Urban comprehensive management and supervision method based on unmanned aerial vehicle airport cluster

Abstract

The invention discloses a method for comprehensively managing and supervising a city based on an unmanned aerial vehicle airport cluster, and relates to the technical field of unmanned aerial vehicles. The problems that an existing unmanned aerial vehicle system cannot comprehensively manage a city, information circulation among different management and supervision departments is not smooth, data sharing is difficult, and an efficient and cooperative working mode is difficult to form when city abnormal conditions are handled are solved. According to the invention, through analysis of geographic information and historical abnormal data, unmanned aerial vehicles are accurately deployed, efficient monitoring and ground resource integration are realized, environment data are obtained, strategies are formulated, environmental influences are evaluated, and comprehensive management data are processed, so that the urban state is comprehensively and intelligently judged; a data sharing platform is established, abnormal information is pushed, all departments respond quickly, an intelligent scheduling platform adjusts unmanned aerial vehicle tasks in time, cooperative emergency of multiple departments is achieved, the adaptability to a complex environment is enhanced, the emergency response speed and the cooperative effect are remarkably improved, and safe and stable operation of a city is effectively guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicles, and particularly to a method for urban comprehensive management and supervision based on an unmanned aerial vehicle airport cluster. Background Art

[0002] Traditional urban management and supervision means rely on a large amount of manpower, with low efficiency and difficulty in achieving full coverage, and the response speed to sudden and emergency major events is relatively slow. For example, the patent application with the publication number CN113313006B discloses a method, system and storage medium for urban illegal construction supervision based on unmanned aerial vehicles, including using unmanned aerial vehicles to conduct aerial photography of the area to be supervised regularly to obtain an aerial photo set of the area to be supervised; processing the aerial photo set to obtain a sliced image set of the area to be supervised; wherein, the aerial photo set includes the original aerial images of the area to be supervised at multiple supervision times respectively; extracting features from the sliced image set to obtain a vector feature layer of the area to be supervised at each supervision time respectively and displaying it; judging whether there are illegal buildings in the area to be supervised according to the vector feature layers at all supervision times, and when there are illegal buildings in the area to be supervised, sending out an illegal construction prompt message and displaying it. This patent application automatically judges whether there is illegal construction, generates an illegal construction prompt message in a timely manner and displays it, with a short discovery cycle, high efficiency, low labor cost, and good supervision effect, which is conducive to the orderly planning of the city.

[0003] However, the existing unmanned aerial vehicle systems cannot conduct comprehensive management of the city. There are problems such as limited monitoring range and single perspective in ground monitoring resources, which cannot meet the real-time and dynamic monitoring requirements of the complex urban environment. At the same time, the information flow between different management and supervision departments is not smooth, and data sharing is difficult, making it difficult to form an efficient collaborative working mode when dealing with urban anomalies. Summary of the Invention

[0004] The purpose of the present invention is to provide a method for urban comprehensive management and supervision based on an unmanned aerial vehicle airport cluster, which uses the unmanned aerial vehicle airport cluster and related information technologies to achieve comprehensive management and supervision of the city, can integrate resources, improve monitoring efficiency, and strengthen department cooperation, covering the cross-integration of geographic information technology, unmanned aerial vehicle application technology, data processing and sharing technology, and emergency management technology, and improve the intelligent and efficient level of urban management 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] A method for urban comprehensive management and supervision based on an unmanned aerial vehicle airport cluster, including:

[0007] Determine the main urban area and key areas of the target city to deploy an unmanned aerial vehicle airport cluster, and the unmanned aerial vehicle airport cluster integrates existing ground monitoring resources to collect comprehensive management data of the target city;

[0008] Process and integrate the comprehensively managed data collected in real time, and based on the results of the processing and integration, conduct real-time monitoring and analysis of the city to determine whether there are abnormalities in the target city;

[0009] Establish a data sharing platform with each management and supervision department of the target city, and based on the judgment results, combined with the data sharing platform, conduct resource scheduling and rapid response of each department when dealing with emergencies and major emergency events.

