Smart city management method and system based on unmanned aerial vehicle inspection

Abstract

The invention relates to the technical field of smart city management, in particular to a smart city management method and system based on unmanned aerial vehicle inspection, and the system comprises a hardware unit and a software system: the hardware unit comprises an unmanned aerial vehicle module, a sensor module, a communication and transmission module and a ground support module; the software system comprises flight control and task management, data processing and analysis, a collaborative management platform and intelligent decision support; the flight control and task management comprises an autonomous flight control system and a task scheduling system; the data processing and analysis comprises an AI image recognition algorithm, a three-dimensional modeling tool and a data fusion platform; the collaborative management platform comprises a GIS integrated system, a closed-loop management system and a large language model integrated system. According to the invention, through deep cooperation of hardware and software, refined requirements of full-time perception, accurate analysis and rapid response in smart city management are met, a risk high-incidence area is predicted, and law enforcement is promoted to change from post-event disposal to pre-event prevention.

Description

Technical Field

[0001] The present invention relates to the field of smart city management technology, and in particular to a smart city management method and system based on drone inspections. Background Art

[0002] Smart cities are a new type of city built with the goal of more scientific development, more efficient management, and a better life. They are supported by information and communications technologies and aim to improve urban operational efficiency, enhance public services, and form a low-carbon urban ecosystem through transparent and comprehensive information access, extensive and secure information transmission, and effective and scientific information processing. With the rapid development of science and technology and the advent of an information-based society, smart cities have become a new direction for future urban planning.

[0003] A Chinese patent with publication number CN114971409B discloses a smart city fire monitoring and early warning method and system based on the Internet of Things. The method is executed by a management platform, including: obtaining monitoring data collected by an object platform through a sensor network platform, the monitoring data including smoke data, temperature data, drone image data, and manual inspection data, and the manual inspection data is obtained based on the manual inspection time interval; determining the fire risk level based on the monitoring data; in response to the fire risk level meeting the preset conditions, issuing an alarm to the user platform through the service platform; in response to the fire risk level not meeting the preset conditions, determining the manual inspection time interval based on the fire risk level, and the manual inspection time interval is sent to the service platform and / or the user platform.

[0004] However, the above technical solution has the following shortcomings: the response time is relatively long, and it is difficult to take timely and effective measures to deal with the abnormal situations discovered. There is a certain lag, which leads to traffic jams, fires and other accidents occurring or even spreading to a certain extent before active responses can be made. This not only increases the difficulty of problem handling and rescue, but also expands the scope and extent of the impact of the accident. Summary of the Invention

[0005] The purpose of this invention is to address the problems existing in the background technology and propose a smart city management method and system based on drone inspection.

[0006] The technical solution of the present invention: a smart city management system based on drone inspection, including hardware units and software systems:

[0007] The hardware unit includes the UAV module, sensor module, communication and transmission module and ground support module;

[0008] The software system includes flight control and mission management, data processing and analysis, collaborative management platform, and intelligent decision support. The operation of the software system depends on the hardware unit and is used to control the operation of the hardware and process data;

[0009] Flight control and mission management include an autonomous flight control system and a mission scheduling system. The autonomous flight control system includes preset route planning, obstacle avoidance algorithms, and dynamic path adjustment, supporting automatic takeoff and landing and scheduled inspections. The mission scheduling system is used to dynamically assign inspection task priorities and optimize resource utilization.

[0010] Data processing and analysis include AI image recognition algorithms, 3D modeling tools, and data fusion platforms;

[0011] The collaborative management platform includes a GIS integration system, a closed-loop management system, and a large language model integration system;

[0012] Intelligent decision support establishes risk warning models based on historical data to predict equipment failures or urban risk points.

[0013] Preferably, the drone module includes a quad-rotor drone group and a fixed-wing drone group to adapt to the needs of different inspection scenarios. The quad-rotor drone group is used for short-distance fine inspections, and the fixed-wing drone group is used for large-scale rapid inspections.

[0014] Preferably, the sensor module is mounted on a quadrotor drone group and a fixed-wing drone group. The sensor module includes an environmental perception unit and a special detection unit. The environmental perception unit is used to perceive environmental data in real time to support dynamic path planning, and the special detection unit is used to collect various inspection data.

