is used to collect information about existing and planned monitoring points in the target monitoring area; the monitoring point information includes hanging height, longitude and latitude, ideal tilt angle, direction angle, field of view angle, focal length, and sensor size; The UAV acquisition module is used to arrange UAV flight missions according to monitoring requirements and collect 3D real-life data of the target area; UAV flight missions include flight range, route planning, and flight parameter settings; The simulation model building module is used to perform information verification and geometric positioning on the three-dimensional real scene data to establish a simulation model; wherein the information verification includes verification of machine parameters, longitude and latitude, altitude and rotation angle; The simulation operation module is used to combine the simulation model with the visual range algorithm to generate a coverage effect diagram; the simulation takes into account the latitude and longitude, hanging height, actual tilt angle, horizontal and vertical viewing angles, and minimum and maximum visual distances of the monitoring point; The results application module is used to output and display the layout list of monitoring points based on the coverage effect diagram; the layout list includes longitude and latitude, hanging height, horizontal and vertical direction angles and actual tilt angle.

8. A device, characterized in that The method comprises a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, a method for fusion of unmanned aerial vehicle modeling and video surveillance as described in any one of claims 1 to 6 is implemented.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute a method for fusion of drone modeling and video surveillance as described in any one of claims 1 to 6.

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