[0010] Furthermore, deploy a drone airport cluster, specifically including:

[0011] Obtain the geographical information data of the target city, determine the functional attributes of different regions of the city, analyze the historical emergency abnormal event data, and identify the frequency and harm degree of abnormal events that occurred in each region in the past;

[0012] Based on the geographical information data, conduct exploration and mapping of the geographical morphology of each region, determine the environmental characteristics of each region, evaluate the difficulty of drone flight and signal stability, screen out the regions suitable for drone deployment and operation, and determine them as the key regions for final deployment;

[0013] Within the determined key regions for deployment, combined with the environmental characteristics of the key regions and the drone flight radius requirements, determine the number of drones in the drone airport cluster, and establish a communication connection with the intelligent scheduling platform of the drone airport cluster.

[0014] Furthermore, the intelligent scheduling platform of the drone airport cluster is used to receive task requests from different departments, generate a flight plan for the drone airport cluster according to the task requests, and dynamically adjust the flight plan allocation of the drone group according to the current position coordinates and task priorities of the drones.

[0015] Furthermore, before the drones execute each flight plan, it also includes:

[0016] Obtain the corresponding flight plan based on the allocation result, extract the flight area and time data in the flight plan, match the extraction result with the flight restriction rules in the preset flight management database, and determine the corresponding flight area and time restriction rules;

[0017] Based on the corresponding flight area and time restriction rules, plan the flight route and task execution time of the drones at the current position, evaluate the battery life of the current drones, and provide feedback on the task execution status based on the evaluation result;

[0018] At the same time, according to the urgency of the task and the flight speed of the drones, allocate the execution time of each task stage in the flight plan.

[0019] Furthermore, the drone airport cluster integrates existing ground monitoring resources and also includes:

[0020] Based on existing ground monitoring resources, obtain real-time environmental data of the location of each drone airport cluster, and formulate corresponding flight plan response strategies based on real-time environmental data;

[0021] Based on the classification categories of real-time environmental data, classification and evaluation are performed in the trained environmental assessment model to determine whether the real-time environmental data has reached a level that affects the safe flight of the drone or the quality of data collection;

[0022] The corresponding flight plan response strategy is matched according to the UAV's performance parameters and mission plan, and the intelligent scheduling platform monitors the UAV status in real time and dynamically adjusts the response strategy.

[0023] Furthermore, it is determined whether the real-time environmental data has reached a level that affects the safe flight of the drone or the quality of data collection, including:

[0024] The collected real-time environmental data is traversed according to the characteristics of the parameters themselves, abnormal values that are obviously deviated from the normal range are identified, and the abnormal data are removed from the real-time environmental data to obtain normal environmental data;

[0025] Classifying the normal environment data to obtain meteorological parameter data and electromagnetic interference parameter data, and normalizing the meteorological parameter data and the electromagnetic interference parameter data;

[0026] The processed meteorological parameter data and electromagnetic interference parameter data are output to the environmental assessment model for evaluation. According to the evaluation results output by the model, the influencing parameter categories of the current real-time environmental data are clarified;

[0027] Determine the comprehensive impact of real-time environmental data on the safe flight of the UAV or the quality of data collection based on the impact parameter categories;

[0028] Furthermore, a data sharing platform will be established with multiple departments in the target cities, including:

[0029] In the data resource catalog of each management and supervision department in the target city, find the standard data type and format of each department, and determine the initial data characteristics of each department based on the standard data type and format of each department;

[0030] Determine the advanced data features in the initial data features based on the business characteristics and data usage habits of each department;

[0031] Obtain the data retrieval rules of each department, combine the advanced data features of each department, and generate the data transmission network protocol between the department and the data sharing platform;

[0032] Establish a connection architecture between the data sharing platform and the databases of various departments based on the data transmission network protocol, and establish a data sharing mechanism between the sharing platform and the databases of various departments.

[0033] Furthermore, the data sharing mechanism further includes:

[0034] Based on the data sharing mechanism, data resource samples from various departments are retrieved from the data sharing platform, and a data association analysis model is built to explore the inherent connections between data from different departments;

[0035] According to the identified data associations with strong correlation, corresponding data are retrieved from the databases of various departments based on the data transmission network protocol;

[0036] In the process of data retrieval, the operation results based on the data transmission network protocol are obtained, and based on the operation results, multiple working modes of each department and the correlation relationship under each working mode are analyzed, and based on the analysis results, the data transmission working mode of each department under different working modes and its corresponding target behavior characteristics are determined;

[0037] Perform feature extraction on the retrieved data based on the target behavior feature to obtain a first integrated feature, and perform gradient inversion processing on the first integrated feature to obtain a second integrated feature;

[0038] The integration effect of the corresponding data is evaluated according to the second integration feature, and the data sharing mechanism is optimized based on the evaluation result.

