Comprehensive data fusion system based on unmanned aerial vehicle road inspection

Abstract

According to the comprehensive data fusion system based on the unmanned aerial vehicle road inspection, a plurality of fusion type comprehensive data bins can be distributed in a road network area, a platform is connected and communicated with the data bins through a convergence switch, and the unmanned aerial vehicle can take off and land among the data bins by operating a scheduling control instruction through the platform. Therefore, a distributed take-off and landing network of the unmanned aerial vehicle is deployed, and the problem that an existing unmanned aerial vehicle cannot complete autonomous routing inspection and needs a pilot and a law enforcement officer to complete field routing inspection is solved; the construction of the system enables data to be transmitted back to a platform in real time, ensures that emergency command can give an instruction in time, and improves the timeliness and response speed. According to the system, linkage of multiple data bin point locations is realized, the data of the whole district road section is comprehensively managed, the control and communication capability of the unmanned aerial vehicle is enhanced, the fusion of the data of the whole road section and the district road section is realized, intelligent network connection and intelligent management are realized, manual operation is reduced, and the system is suitable for diversified application scenes of urban traffic road intersections.

Description

Technical Field

[0001] The utility model relates to the technical field of unmanned aerial vehicle inspection, and in particular to a comprehensive data fusion system based on unmanned aerial vehicle road inspection. Background Art

[0002] Drone inspections have become a crucial component of smart city road inspections and a crucial enabler of intelligent road systems. With the continuous advancement of drone technology, particularly in fixed-point parking and route-setting capabilities, drones can now automatically take off, inspect specific events, and return to base without human intervention. Consequently, addressing drone positioning and deployment, integrating road environment data, and remote platform data monitoring have become pressing challenges.

[0003] The machine nest fusion data warehouse can collect and fuse data from multiple devices. After these collected and fused data are connected to the control center platform such as the transportation bureau, control commands can be issued through autonomous or manual background to initiate inspection commands for drones, check road conditions in real time, relieve traffic pressure, and deal with sudden traffic accidents.

[0004] Currently, traffic lights are managed in a separate system, primarily by higher-level authorities. Road test sensing units only collect traffic data and have no actual involvement in traffic control. Drone inspections are mostly manually operated by higher-level authorities, which lacks real-time performance and prevents rapid response to emergencies.

[0005] These existing technologies that are not widely used still have the following major defects: 1. On-site drone inspections by pilots: The real-time performance is not high, and inspections cannot be carried out around the clock. The superior law enforcement department needs to be equipped with pilots and law enforcement personnel to be present at the same time to complete the on-site inspection tasks.

[0006] 2. Data cannot be transmitted back to the platform in real time: UAV inspection image data can only be saved locally, and the background cannot be viewed in real time, and emergency command orders cannot be issued in real time.

[0007] 3. Slow response: In case of emergency, personnel can only be sent to the scene to deal with it after receiving feedback from the scene, which does not reflect the advantages of drone inspection in handling emergency incidents.

[0008] 4. Single route and no linkage between points: The route is single and cannot be flexibly adjusted; the drones are distributed relatively independently, and drones at different points cannot be linked together, nor can they be linked with other networked sensing equipment on the road.

[0009] 5. Backstage staff need to analyze data in real time: During the drone cruise, backstage staff need to view the video stream in real time to determine whether the scene is being viewed at a fixed point. The drone hovering, camera angle and focal length adjustment rely on the backstage staff to control. Utility Model Content

[0010] The purpose of this utility model is to solve the problems of existing drones that cannot conduct fully autonomous inspections, cannot communicate with other road equipment, and have poor timeliness. This utility model provides the following technical solutions:

[0011] A comprehensive data fusion system based on UAV road inspection, including: UAV nest and UAV, comprehensive data warehouse and platform layer;

[0012] A drone nest is provided with an external micro-weather station, a night vision indicator, and a camera. The drone nest is used to obtain collected environmental data and use its internal control unit to determine whether the drone take-off and landing conditions are met based on the environmental data. The drone nest also includes a control command unit, which is used to send one or more of a cruise path command, a return time command, a fixed-point inspection command, and a photo taking command to the drone.