[0015] Preferably, the special detection unit includes an optical camera, an infrared thermal imager, a lidar and a gas sensor; the optical camera collects high-resolution images for identifying problems such as road occupation and equipment failure; the infrared thermal imager is used to detect thermal anomalies such as overheating of power equipment and fire hazards; the lidar supports terrain mapping, obstacle detection and three-dimensional modeling; the gas sensor is used to monitor environmental indicators such as air quality in real time.

[0016] Preferably, the communication and transmission module includes 5G communication network and satellite communication; the 5G communication network is used to ensure low-latency communication between the drone and the ground control center, and the satellite communication is used for signal coverage in remote areas or complex environments.

[0017] Preferably, the ground support module includes an intelligent charging base station, a large visual screen and a mobile terminal device; the intelligent charging base station supports automatic charging of the drone to ensure mission continuity, the large visual screen is used to display the full-area inspection status, early warning information and disposal progress in real time, and the mobile terminal device is used to view inspection data and process alarm information in real time.

[0018] Optimally, AI image recognition algorithms automatically detect problems such as illegal buildings, equipment failures, and environmental pollution; 3D modeling tools generate digital models of urban infrastructure based on lidar point cloud data; and data fusion platforms integrate drone data with GIS maps and IoT device information to generate visual reports.

[0019] Preferably, the GIS integrated system overlays geographic information and inspection data to achieve accurate problem location and route planning; the closed-loop management system supports full-process digital management; and the large language model integrated system generates inspection reports and emergency response recommendations through natural language interaction.

[0020] On the other hand, the present invention proposes a smart city management method based on drone inspection, which adopts the above-mentioned smart city management system based on drone inspection, and specifically includes the following steps:

[0021] S1. The drone module conducts scheduled inspections along a preset route under the control of an autonomous flight control system. During the inspection process, the quadcopter drone group, in cooperation with a special inspection unit, mainly identifies data such as road occupation, traffic flow, overheating of power equipment, fire hazards, and air quality. The quadcopter drone group detects road damage and identifies illegal buildings, while the fixed-wing drone group mainly conducts forest fire monitoring, river pollution tracking, terrain mapping, and 3D modeling.

[0022] S2, AI image recognition algorithm automatically detects problems such as road occupation, traffic flow, overheating of power equipment, and fire hazards based on data synchronized with the communication and transmission modules, with recognition speeds reaching minutes.

[0023] S3. During the inspection process of the drone module, if unexpected situations such as road occupation, traffic congestion, fire, etc. occur, the task scheduling system will dynamically assign inspection tasks according to task priority to optimize resource utilization;

[0024] S4, 3D modeling tools generate digital models of urban infrastructure based on LiDAR point cloud data. The data fusion platform integrates drone data with GIS maps and IoT device information to generate visualization reports with positioning information for real-time display on large-screen visualizations.

[0025] S5. The closed-loop management system supports digital management of the entire process of "discovering problems - issuing work orders - processing feedback - archiving". The large language model integration system generates inspection reports and emergency response suggestions through natural language interaction.

[0026] Compared with the prior art, the above technical solution of the present invention has the following beneficial technical effects:

[0027] This invention achieves urban grid inspection and global data collection through deep collaboration of hardware and software, and standardized route design of multi-machine collaboration. The AI ​​algorithm speeds up problem identification to minutes, shortening the processing cycle. The system can meet the refined requirements of "full-time perception, precise analysis, and rapid response" in smart city management, integrate multimodal large models to predict high-risk areas, and promote law enforcement from post-processing to pre-prevention. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 A schematic diagram of a hardware unit in an embodiment of the present invention;

[0029] Figure 2 A schematic diagram of a software system in an embodiment of the present invention. DETAILED DESCRIPTION

[0030] Example 1, as Figure 1-2 As shown, the smart city management system based on drone inspection proposed by the present invention includes hardware units and software systems:

[0031] The hardware unit includes the UAV module, sensor module, communication and transmission module and ground support module;

[0032] The software system includes flight control and mission management, data processing and analysis, collaborative management platform, and intelligent decision support. The operation of the software system depends on the hardware unit and is used to control the operation of the hardware and process data;

[0033] Flight control and mission management include an autonomous flight control system and a mission scheduling system. The autonomous flight control system includes preset route planning, obstacle avoidance algorithms, and dynamic path adjustment, supporting automatic takeoff and landing and scheduled inspections. The mission scheduling system is used to dynamically assign inspection task priorities and optimize resource utilization.