[0039] Furthermore, we will dispatch resources and respond quickly to various departments, including:

[0040] When an abnormal situation is determined in the target city, the data sharing platform immediately pushes the abnormal information and corresponding real-time comprehensive management data to various departments;

[0041] Based on the information received, each department quickly launches emergency response plans, mobilizes resources according to the type and impact of major emergencies and emergency events, and provides real-time feedback on rescue progress to each department through the data sharing platform.

[0042] Furthermore, if there are abnormal situations in the target city of the intelligent dispatching platform, the flight mission of the drone can be adjusted in real time according to the task requirements of various departments to obtain real-time monitoring data of the locations of sudden and emergency major events.

[0043] Compared with the prior art, the present invention has the following beneficial effects:

[0044] By analyzing geographical information and historical anomaly data, drones are precisely deployed to achieve efficient monitoring. Ground resources are integrated to obtain environmental data and formulate strategies, evaluate environmental impacts, and at the same time process comprehensive management data to comprehensively and intelligently judge the urban status. When an anomaly occurs, the anomaly information can be quickly pushed through the data sharing platform, and each department responds promptly. The intelligent dispatching platform adjusts the drone tasks in a timely manner to achieve multi-department collaborative emergency response, greatly improving the accuracy and efficiency of urban monitoring, enhancing the adaptability to complex environments, significantly increasing the emergency response speed and collaborative effect, effectively ensuring the safe and stable operation of the city, reducing the losses caused by various risks, and comprehensively improving the overall urban management level. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 It is a flowchart of a method for urban comprehensive management supervision based on a drone airport cluster of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0046] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with 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 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.

[0047] In order to solve the technical problems that the existing drone system cannot comprehensively manage a city, the information flow between different management and supervision departments is blocked, data sharing is difficult, and it is difficult to form an efficient collaborative working mode when dealing with urban anomalies, please refer to Figure 1 , the following technical solutions are provided in this embodiment:

[0048] A method for urban comprehensive management supervision based on a drone airport cluster includes:

[0049] Determine the main urban area and key areas of the target city (such as key township areas, etc., which are township areas with special management needs or key geographical locations), and deploy a drone airport cluster. The drone airport cluster integrates existing ground monitoring resources to collect comprehensive management data of the target city;

[0050] Process and integrate the real-time collected comprehensive management data, and conduct real-time monitoring and analysis of the city based on the processing and integration results to determine whether there are anomalies in the target city;

[0051] Establish a data sharing platform with each management and supervision department of the target city, and based on the judgment results, combined with the data sharing platform, conduct resource scheduling and rapid response of each department when dealing with sudden and emergency major events.

[0052] In this embodiment, in response to the supervision needs of the Agricultural and Rural Water Conservancy Bureau, drones equipped with high-definition cameras and image recognition algorithms are used to perform river inspection tasks. By analyzing the captured images, it is identified whether there is illegal sand mining or encroachment on the river; image recognition technology is used to accurately locate and identify floating objects in the river; drones are used to take low-altitude images of rural areas, and the human settlement environment problems such as garbage dumping and sewage discharge in the village are determined through analysis;

[0053] In response to the supervision needs of emergency management departments, when a disaster occurs, drones take off quickly in response to emergency commands, use cameras and sensors to collect data such as the scope of the disaster and trapped personnel, and provide a basis for the deployment of rescue forces through analysis of these data; in daily hidden danger inspections, key areas such as industrial clusters and hazardous chemical warehouses are photographed and inspected, and safety hazards such as damaged buildings and accumulation of flammable materials are identified through image analysis;

[0054] In response to the supervision needs of emergency management departments, when a disaster occurs, drones take off quickly in response to emergency commands, use cameras and sensors to collect data such as the scope of the disaster and trapped personnel, and provide a basis for the deployment of rescue forces through analysis of these data; in daily hidden danger inspections, key areas such as industrial clusters and hazardous chemical warehouses are photographed and inspected, and safety hazards such as damaged buildings and accumulation of flammable materials are identified through image analysis;