[0013] The integrated data warehouse, wherein the drone nest is connected to the edge computing gateway of the integrated data warehouse via a network to achieve data transmission; the integrated data warehouse includes at least a traffic signal, an electronic police equipment host, a vehicle-road collaboration unit, a surveillance camera, an environmental sensor, an RFID device, and an industrial computer; the vehicle-road collaboration unit is used to collect vehicle information to restore the real-time traffic road scene, and is also used to determine whether a traffic incident has occurred based on the vehicle information and generate a corresponding emergency response plan based on the traffic incident; the vehicle-road collaboration unit uses the signals fed back by the traffic signal and the electronic police equipment host to perform linkage control and intelligent management of the traffic incident scene;

[0014] The platform layer can connect multiple integrated data warehouses through a convergence switch to form a fusion data warehouse. The platform layer can name, publish and control the drones in a linked manner, and comprehensively manage the road section data of the entire area, so that drones can take off and land between multiple integrated data warehouses. The platform layer is deployed on the upper-level server.

[0015] Further preferably, the micro weather station includes a temperature and humidity sensor, a wind speed and direction sensor, and a rainfall sensor to collect corresponding temperature and humidity information, wind speed and direction information, and rainfall information; the night vision indicator emits green visible light and infrared light at night to guide the take-off and landing of the drone at night; the camera is placed externally above the drone nest to collect real-time information about the surrounding environment of the drone nest.

[0016] Further preferably, the edge computing gateway is provided with an AI analysis module, which performs AI analysis on the real-time video data captured by the drone through its own data processing capabilities and AI analysis.

[0017] Further preferably, the traffic signal is used to control traffic lights at intersections and receive control commands transmitted by the edge computing gateway at the same time; the electronic police equipment host controls the checkpoint camera and speed measurement equipment to collect evidence of traffic violations; the vehicle information collected by the vehicle-road collaborative unit includes road test vehicle information, real-time scheduling information, vehicle characteristics and behavior information, and the vehicle information data is transmitted to the edge computing gateway through a network connection for data fusion; the surveillance camera monitors the obstruction and abnormal intrusion around the integrated data warehouse, and alarms to the platform layer; the environmental sensor includes a temperature and humidity sensor, a water immersion sensor, and a smoke sensor, which monitors abnormal temperature, water immersion and smoke information inside the integrated data warehouse, and alarms to the platform layer; the RFID device marks the location information of the integrated data warehouse and enables the drone to identify any of the integrated data warehouses; the industrial computer is used to locally view the environmental data information of the drone and the drone nest.

[0018] Further preferably, the edge computing gateway is also connected to all devices built into the integrated data warehouse. The integrated data warehouse is equipped with a UPS and a battery. The battery is a backup battery that provides backup power in the event of an abnormal power outage. The UPS is used in conjunction with the battery to invert the DC power provided by the backup battery into a 220V AC voltage used by each module, and is connected to the edge computing gateway via an RS232 bus.

[0019] Further preferably, an intelligent circuit breaker system is provided, which consists of a lightning arrester, a main circuit breaker and branch circuit breakers. It communicates with the edge computing gateway through the RS485 bus to monitor the power consumption in the integrated data warehouse in real time.

[0020] The utility model is a comprehensive data fusion system based on drone road inspection. Multiple integrated integrated data warehouses can be distributed in the road network area. The platform connects and communicates with multiple data warehouses through a convergence switch. Through the scheduling control instructions run by the platform, drones can be taken off and landed between multiple data warehouses, thereby realizing a distributed take-off and landing network for deploying drones, solving the problem that existing drones cannot conduct completely autonomous inspections and require pilots and law enforcement personnel to complete on-site inspections. The construction of this system enables data to be transmitted back to the platform in real time, ensuring that emergency command can issue instructions in a timely manner, improving timeliness and response speed; this system realizes the linkage of multiple data warehouse points, comprehensively manages the data of the entire road section, enhances the control and communication capabilities of drones, realizes the fusion of road data of the entire road section and the area, realizes intelligent networking and intelligent management, and reduces manual operations.

