Space-air-ground integrated smart city comprehensive treatment platform

Abstract

The invention discloses a space-air-ground integrated smart city comprehensive treatment platform, and relates to the technical field of smart city management. In order to solve the problems that a traditional ground monitoring means often has a blind area, omnibearing monitoring is difficult to realize, the response speed of ground monitoring is limited by factors such as traffic and personnel scheduling, and the ground monitoring cannot quickly arrive at a site for disposal. A space-air-ground integrated smart city comprehensive treatment platform comprises an unmanned aerial vehicle cluster unit, a ground monitoring integration unit and a comprehensive treatment unit. Comprehensive and efficient urban patrol and treatment are realized through the unmanned aerial vehicle cluster unit, the unmanned aerial vehicle cluster is wide in coverage, running state data are acquired in real time, stable and reliable task execution is ensured, a comprehensive treatment platform quickly responds to abnormal conditions, unmanned aerial vehicles are scheduled for live broadcast, and powerful support is provided for emergency response; comprehensive monitoring, data analysis and emergency response of the urban environment are realized, and the intelligent and refined level of comprehensive treatment of the smart city is effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of smart city management, and particularly to an integrated space-air-ground smart city comprehensive governance platform. Background Art

[0002] For the comprehensive governance of smart cities, traditional ground monitoring means have problems such as many blind spots and slow response speed, and it is difficult to meet the needs of modern urban governance. UAV technology, with its flexible and mobile characteristics and wide coverage, shows great potential in the governance of smart cities. Currently, regarding the comprehensive governance of smart cities, a patent application with the publication number CN109544428A discloses a smart city governance platform, which includes establishing a city CIM early warning model, a city three-dimensional traffic special system, a city environmental information special system, a city government service special system, a city economic operation special system, a city work safety special system, and a city infrastructure special system based on a 3D GIS model and AI intelligent early warning, realizing the monitoring, processing, and operation of the overall wisdom of the city. Through the integration ability of the city special application for information and urban IoT perception resources, it plays a general role in major livelihood services such as three-dimensional traffic, economic operation, work safety, infrastructure, and government services. Its application in urban governance and public services is conducive to promoting the coverage of basic public services among different levels, different regions, and different groups, effectively improving the level of public services and their equal and inclusive degree, assisting government decision-making and crisis management, and enhancing the government's governance ability.

[0003] Although the above patent conducts urban governance by integrating multiple systems, there may still be deficiencies in information integration, sharing, and utilization, resulting in the phenomenon of information islands, unable to fully utilize the value of data. Moreover, traditional ground monitoring means often have blind spots, especially in complex terrains or areas with dense high-rise buildings, making it difficult to achieve full-range monitoring. The response speed of ground monitoring is limited by factors such as traffic and personnel dispatching, and it is impossible to quickly reach the scene for disposal, and it is impossible to achieve comprehensive and efficient inspections of urban areas. Summary of the Invention

[0004] The purpose of the present invention is to provide an integrated space-air-ground smart city comprehensive governance platform, which realizes comprehensive and efficient urban inspection and governance through a UAV cluster unit. The UAV cluster has a wide coverage and can obtain real-time operation status data to ensure stable and reliable task execution. The comprehensive governance platform quickly responds to abnormal situations, dispatches UAVs for live broadcasts, provides strong support for emergency response, realizes comprehensive monitoring, data analysis, and emergency response of the urban environment, and effectively improves the intelligent and refined level of smart city comprehensive governance, so as to solve the problems raised in the above background art.

[0005] To achieve the above purpose, the present invention provides the following technical solutions:

[0006] Space-air-ground integrated intelligent urban comprehensive governance platform, including:

[0007] Drone cluster unit, used to deploy no less than one drone in the city to form a drone cluster, determine the detection range of each drone, where the detection ranges cover each other, plan the task instructions of the drone cluster, and control the drone cluster to execute various tasks according to the task instructions;

[0008] The drone cluster unit is also used to obtain the operation status data of each drone in real time, determine the endurance capacity, payload capacity and flight execution stability data of each drone based on the operation status data, judge whether the drone is abnormal based on the operation status data of each drone, and give early warnings and take emergency measures in a timely manner;

[0009] Ground monitoring integration unit, used to integrate existing ground monitoring resources based on existing ground monitoring terminals, build a unified ground monitoring system, and establish a data sharing mechanism between the ground monitoring terminals and the drone cluster;

[0010] Comprehensive governance unit, used to receive and integrate the monitoring data and its analysis results obtained from the drone scheduling unit and the ground monitoring integration unit, obtain the location information of abnormal situations, dispatch no less than one nearby drone to respond synchronously based on the location information, formulate a flight plan to control the drone to the designated location, and provide a live video feed of the scene in real time from all directions.