[0055] In response to the supervision needs of urban management departments, the thermal imaging and image recognition technology carried by drones is used to scan urban areas, and the location of open-air burning points is determined by analyzing thermal imaging and image data; image recognition technology is used to monitor the driving conditions of construction waste and muck trucks around construction sites, and illegal dumping of construction waste and muck trucks not following the prescribed route are identified by analyzing the captured images;

[0056] In response to the supervision needs of the public security department, drones perform security patrol tasks in urban streets and public places, and send back the shooting images in real time to assist in public security management; when patrolling in dangerous waters such as rivers and lakes, they use image recognition and intelligent algorithms to monitor whether someone is approaching the dangerous area, and issue timely warnings after confirmation; in emergency search and rescue, multiple drones take off from different directions to expand the search range, and determine the location information of people through analysis of the shooting images;

[0057] In response to the supervision needs of the ecological environment department, drones equipped with sensors are used to detect the concentration of water pollutants, and data is collected by flying over industrial enterprises and domestic sewage outlets. The detection data is analyzed to determine whether the sewage discharge meets the standards; illegal sewage outlets are identified through image recognition and data analysis, and the water quality and water quantity data of the sewage outlets are monitored and analyzed in real time;

[0058] In response to the supervision needs of the natural resources and planning departments, drones are used to regularly photograph and inspect natural resource areas such as cultivated land and forest land, and illegal land occupation, illegal mining and other behaviors are identified through image analysis; in forest areas, thermal imaging technology is used for forest fire prevention inspections, and forest fire hazards are determined through the analysis of thermal imaging data; crops and trees are photographed, and the pest and disease situation is determined through image analysis and professional algorithms.

[0059] When an emergency occurs, based on the intelligent scheduling platform of the drone airport cluster, multiple drones near the incident site are determined to respond synchronously, and the drones are controlled to take off quickly and reach the designated location, photograph and transmit on-site image information in real time from different angles and heights, and provide an intuitive basis for on-site command and dispatch by comprehensively feedbacking the on-site intelligence picture through the analysis of these image information.

[0060] In this embodiment, deploying a drone airport cluster specifically includes:

[0061] Obtain the geographical information data of the target city, including population density, building distribution, transportation network, etc., determine the functional attributes of different regions of the city, such as dividing industrial production areas, residential areas, commercial activity areas, nature reserves and transportation hub areas, etc., analyze the historical sudden abnormal event data, and identify the frequency and harm degree of abnormal events occurring in each region in the past.

[0062] Based on the geographical information data, conduct exploration and mapping of the geographical morphology of each region, including terrain undulation, water system distribution, vegetation coverage, etc., determine the environmental characteristics of each region, evaluate the difficulty of drone flight and signal stability, screen out the regions suitable for drone deployment and operation, ensure that the drones can fly stably, accurately obtain information and are not overly interfered by environmental factors when performing tasks, and determine them as the key regions for final deployment.

[0063] Within the determined key deployment regions, combine the environmental characteristics of the key regions and the drone flight radius requirements to determine the number of drones in the drone airport cluster, and establish a communication connection with the intelligent scheduling platform of the drone airport cluster.

[0064] The intelligent scheduling platform of the drone airport cluster is used to receive task requests from different departments, generate a flight plan for the drone airport cluster according to the task requests, and dynamically adjust the flight plan allocation of the drone group according to the current position coordinates and task priorities of the drones.

[0065] In this embodiment, before the drones execute each flight plan, it also includes:

[0066] Obtain the corresponding flight plan based on the allocation result, extract the flight area and time data in the flight plan, match the extraction result with the flight restriction rules in the preset flight management database to determine the corresponding flight area and time restriction rules;

[0067] Plan the flight route and mission execution time of the UAV at the current position based on the corresponding flight area and time restriction rules, evaluate the endurance ability of the current UAV, and provide feedback on the mission execution status based on the evaluation result;

[0068] Meanwhile, according to the urgency of the mission and the flight speed of the UAV, allocate the execution time for each mission stage in the flight plan to ensure that the entire supervision mission can be completed on time and efficiently;

[0069] For example, when planning a river inspection route, the algorithm takes into account the meandering shape of the river, the location of charging points along the way, and the flight restricted areas to generate an efficient inspection route, ensuring that the UAV can fully cover the river and complete the mission within the allowable battery range.