[0021] The utility model discloses a comprehensive data fusion system based on drone road inspections, which has fusion functions, an integrated drone nest and a comprehensive data warehouse, and targeted detection and monitoring functions for harsh environments and weather. The comprehensive data warehouse can detect and collect pedestrian and vehicle traffic conditions at the entire traffic intersection and related road sections, especially data from road test sensing equipment, signal control data, data warehouse operation monitoring data, drone cruise data, and nest environmental monitoring data. It also has module detection, intelligent operation and maintenance, and alarm functions. The drone and nest can flexibly inspect road conditions within the patrol radius, including congestion, road surface, accidents, illegal parking, and other violations. It constructs an air-ground integrated data warehouse with "communication, guidance, monitoring, and calculation" to support drone takeoff and landing, charging and battery replacement, maintenance and operation, and other inspection services to meet the needs of large-scale drone operations for heterogeneous, high-density, high-frequency, and high-complexity road inspections. It is also a holistic edge intelligence aggregate for the field of intelligent transportation, suitable for diversified application scenarios at urban traffic intersections. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 It is a schematic diagram of the system composition of the present utility model. DETAILED DESCRIPTION

[0023] 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 embodiments described 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 making creative efforts are within the scope of protection of the present invention.

[0024] The preferred embodiments of the present invention are described in further detail below with reference to the accompanying drawings.

[0025] Example 1:

[0026] The present invention provides a comprehensive data fusion system based on drone road inspection, comprising a drone nest and drones, a comprehensive data warehouse and a platform layer. In addition to being used to accommodate drones, the drone nest is also used for monitoring the drone take-off and landing environment, drone flight control and drone protection. The drone nest of the present invention's comprehensive data fusion system based on drone road inspection is equipped with an external micro-weather station, a night vision indicator and a camera. After collecting environmental data, the internal control unit of the drone nest determines whether the drone take-off and landing conditions are met; the drone nest is equipped with a control command unit that sends cruise path, return time, fixed-point inspection and photo-taking instructions to the drone.

[0027] The micro-weather station includes temperature and humidity sensors, wind speed and direction sensors, and rainfall sensors, which collect corresponding temperature and humidity information, wind speed and direction information, and rainfall information for the UAV to judge the flight conditions and whether they meet the flight safety requirements; the night vision indicator emits green visible light and infrared light at night to guide the take-off and landing of the UAV at night; the camera is placed outside the drone nest to collect real-time information about the surrounding environment of the drone nest. The camera uses a high-definition camera to monitor the take-off and landing environment of the drone, and judges whether the take-off and landing environment meets the take-off and landing conditions through the control unit inside the nest. The judgment of the internal control unit includes the environmental conditions of the micro-weather station, the high-definition camera outside the nest, and the night vision indicator data to ensure that the drone take-off and landing environment is not disturbed, so that the drone has 24-hour all-weather operation and inspection capabilities.

[0028] This new drone nest can flexibly inspect road conditions within its cruising radius, including information on congestion, road surface conditions, accidents, illegal parking, and other violations. It builds an integrated air-ground data warehouse featuring "communication, guidance, monitoring, and calculation," supporting drone takeoff and landing, charging and battery replacement, maintenance, and other inspection services. This meets the demands of large-scale drone operations for heterogeneous, high-density, high-frequency, and highly complex road inspections. It also serves as a holistic edge intelligence aggregation platform for intelligent transportation, applicable to diverse application scenarios at urban intersections.

[0029] The drone itself has an image transmission function, which transmits the drone's cruise video to the drone nest of the utility model. The drone nest realizes data transmission through a network connection and the edge computing gateway of the integrated data warehouse. After the video data is transmitted to the edge computing gateway in the integrated data warehouse, the edge computing gateway is equipped with an AI analysis module, which performs AI analysis on the real-time video data taken by the drone through its own data processing capabilities and AI analysis.

[0030] The comprehensive data warehouse of the present invention has built-in traffic signals, electronic police equipment host, vehicle-road collaboration unit, surveillance cameras, environmental sensors, RFID equipment and industrial control computers; the vehicle information collected by the vehicle-road collaboration unit restores the real-time scene of the entire traffic road, determines the occurrence of traffic incidents and generates emergency response plans; the vehicle-road collaboration unit uses the traffic signals and electronic police equipment host to carry out linkage control and intelligent management of the traffic incident scene.

[0031] Traffic signals are used to control traffic lights at intersections and receive control commands transmitted by edge computing gateways to control traffic lights in emergencies.