[0011] Furthermore, the drone cluster unit includes:

[0012] Data acquisition module, used to match corresponding drones from the drone cluster airport according to governance requirements for task deployment. After the drones take off, they conduct inspections and data acquisition on the target area through the carried high-definition cameras and sensors;

[0013] Drone cluster monitoring module, used to uniquely encode the drones, obtain the operation data and feedback data of each drone in real time based on each code, and monitor the operation status of the drones based on the carried drone sub-information data;

[0014] Drone cluster control module, used to obtain the corresponding terrain data and environmental data based on the monitoring ranges of each drone, determine the flight routes and waypoint action instructions of the drones based on the terrain data and environmental data, and generate corresponding control instructions based on the flight routes and waypoint action instructions to control each drone to respond;

[0015] Data transmission module, used to establish a data interaction channel with the comprehensive governance unit, and the drones transmit the collected video data and perception data to the comprehensive governance unit in real time based on the data interaction channel.

[0016] Further, the UAV cluster monitoring module monitors the operating status of UAVs, specifically including:

[0017] Establish the correspondence between UAV codes and UAV information, classify and integrate the real-time operating data of UAVs and UAV sub-information data based on the UAV codes, and generate at least one UAV monitoring sub-dataset;

[0018] Extract target data based on the data types of the UAV monitoring sub-datasets, perform clustering processing based on the data characteristics of the target data, and construct a UAV performance evaluation curve based on the time series and target values of the processed target data.

[0019] When there is a large difference between the UAV performance evaluation curve and the preset UAV performance evaluation curve, it is determined that the current UAV operating status is abnormal, and corresponding emergency response measures are triggered according to the severity of the abnormal situation.

[0020] Further, the UAV cluster control module determines the flight routes and waypoint action instructions of the UAVs, specifically as follows:

[0021] Clarify the inspection task objectives of the UAV cluster, including the monitoring scope, key inspection areas, and data collection requirements, obtain the terrain data of the inspection area based on GIS, including elevation information, geomorphic features, and obstacle distribution, and analyze the environmental factors of the inspection area;

[0022] According to the performance parameters of each UAV in the UAV cluster, configure corresponding sensors, payloads, and communication devices for each UAV according to the task requirements and terrain characteristics.

[0023] Based on the terrain data and environmental factors, plan the flight routes for each UAV, set key waypoints on the routes, and formulate detailed action instructions for each waypoint.

[0024] The UAVs execute the inspection tasks according to the planned routes and waypoint action instructions, collect and transmit data in real time. At the same time, monitor the flight status and data collection situation of the UAVs in real time, and dynamically adjust the flight routes and action instructions according to the actual situation.

[0025] Further, the UAV cluster control module determines the flight routes and waypoint action instructions of the UAVs, and also includes: analyzing the control and scheduling instructions issued by the comprehensive management unit received, determining the task priority, and making corresponding adjustments to the flight routes, action instructions, and task objectives of the UAV cluster according to the content of the control and scheduling instructions.

[0026] Further, the comprehensive management unit includes:

[0027] A data reading module, which is used to read the monitoring data collected by the drone and the video data collected by the ground monitoring, and integrate and standardize the monitoring data and video data;

[0028] An anomaly analysis module, which is used to identify the integrated data after standardization based on the target detection model, identify problems such as river floating objects, open burning, construction waste, muck trucks, and illegal occupation of natural resources based on governance requirements, locate the identified problems to determine the location information of the problems, and evaluate their severity and scope of influence;

[0029] A drone scheduling module, which is used to dispatch nearby drones for inspection and monitoring according to the identified problem types and location information, and formulate a flight plan based on the evaluation results;

[0030] A real-time feedback module, which is used to transmit the on-site data collected by the drone to the comprehensive governance unit in real time and display it in real time through a display terminal, so that relevant personnel can remotely monitor the situation of the problem site.