[0034] Data processing and analysis include AI image recognition algorithms, 3D modeling tools, and data fusion platforms;

[0035] The collaborative management platform includes a GIS integration system, a closed-loop management system, and a large language model integration system;

[0036] Intelligent decision support establishes risk warning models based on historical data to predict equipment failures or urban risk points.

[0037] The drone module includes a quad-rotor drone group and a fixed-wing drone group to meet the needs of different inspection scenarios. The quad-rotor drone group is used for short-distance fine inspections, and the fixed-wing drone group is used for large-scale rapid inspections.

[0038] The sensor module is mounted on the quadcopter drone group and the fixed-wing drone group. The sensor module includes an environmental perception unit and a special detection unit. The environmental perception unit is used to perceive environmental data in real time to support dynamic path planning, and the special detection unit is used to collect various inspection data.

[0039] Special detection units include optical cameras, infrared thermal imagers, lidars and gas sensors; optical cameras capture high-resolution images to identify problems such as road occupation and equipment failure; infrared thermal imagers are used to detect thermal anomalies such as overheating of power equipment and fire hazards; lidars support terrain mapping, obstacle detection and three-dimensional modeling; gas sensors are used to monitor environmental indicators such as air quality in real time.

[0040] The communication and transmission module includes 5G communication network and satellite communication; the 5G communication network is used to ensure low-latency communication between the drone and the ground control center, and satellite communication is used for signal coverage in remote areas or complex environments.

[0041] The ground support module includes an intelligent charging base station, a large visual screen and mobile terminal devices; the intelligent charging base station supports automatic charging of drones to ensure mission continuity, the large visual screen is used to display the full-area inspection status, early warning information and disposal progress in real time, and the mobile terminal devices are used to view inspection data and process alarm information in real time.

[0042] AI image recognition algorithms automatically detect illegal buildings, equipment failures, environmental pollution and other problems; 3D modeling tools generate digital models of urban infrastructure based on lidar point cloud data; and data fusion platforms integrate drone data with GIS maps and IoT device information to generate visual reports.

[0043] The GIS integrated system overlays geographic information and inspection data to achieve accurate problem location and route planning; the closed-loop management system supports full-process digital management; and the large language model integrated system generates inspection reports and emergency response recommendations through natural language interaction.

[0044] In the second embodiment, a smart city management method based on drone inspection is proposed by the present invention, which adopts the smart city management system based on drone inspection in the first embodiment and specifically includes the following steps:

[0045] S1. The drone module conducts scheduled inspections along a preset route under the control of an autonomous flight control system. During the inspection process, the quadcopter drone group, in cooperation with a special inspection unit, mainly identifies data such as road occupation, traffic flow, overheating of power equipment, fire hazards, and air quality. The quadcopter drone group detects road damage and identifies illegal buildings, while the fixed-wing drone group mainly conducts forest fire monitoring, river pollution tracking, terrain mapping, and 3D modeling.

[0046] S2, AI image recognition algorithm automatically detects problems such as road occupation, traffic flow, overheating of power equipment, and fire hazards based on data synchronized with the communication and transmission modules, with recognition speeds reaching minutes.

[0047] S3. During the inspection process of the drone module, if unexpected situations such as road occupation, traffic congestion, fire, etc. occur, the task scheduling system will dynamically assign inspection tasks according to task priority to optimize resource utilization;

[0048] S4, 3D modeling tools generate digital models of urban infrastructure based on LiDAR point cloud data. The data fusion platform integrates drone data with GIS maps and IoT device information to generate visualization reports with positioning information for real-time display on large-screen visualizations.

[0049] S5. The closed-loop management system supports digital management of the entire process of "discovering problems - issuing work orders - processing feedback - archiving". The large language model integration system generates inspection reports and emergency response suggestions through natural language interaction.

[0050] The embodiments of the present invention are described in detail above with reference to the accompanying drawings, but the present invention is not limited thereto. Various changes can be made within the scope of knowledge possessed by those skilled in the art without departing from the spirit of the present invention.