[0032] The electronic police equipment host controls the checkpoint cameras and speed measuring equipment to collect evidence of traffic violations;

[0033] The vehicle information collected by the vehicle-road collaboration unit includes road test vehicle information, real-time scheduling information, vehicle characteristics and behavior information. After analyzing the above information, the data can be transmitted to the edge computing gateway through the network connection for data fusion;

[0034] Surveillance cameras monitor obstructions and abnormal intrusions around the integrated data warehouse, and send alarms to the platform layer to ensure a safe operating environment for drones and data warehouses.

[0035] Environmental sensors include temperature and humidity sensors, water immersion sensors, and smoke sensors. They monitor abnormal temperature, water immersion, and smoke information inside the integrated data warehouse, and send alarms to the platform layer for emergency processing. This enables intelligent operation and maintenance, while also monitoring the safety conditions of equipment and data operation inside the data warehouse in real time.

[0036] The RFID device marks the location information of the integrated data warehouse and enables the drone to identify any integrated data warehouse. The drone can realize functions such as frog jumping (the drone can take off, land and charge at any data warehouse) and linked flight according to the scheduling information.

[0037] The industrial computer is used to locally view the environmental data information of the drone and its nest, realizing the purpose of on-site intelligent operation and maintenance.

[0038] The edge computing gateway also connects to all devices within the integrated data warehouse and the drone control center. Leveraging its own data processing and AI analysis capabilities, it performs AI analysis on real-time video data captured by drones. Combined with vehicle information collected by the vehicle-road collaboration unit, it restores the entire real-time traffic scene, identifies incidents, and generates emergency response plans. Traffic signals, electronic police systems, and other systems then coordinate on-site control, enabling intelligent road management.

[0039] The integrated data warehouse is equipped with a UPS and a battery. The battery is a backup battery that provides backup power in the event of an abnormal power outage. The UPS and the battery are used together to invert the DC power provided by the backup battery into a 220V AC voltage used by each module, and connected to the edge computing gateway through the RS232 bus.

[0040] The integrated data warehouse is also equipped with an intelligent circuit breaker system, which consists of a lightning arrester, a main circuit breaker and various branch circuit breakers. It communicates with the edge computing gateway through the RS485 bus, monitors the power consumption in the integrated data warehouse in real time, and can remotely control the power supply of each module in the integrated data warehouse.

[0041] At the platform level, multiple integrated data warehouses are connected via aggregation switches to form a fused data warehouse. This allows for naming, publishing, and coordinated control, comprehensively managing data for the entire road section. This allows drones to take off and land between these integrated data warehouses, achieving intelligent connectivity and reducing manual operations. Deployed on a higher-level server, such as a transportation bureau or other administrative department, the platform layer can access multiple data warehouses for unified management. A central control screen at the platform level can issue control commands, view real-time road conditions, and monitor drone inspection videos. Furthermore, event analysis results from the data warehouse's internal edge computing gateway can be combined to enable remote control, task scheduling, and emergency response. The integrated data warehouse collects and integrates data from multiple devices. Once connected to a control center platform, such as the transportation bureau, control commands can be issued autonomously or manually, initiating inspection commands for drones. This allows for real-time monitoring of road conditions, alleviating traffic pressure and addressing emergencies.

[0042] The utility model is a comprehensive data fusion system based on drone road inspection. Multiple integrated integrated data warehouses can be distributed in the road network area. The platform connects and communicates with multiple data warehouses through a convergence switch. Through the scheduling control instructions run by the platform, drones can be taken off and landed between multiple data warehouses, thereby realizing a distributed take-off and landing network for deploying drones, solving the problem that existing drones cannot conduct completely autonomous inspections and require pilots and law enforcement personnel to complete on-site inspections. The construction of this system enables data to be transmitted back to the platform in real time, ensuring that emergency command can issue instructions in a timely manner, improving timeliness and response speed; this system realizes the linkage of multiple data warehouse points, comprehensively manages the data of the entire road section, enhances the control and communication capabilities of drones, realizes the fusion of road data of the entire road section and the area, realizes intelligent networking and intelligent management, and reduces manual operations.

[0043] This utility model features a comprehensive data fusion system for drone road inspections, featuring integrated drone nests and a comprehensive data warehouse, with targeted detection and monitoring capabilities for harsh environments and weather conditions. The comprehensive data warehouse monitors and collects pedestrian and vehicle traffic at intersections and related road sections, specifically sensor data, signal control data, data warehouse operation monitoring data, drone patrol data, and nest environment monitoring data. It also incorporates module detection, intelligent operation and maintenance, and alarm capabilities.