[0031] Further, the identification of problems such as river floating objects, open burning, construction waste, muck trucks, and illegal occupation of natural resources based on governance requirements specifically includes:

[0032] Load a pre-trained target detection model, and input the standardized image data into the target detection model for image recognition and feature extraction;

[0033] Based on the feature extraction results, analyze and screen out the color and shape of the target data, and judge whether the target data is located in the river or water area for the identification of river floating objects;

[0034] Based on the feature extraction results, analyze and screen out the color and brightness features of the flame, analyze whether there is relevant data in the continuous frame images, and identify open burning based on the dynamic change features of the relevant data;

[0035] Based on the feature extraction results, analyze and screen out the shape and texture features of the construction waste, extract the location information of the shape and texture features of the construction waste, and judge whether it is located in the construction site and related areas;

[0036] Based on the feature extraction results, analyze and screen out the shape, size, and color features of the muck truck for identification, and extract the license plate number for confirmation to judge whether it is a muck truck;

[0037] Obtain the historical image data of this location based on the geographical information data and time stamp of the image data, compare the image data with the historical image data, and identify whether there are illegal construction and natural resource destruction behaviors at this location.

[0038] Further, the anomaly analysis module identifies problems with river floating objects, specifically including:

[0039] Extract target image data corresponding to the characteristics of the river channel image data from the image data. After the target image data is extracted, obtain the pixel values corresponding to the target image data, and calculate the connected region area of the target image based on the pixel values corresponding to the target image.

[0040] Obtain a preset area threshold, and remove the connected regions in the connected region area that are smaller than the preset area threshold.

[0041] Based on the removal result, complete the filtering process of the target image data, perform image recognition on the filtered target image data, and determine whether there are river floating objects in the target image data.

[0042] Compared with the prior art, the beneficial effects of the present invention are:

[0043] Through the UAV cluster unit, comprehensive and efficient urban inspection and governance are realized. The UAV cluster has a wide coverage, can obtain real-time operation status data, ensures stable and reliable task execution. The ground monitoring integration unit cooperates with the UAV cluster to improve governance efficiency and accuracy. The comprehensive governance platform can quickly respond to abnormal situations, dispatch UAVs for on-site live broadcast, provide strong support for emergency response, realize comprehensive monitoring, data analysis and emergency response of the urban environment, and effectively improve the intelligent and refined level of smart city comprehensive governance. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 It is the schematic diagram of the modules of the integrated air, space and ground smart city comprehensive governance platform of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0045] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0046] To solve the technical problems that traditional ground monitoring means often have blind spots, especially in complex terrains or areas with dense high-rise buildings, it is difficult to achieve full-round monitoring, and the response speed of ground monitoring is limited by factors such as traffic and personnel scheduling, unable to quickly reach the scene for disposal, and unable to achieve comprehensive and efficient inspection of urban areas, please refer to Figure 1 , this embodiment provides the following technical solutions:

[0047] The integrated air, space and ground smart city comprehensive governance platform includes:

[0048] The UAV cluster unit is used to deploy at least one UAV in the city to form a UAV cluster, determine the detection range of each UAV, where the detection ranges cover each other, plan the take-off, landing, and cruise mission instructions of the UAV cluster, and control the UAV cluster to execute various tasks according to the mission instructions;

[0049] The UAV cluster unit is also used to obtain the operation status data of each UAV in real time, determine the endurance capacity, payload capacity, and flight execution stability data of each UAV based on the operation status data, judge whether the UAV is abnormal based on the operation status data of each UAV, and give early warnings and take emergency measures in a timely manner;

[0050] In this embodiment, the UAV cluster unit includes:

[0051] The data acquisition module is used to match the corresponding UAVs from the UAV cluster airport according to the governance requirements for task deployment. After the UAVs take off, they conduct inspections and data collection on the target area through the equipped high-definition cameras and sensors;