Claims

1. The smart city management system based on drone inspection is characterized by: Including hardware units and software systems: The hardware unit includes the UAV module, sensor module, communication and transmission module and ground support module; The software system includes flight control and mission management, data processing and analysis, collaborative management platform, and intelligent decision support. The operation of the software system depends on the hardware unit and is used to control the operation of the hardware and process data; Flight control and mission management include an autonomous flight control system and a mission scheduling system. The autonomous flight control system includes preset route planning, obstacle avoidance algorithms, and dynamic path adjustment, supporting automatic takeoff and landing and scheduled inspections. The mission scheduling system is used to dynamically assign inspection task priorities and optimize resource utilization. Data processing and analysis include AI image recognition algorithms, 3D modeling tools, and data fusion platforms; The collaborative management platform includes a GIS integration system, a closed-loop management system, and a large language model integration system; Intelligent decision support establishes risk warning models based on historical data to predict equipment failures or urban risk points.

2. The smart city management system based on drone inspection according to claim 1 is characterized in that: The drone module includes a quad-rotor drone group and a fixed-wing drone group to meet the needs of different inspection scenarios. The quad-rotor drone group is used for short-distance fine inspections, and the fixed-wing drone group is used for large-scale rapid inspections.

3. The smart city management system based on drone inspection according to claim 2 is characterized in that: The sensor module is mounted on the quadcopter drone group and the fixed-wing drone group. The sensor module includes an environmental perception unit and a special detection unit. The environmental perception unit is used to perceive environmental data in real time to support dynamic path planning, and the special detection unit is used to collect various inspection data.

4. The smart city management system based on drone inspection according to claim 3 is characterized in that: Special detection units include optical cameras, infrared thermal imagers, lidars and gas sensors; optical cameras capture high-resolution images to identify problems such as road occupation and equipment failure; infrared thermal imagers are used to detect thermal anomalies such as overheating of power equipment and fire hazards; lidars support terrain mapping, obstacle detection and three-dimensional modeling; gas sensors are used to monitor environmental indicators such as air quality in real time.

5. The smart city management system based on drone inspection according to claim 4 is characterized in that: The communication and transmission module includes 5G communication network and satellite communication; the 5G communication network is used to ensure low-latency communication between the drone and the ground control center, and satellite communication is used for signal coverage in remote areas or complex environments.

6. The smart city management system based on drone inspection according to claim 5 is characterized in that: The ground support module includes an intelligent charging base station, a large visual screen and mobile terminal devices; the intelligent charging base station supports automatic charging of drones to ensure mission continuity, the large visual screen is used to display the full-area inspection status, early warning information and disposal progress in real time, and the mobile terminal devices are used to view inspection data and process alarm information in real time.

7. The smart city management system based on drone inspection according to claim 6 is characterized in that: AI image recognition algorithms automatically detect illegal buildings, equipment failures, environmental pollution, and other issues; 3D modeling tools generate digital models of urban infrastructure based on LiDAR point cloud data; The data fusion platform integrates drone data with GIS maps and IoT device information to generate visual reports.

8. The smart city management system based on drone inspection according to claim 7 is characterized in that: The GIS integrated system overlays geographic information and inspection data to achieve accurate problem location and route planning; the closed-loop management system supports full-process digital management; and the large language model integrated system generates inspection reports and emergency response recommendations through natural language interaction.

9. A smart city management method based on drone inspection, using the smart city management system based on drone inspection according to any one of claims 1 to 8, characterized in that: The specific steps include: S1. The drone module conducts scheduled inspections along a preset route under the control of an autonomous flight control system. During the inspection process, the quadcopter drone group, in cooperation with a special inspection unit, mainly identifies data such as road occupation, traffic flow, overheating of power equipment, fire hazards, and air quality. The quadcopter drone group detects road damage and identifies illegal buildings, while the fixed-wing drone group mainly conducts forest fire monitoring, river pollution tracking, terrain mapping, and 3D modeling. S2, AI image recognition algorithm automatically detects problems such as road occupation, traffic flow, overheating of power equipment, and fire hazards based on data synchronized with the communication and transmission modules, with recognition speeds reaching minutes. S3. During the inspection process of the drone module, if unexpected situations such as road occupation, traffic congestion, fire, etc. occur, the task scheduling system will dynamically assign inspection tasks according to task priority to optimize resource utilization; S4, 3D modeling tools generate digital models of urban infrastructure based on LiDAR point cloud data. The data fusion platform integrates drone data with GIS maps and IoT device information to generate visualization reports with positioning information for real-time display on large-screen visualizations. S5. The closed-loop management system supports digital management of the entire process of "discovering problems - issuing work orders - processing feedback - archiving". The large language model integration system generates inspection reports and emergency response suggestions through natural language interaction.

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