[0044] The above content is a further detailed description of the present invention in conjunction with specific preferred embodiments, and the specific implementation of the present invention cannot be considered to be limited to these descriptions. For those skilled in the art of the present invention, without departing from the concept of the present invention, several simple deductions or substitutions can be made, which should be considered to fall within the scope of protection of the present invention.

Claims

1. A comprehensive data fusion system based on UAV road inspection, characterized by: include: UAV nest and drones, integrated data warehouse and platform layer; A drone nest is provided with an external micro-weather station, a night vision indicator, and a camera. The drone nest is used to obtain collected environmental data and use its internal control unit to determine whether the drone take-off and landing conditions are met based on the environmental data. The drone nest also includes a control command unit, which is used to send one or more of a cruise path command, a return time command, a fixed-point inspection command, and a photo taking command to the drone. The integrated data warehouse, wherein the drone nest is connected to the edge computing gateway of the integrated data warehouse via a network to achieve data transmission; the integrated data warehouse includes at least a traffic signal, an electronic police equipment host, a vehicle-road collaboration unit, a surveillance camera, an environmental sensor, an RFID device, and an industrial computer; the vehicle-road collaboration unit is used to collect vehicle information to restore the real-time traffic road scene, and is also used to determine whether a traffic incident has occurred based on the vehicle information and generate a corresponding emergency response plan based on the traffic incident; the vehicle-road collaboration unit uses the signals fed back by the traffic signal and the electronic police equipment host to perform linkage control and intelligent management of the traffic incident scene; The platform layer can connect multiple integrated data warehouses through a convergence switch to form a fusion data warehouse. The platform layer can name, publish and control the drones in a linked manner, and comprehensively manage the road section data of the entire area, so that drones can take off and land between multiple integrated data warehouses. The platform layer is deployed on the upper-level server.

2. The comprehensive data fusion system based on UAV road inspection according to claim 1 is characterized in that: The micro-weather station includes a temperature and humidity sensor, a wind speed and direction sensor, and a rainfall sensor to collect corresponding temperature and humidity information, wind speed and direction information, and rainfall information; the night vision indicator emits green visible light and infrared light at night to guide the takeoff and landing of the drone at night; the camera is placed externally above the drone nest to collect real-time information about the surrounding environment of the drone nest.

3. The comprehensive data fusion system based on drone road inspection according to claim 1 is characterized in that: The edge computing gateway is provided with an AI analysis module, which performs AI analysis on the real-time video data captured by the drone through its own data processing capabilities and AI analysis.

4. The comprehensive data fusion system based on drone road inspection according to claim 3 is characterized in that: The traffic signal is used to control the traffic lights at the intersection and receive control commands transmitted by the edge computing gateway at the same time; the electronic police equipment host controls the checkpoint camera and speed measurement equipment to collect evidence of traffic violations; the vehicle information collected by the vehicle-road collaborative unit includes road test vehicle information, real-time scheduling information, vehicle characteristics and behavior information, and the vehicle information data is transmitted to the edge computing gateway through a network connection for data fusion; the monitoring camera monitors the obstruction and abnormal intrusion around the integrated data warehouse and sends an alarm to the platform layer; the environmental sensor includes a temperature and humidity sensor, a water immersion sensor, and a smoke sensor, which monitors the abnormal temperature, water immersion and smoke information inside the integrated data warehouse and sends an alarm to the platform layer; the RFID device marks the location information of the integrated data warehouse and enables the drone to identify any of the integrated data warehouses; the industrial computer is used to locally view the environmental data information of the drone and the drone nest.

5. The comprehensive data fusion system based on UAV road inspection according to claim 3 is characterized in that: The edge computing gateway is also connected to all devices built into the integrated data warehouse, which is equipped with a UPS and a battery. The battery is a backup battery that provides backup power in the event of an abnormal power outage. The UPS is used in conjunction with the battery to invert the DC power provided by the backup battery into a 220V AC voltage used by each module, and is connected to the edge computing gateway via an RS232 bus.

6. The comprehensive data fusion system based on drone road inspection according to claim 5 is characterized in that: An intelligent circuit breaker system is also provided, which consists of a lightning arrester, a main circuit breaker and branch circuit breakers. It communicates with the edge computing gateway through the RS485 bus to monitor the power consumption in the integrated data warehouse in real time.

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