[0052] The UAV cluster monitoring module is used to uniquely encode the UAVs, obtain the operation data and feedback data of each UAV in real time based on the respective codes. The operation data includes key parameters such as flight altitude, speed, heading, battery power, etc., and the feedback data such as sensor readings, image or video information, etc. It monitors the operation status of the UAVs based on the carried UAV sub-information data, and the sub-information data includes its configuration information, historical maintenance records, task execution records, etc.;

[0053] The UAV cluster control module is used to obtain the corresponding terrain data and environmental data based on the monitoring ranges of the UAVs, determine the flight routes and waypoint action instructions of the UAVs based on the terrain data and environmental data, and generate corresponding control instructions based on the flight routes and waypoint action instructions to control each UAV to respond;

[0054] The data transmission module is used to establish a data interaction channel with the comprehensive governance unit, and the UAVs transmit the collected video data and perception data to the comprehensive governance unit in real time based on the data interaction channel;

[0055] In this embodiment, the data acquisition module realizes precise inspection and real-time data acquisition of the target area, improving the efficiency and accuracy of governance; the UAV cluster monitoring module ensures the stable operation of UAVs, and through unique coding and real-time data monitoring, effectively prevents the occurrence of faults and accidents; the UAV cluster control module intelligently plans flight routes based on terrain and environmental data, enhancing the collaborative operation ability of the UAV cluster; the data transmission module ensures the real-time transmission and sharing of data, providing strong data support for the comprehensive governance unit; effectively improving the intelligent level of the comprehensive governance of the smart city and enhancing the governance effect and efficiency;

[0056] The ground monitoring integration unit is used to integrate existing ground monitoring resources based on existing ground monitoring terminals, construct a unified ground monitoring system, establish a data sharing mechanism between the ground monitoring terminals and the UAV cluster, realize real-time interaction and collaborative analysis of monitoring data, and improve the efficiency and accuracy of comprehensive governance;

[0057] The comprehensive governance unit is used to receive and integrate the monitoring data and its analysis results obtained from the UAV scheduling unit and the ground monitoring integration unit, obtain the location information of abnormal situations, and based on the location information, dispatch no less than one nearby UAV to respond synchronously, formulate a flight plan to control the UAV to the designated location, and provide a live broadcast picture of the scene in real time and all directions to support emergency response.

[0058] In this embodiment, the UAV cluster unit is responsible for deploying UAVs in the city, planning their tasks and executing them, and at the same time, monitoring the operating status of UAVs in real time to ensure their normal operation. The ground monitoring integration unit realizes data sharing and collaborative analysis with the UAV cluster by integrating ground monitoring resources. The comprehensive governance unit is responsible for receiving and integrating the data obtained from the UAV cluster and ground monitoring resources, performing abnormal analysis, and dispatching UAVs for emergency response, and making corresponding adjustments to the flight routes, action instructions, and task objectives of the UAV cluster according to the instruction content to achieve more flexible and efficient comprehensive governance, realizing comprehensive monitoring, data analysis, and emergency response of the urban environment, and providing strong support for the construction and management of the smart city.

[0059] In this embodiment, the UAV cluster monitoring module monitors the operating status of UAVs, specifically including:

[0060] Establish the correspondence between UAV coding and UAV information, classify and integrate the real-time operating data of UAVs and UAV sub-information data based on the coding of UAVs, and generate no less than one UAV monitoring sub-dataset;

[0061] Extract target data based on the data types of the UAV monitoring sub-datasets, perform clustering processing based on the data characteristics of the target data, and construct a UAV performance evaluation curve based on the time series and target values of the processed target data, such as flight speed, stability, power consumption, etc.;

[0062] When there is a large difference between the UAV performance evaluation curve and the preset UAV performance evaluation curve, it is determined that the current UAV operating state is abnormal, and corresponding emergency response measures are triggered according to the severity of the abnormal situation;

[0063] In this embodiment, the UAV cluster control module determines the flight route and waypoint action instructions of the UAV, specifically:

[0064] Clarify the inspection task objectives of the UAV cluster, including the monitoring scope, key inspection areas, and data collection requirements. Obtain the terrain data of the inspection area based on GIS, including elevation information, geomorphic features, and obstacle distribution. Analyze the environmental factors of the inspection area, such as meteorological conditions, wind speed, wind direction, rainfall, etc., electromagnetic environment, signal interference, communication quality, etc., and possible safety risks, such as no-fly zones, sensitive areas, etc.;

[0065] According to the performance parameters of each UAV in the UAV cluster, such as flight speed, payload capacity, endurance, stability, etc., evaluate its ability to adapt to different terrain and environmental conditions, and configure corresponding sensors, payloads, and communication equipment for each UAV according to the task requirements and terrain characteristics;

[0066] Based on the terrain data and environmental factors, plan the flight route for each UAV. The route should avoid obstacles and sensitive areas, and at the same time ensure that the UAV can efficiently cover the target area. Set key waypoints on the route, and formulate detailed action instructions for each waypoint, including shooting angles and data collection methods. The instructions are designed specifically according to the terrain characteristics and task requirements;

[0067] The UAV executes the inspection task according to the planned route and waypoint action instructions, collects and transmits data in real time. At the same time, monitor the flight status and data collection situation of the UAV in real time, and dynamically adjust the flight route and action instructions according to the actual situation.

[0068] In this embodiment, the UAV cluster monitoring module effectively monitors the UAV performance by encoding and integrating UAV data, discovers anomalies in a timely manner and triggers emergency responses to ensure the stable operation of the UAV. The UAV cluster control module plans efficient routes and action instructions for the UAV based on task requirements, terrain, and environmental factors, improving the inspection efficiency and data collection quality, enhancing the coordination and intelligence level of the UAV cluster in the comprehensive governance of smart cities, and contributing to the realization of precise and efficient urban governance.

[0069] In this embodiment, the UAV cluster control module determines the flight route and waypoint action instructions of the UAVs, and further includes: analyzing the control and scheduling instructions issued by the comprehensive management unit received, determining the task priority, and making corresponding adjustments to the flight route, action instructions, and task objectives of the UAV cluster according to the content of the control and scheduling instructions.

[0070] In this embodiment, the comprehensive management unit includes:

[0071] A data reading module, which is used to read the monitoring data collected by the UAVs and the video data collected by the ground monitoring, and integrate and standardize the monitoring data and video data;

[0072] An anomaly analysis module, which is used to identify the integrated data after standardization based on the target detection model, identify problems such as river floating objects, open-air burning, construction waste, muck trucks, and illegal occupation of natural resources based on the governance requirements, locate the identified problems to determine the location information of the problems, and evaluate their severity and scope of influence;

[0073] A UAV scheduling module, which is used to dispatch nearby UAVs for inspection and monitoring according to the identified problem types and location information, and formulate a flight plan based on the evaluation results, including parameters such as flight altitude, speed, and shooting angle, in order to obtain the best on-site data of the problems;

[0074] A real-time feedback module, which is used to transmit the on-site data collected by the UAVs to the comprehensive management unit in real time and display it in real time through a display terminal, so that relevant personnel can remotely monitor the situation of the problem site in order to make decisions and responses in a timely manner;

[0075] In this embodiment, the identification of problems such as river floating objects, open-air burning, construction waste, muck trucks, and illegal occupation of natural resources based on the governance requirements specifically includes:

[0076] Loading a pre-trained target detection model, and inputting the standardized image data into the target detection model for image recognition and feature extraction;

[0077] Analyzing and screening the color and shape of the target data based on the feature extraction results, and judging whether the target data is located in a river or water area for the identification of river floating objects;

[0078] Analyzing and screening the color and brightness characteristics of the flame based on the feature extraction results, analyzing whether there is relevant data in the consecutive frame images, and identifying open-air burning based on the dynamic change characteristics of the relevant data;

[0079] Based on the analysis of the feature extraction results, the shape and texture features of construction waste are screened out, the location information of the shape and texture features of the construction waste is extracted, and it is judged whether it is located in the construction site and related areas;

[0080] Based on the analysis of the feature extraction results, the shape, size and color features of the muck truck are screened out for recognition, and the license plate number is extracted for confirmation to judge whether it is a muck truck;

[0081] Based on the geographical information data and time stamp of the image data, the historical image data of the location is obtained, and the image data is compared with the historical image data to identify whether there are illegal construction and natural resource damage behaviors at the location;

[0082] In this embodiment, the anomaly analysis module identifies the problem of river floating objects, specifically including:

[0083] Extract the target image data corresponding to the river channel image data features from the image data. After the target image data is extracted, obtain the pixel values corresponding to the target image data, and calculate the connected region area of the target image according to the pixel values corresponding to the target image;

[0084] Obtain the preset area threshold, and remove the connected regions with an area smaller than the preset area threshold in the connected region area;

[0085] Based on the removal result, complete the filtering process of the target image data, perform image recognition on the filtered target image data, and judge whether there are river floating objects in the target image data.

[0086] In this embodiment, the flight route and mission objectives are adjusted in real time according to the dispatching instructions of the comprehensive management unit, which improves the flexibility and response speed of the UAV cluster; the comprehensive management unit realizes the integration, analysis and application of the data collected by the UAVs through functional modules such as data reading, anomaly analysis, UAV dispatching and real-time feedback, effectively improving the efficiency and accuracy of urban governance; at the same time, through the recognition and feature extraction of the image data by the target detection model, problems such as river floating objects, open burning, construction waste, muck trucks and illegal occupation of natural resources can be accurately identified, and according to the actual situation, the investigation of the human settlement environment, emergency response, hidden danger investigation, public security police patrol, emergency search and rescue, sewage discharge detection, sewage outlet monitoring, forest fire prevention inspection, pest monitoring and other problems can be expanded, providing strong data support for urban governance; it improves the intelligent and refined level of the comprehensive management of smart cities and provides a strong guarantee for the sustainable development of cities.

[0087] The above are only the preferred specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention, according to the technical solution and inventive concept of the present invention, making equivalent substitutions or changes, shall be covered by the protection scope of the present invention.

Claims

1. The integrated air-ground-ground smart city management platform is characterized by: include: A drone cluster unit is used to deploy at least one drone in the city to build a drone cluster, determine the detection range of each drone, wherein the detection ranges overlap each other, plan the task instructions of the drone cluster, and control the drone cluster to perform various tasks according to the task instructions; The drone cluster unit is also used to obtain the operating status data of each drone in real time, determine the endurance, load capacity and flight execution stability data of each drone based on the operating status data, judge whether the drone is abnormal based on the operating status data of each drone, and issue early warnings and take emergency measures in a timely manner; The ground monitoring integration unit is used to integrate existing ground monitoring resources based on existing ground monitoring terminals, build a unified ground monitoring system, and establish a data sharing mechanism between ground monitoring terminals and drone clusters; The comprehensive management unit is used to receive and integrate the monitoring data and analysis results obtained from the drone dispatch unit and the ground monitoring integration unit, obtain the location information of abnormal situations, dispatch at least one nearby drone to respond synchronously based on the location information, formulate a flight plan to control the drone to the designated location, and provide full-scale real-time feedback of live broadcast images.

2. The integrated air-ground-integrated smart city management platform as claimed in claim 1, characterized in that: Drone swarm unit, including: The data collection module is used to match the corresponding drones from the drone cluster airport for task deployment according to the governance needs. After the drone takes off, it patrols the target area and collects data through the high-definition cameras and sensors it carries; The drone cluster monitoring module is used to uniquely encode drones, obtain the operation data and feedback data of each drone in real time based on each code, and monitor the operation status of drones based on the carried drone sub-information data; The UAV cluster control module is used to obtain corresponding terrain data and environmental data based on the monitoring range of each UAV, determine the flight route and waypoint action instructions of the UAV based on the terrain data and environmental data, and generate corresponding control instructions based on the flight route and waypoint action instructions to control each UAV to respond; The data transmission module is used to establish a data interaction channel with the comprehensive management unit. The drone transmits the collected video data and perception data to the comprehensive management unit in real time based on the data interaction channel.

3. The integrated air-ground-integrated smart city management platform as claimed in claim 2, characterized in that: The drone cluster monitoring module monitors the operation status of drones, including: Establish the correspondence between drone codes and drone information, classify and integrate drone real-time operation data and drone sub-information data based on drone codes, and generate at least one drone monitoring sub-dataset; Extract target data based on the data type of the drone monitoring sub-dataset, perform clustering processing based on the data features of the target data, and construct a drone performance evaluation curve based on the time series and target values ​​of the processed target data; When the UAV performance evaluation curve differs greatly from the preset UAV performance evaluation curve, it is judged that the current operating state of the UAV is abnormal, and corresponding emergency response measures are triggered according to the severity of the abnormal situation.

4. The integrated air-ground-integrated smart city management platform as claimed in claim 2, characterized in that: The drone cluster control module determines the flight route and waypoint action instructions of the drone, specifically: Clarify the inspection mission objectives of the drone cluster, including monitoring scope, key inspection areas and data collection requirements; obtain terrain data of the inspection area based on GIS, including elevation information, landform features and obstacle distribution; and analyze environmental factors of the inspection area; According to the performance parameters of each drone in the drone cluster, and based on the mission requirements and terrain characteristics, each drone is equipped with corresponding sensors, payloads and communication equipment; Plan flight routes for each drone based on terrain data and environmental factors, set key waypoints on the route, and develop detailed action instructions for each waypoint; The drone performs inspection tasks according to the planned routes and waypoint action instructions, collects and transmits data in real time. At the same time, it monitors the flight status and data collection of the drone in real time, and dynamically adjusts the flight route and action instructions according to actual conditions.

5. The integrated air-ground-integrated smart city management platform as claimed in claim 4, characterized in that: The drone cluster control module determines the flight route and waypoint action instructions of the drone, and also includes: analyzing the control scheduling instructions received from the comprehensive management unit, determining the task priority, and making corresponding adjustments to the flight route, action instructions and task objectives of the drone cluster according to the content of the control scheduling instructions.

6. The integrated air-ground-space smart city management platform as claimed in claim 1, characterized in that: Comprehensive governance unit, including: The data reading module is used to read the monitoring data collected by the drone and the video data collected by the ground monitoring, and integrate and standardize the monitoring data and video data; The anomaly analysis module is used to identify the standardized integrated data based on the target detection model, identify the problems of floating objects in rivers, open-air burning, construction waste, muck trucks, and illegal occupation of natural resources based on the governance requirements, locate the identified problems, determine the location information of the problems, and evaluate the severity and scope of impact; The drone dispatching module is used to dispatch nearby drones for inspection and monitoring based on the identified problem type and location information, and formulate flight plans based on the evaluation results; The real-time feedback module is used to transmit the on-site data collected by the drone to the comprehensive management unit in real time, and display it in real time through the display terminal, so that relevant personnel can remotely monitor the situation at the problem site.

7. The integrated air-ground-integrated smart city management platform as claimed in claim 6, characterized in that: The above-mentioned issues of identifying floating objects in rivers, open-air burning, construction waste, muck trucks and illegal occupation of natural resources based on governance requirements include: Load the pre-trained object detection model and input the standardized image data into the object detection model for image recognition and feature extraction; Based on the feature extraction results, the color and shape of the target data are screened out to determine whether the target data is located in a river or water area, and to identify floating objects in the river; Based on the feature extraction results, the color and brightness features of the flame are screened out, and the continuous frame images are analyzed to see whether there is relevant data. Based on the dynamic change characteristics of the relevant data, open-air burning is identified; Based on the feature extraction result analysis, the shape and texture features of the construction waste are screened out, and the location information of the shape and texture features of the construction waste is extracted to determine whether it is located at the construction site and related areas; Based on the feature extraction results, the shape, size and color features of the muck truck are screened out for identification, and the license plate number is extracted for confirmation to determine whether it is a muck truck; Based on the geographic information data and timestamp of the image data, the historical image data of the location is obtained, and the image data is compared with the historical image data to identify whether there is any illegal construction or destruction of natural resources at the location.

8. The integrated air-ground-integrated smart city management platform as claimed in claim 6, characterized in that: The anomaly analysis module identifies the problem of floating objects in the river, specifically including: Extracting target image data corresponding to the river channel image data features from the image data, obtaining pixel values ​​corresponding to the target image data after the target image data are extracted, and calculating the connected region area of ​​the target image according to the pixel values ​​corresponding to the target image; Obtain a preset area threshold, and remove connected domains whose area is smaller than the preset area threshold in the connected domain; Based on the removal result, filtering processing is completed on the target image data, and image recognition is performed on the filtered target image data to determine whether there are river floating objects in the target image data.

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