[0070] In this embodiment, the UAV airport cluster integrates existing ground monitoring resources and further includes:

[0071] Based on the existing ground monitoring resources, obtain the real-time environmental data of the locations where each UAV airport cluster is located, such as meteorological parameters such as temperature, humidity, wind speed, wind direction, rainfall, etc., and the magnetic interference intensity and frequency range, etc.; Based on the real-time environmental data, formulate corresponding flight plan response strategies;

[0072] Based on the classification categories of the real-time environmental data, conduct classification evaluation in the trained environmental evaluation model to determine whether the real-time environmental data such as meteorological parameters or electromagnetic interference reaches the level that affects the safe flight of the UAV or the quality of data collection;

[0073] Match the corresponding flight plan response strategy according to the performance parameters of the UAV and the mission plan, and take measures including but not limited to evaluating whether to suspend the flight mission, adjusting the flight altitude and speed, activating the protection mechanism, optimizing the flight route, changing the communication frequency, enhancing the signal processing ability, etc., and the intelligent scheduling platform monitors the status of the UAV in real time and dynamically adjusts the response strategy to ensure that the UAV can safely execute the supervision mission in a complex environment.

[0074] In this embodiment, when the rainfall reaches a certain threshold, the waterproof performance of the drone is evaluated to determine whether to suspend the flight mission. If the mission is to be continued, the flight altitude and speed are adjusted to prevent rain from having too much impact on the sensor and flight stability, and the frequency of data collection is increased to compensate for the loss of image and data quality caused by bad weather; when the dust concentration exceeds the set standard, the anti-dust protection mechanism of the drone is activated, such as closing unnecessary vents and adjusting the sensor viewing angle to reduce dust obstruction. At the same time, the flight altitude is lowered, ground buildings are used as shields, the flight route is optimized, and the drone is ensured to perform its mission safely; the wind speed and direction are monitored in real time. When the wind speed exceeds the drone's safe flight threshold, the flight attitude and route are adjusted according to the wind direction, and headwind or crosswind flight techniques are used to maintain the stability of the drone. If the wind is too strong to fly safely, the mission is suspended, and the flight is replanned when the wind weakens to a safe range;

[0075] When electromagnetic interference is detected, the type and strength of the interference source are analyzed first. If the interference source is a known fixed facility, such as a communication base station, substation, etc., the flight route of the drone is adjusted to avoid the strong interference area of the interference source. If the interference source is a mobile device or an unknown source, the anti-interference algorithm of the drone is activated, such as using frequency hopping communication technology to change the communication frequency to avoid conflict with the interference signal; enhance the signal processing capability of the drone, filter and correct the collected data to ensure the accuracy and integrity of the data. At the same time, the ground command center monitors the communication status and flight stability of the drone in real time, and adjusts the response measures in time according to the actual situation to ensure that the drone can continue to perform regulatory tasks in an electromagnetic interference environment.

[0076] In this embodiment, determining whether the real-time environmental data reaches a level that affects the safe flight of the drone or the quality of data collection specifically includes:

[0077] The collected real-time environmental data is traversed according to the characteristics of the parameters themselves, and abnormal values that are obviously deviated from the normal range are identified, and the abnormal data are removed from the real-time environmental data to obtain normal environmental data; for example, if the temperature is too high or too low and it is impossible to have such values under local climate conditions, these abnormal data are marked;

[0078] Classifying the normal environment data to obtain meteorological parameter data and electromagnetic interference parameter data, and normalizing the meteorological parameter data and the electromagnetic interference parameter data;

[0079] The processed meteorological parameter data and electromagnetic interference parameter data are output to the environmental assessment model for evaluation. The environmental assessment model is trained based on a large amount of historical environmental data and the associated data of the corresponding UAV flight status and data collection quality. According to the evaluation results output by the model, the influencing parameter categories of the current real-time environmental data are clarified;

[0080] Judge the comprehensive impact degree of real-time environmental data on the safe flight of the UAV or the quality of data collection based on the impact parameter category;

[0081] The comprehensive impact degree on the safe flight of the UAV or the quality of data collection is calculated by the following formula:

[0082]

[0083] Among them, n represents that there are n environmental factors affecting the UAV; I t represents the comprehensive impact degree; represents the current real-time monitoring value of the i-th environmental parameter data; represents the safety threshold of the i-th environmental parameter data; w i represents the impact degree weight of the i-th environmental parameter data, and

[0084] By calculating the impact degree of each single environmental factor on the safe flight of the UAV or the quality of data collection, and performing weighted summation on the impact degrees of each single factor, the comprehensive impact degree of all environmental factors on the UAV is obtained;

[0085] In this embodiment, the impact degrees of various complex and diverse environmental factors, such as meteorological conditions (wind speed, rainfall, etc.) and electromagnetic interference, on the safe flight of the UAV or the quality of data collection are calculated. The operator can quickly and accurately understand the overall impact of the current environment on the UAV operation. Compared with the traditional qualitative description, it greatly improves the accuracy and clarity of the evaluation, comprehensively and pertinently reflects the combined effects of various environmental factors, ensures that the evaluation results meet the actual needs, and the operator can take corresponding measures in a timely manner, such as changing the flight altitude, speed, adjusting the flight route, or even suspending the flight mission, effectively guaranteeing the safe operation of the UAV and the quality of data collection, improving the reliability and stability of the UAV operation, and reducing the risks caused by environmental factors.

[0086] In this embodiment, a data sharing platform is established with multiple departments in the target city, specifically including:

[0087] In the data resource catalogs of each management and supervision department in the target city, search for the standard data types and formats of each department. For example, the river monitoring data format of the Agriculture, Rural Water Conservancy and Fisheries Bureau, the disaster data format of the Emergency Management Department, etc.; according to the standard data types and formats of each department, determine the initial data characteristics of each department, such as the data volume size, data update frequency, etc.;

[0088] Combined with the business characteristics and data usage habits of each department, determine the advanced data characteristics in the initial data characteristics. For example, the high-frequency call requirement for real-time location data by the public security department in emergency search and rescue tasks corresponds to the advanced feature of the department's data;

[0089] Obtain the data retrieval rules of each department, such as the data access rights and data usage purposes of different departments, and generate the data transmission network protocol between the department and the data sharing platform based on the advanced data characteristics of each department;

[0090] Establish a connection architecture between the data sharing platform and the databases of various departments based on the data transmission network protocol, and establish a data sharing mechanism between the sharing platform and the databases of various departments.

[0091] In this embodiment, the data sharing mechanism further includes:

[0092] Based on the data sharing mechanism, data resource samples of various departments are retrieved from the data sharing platform, and a data association analysis model is built to explore the inherent connections between data from different departments. For example, the water quality monitoring data of the ecological environment department and the river management data of the Agricultural and Rural Water Conservancy Bureau may be associated with river areas, pollutant indicators, etc. The correlation coefficient of the construction site supervision data of the urban management department and the land use data of the natural resources and planning department in terms of project construction location, area, etc. is calculated to determine the strength of the correlation between the two;

[0093] According to the data associations with strong correlations identified, corresponding data are retrieved from the databases of various departments based on the data transmission network protocol. For example, when it is determined that the disaster relief data of the emergency management department and the personnel dispatch data of the public security department are highly correlated, the relevant data are retrieved from the databases of the two departments in a safe and efficient manner as specified by the data transmission network protocol;

[0094] During the data retrieval process, the operation results based on the data transmission network protocol are obtained, including key information such as whether the data transmission is successful and the transmission duration. Based on the operation results, multiple working modes of each department and the correlation relationship under each working mode are analyzed. Based on the analysis results, the data transmission working mode of each department under different working modes and its corresponding target behavior characteristics are determined. For example, the most suitable combination of data transmission frequency and data type is determined when the urban management department handles open-air burning incidents as its efficient working mode and target behavior characteristics;

[0095] Based on the target behavior characteristics, the retrieved data is feature extracted to obtain the first integrated feature, which reflects the basic characteristics of the retrieved data, such as the main trend, data distribution characteristics, etc. The first integrated feature is subjected to gradient inversion processing to obtain the second integrated feature. The second grid feature highlights the characteristics of the retrieved data from another perspective, such as strengthening the abnormal fluctuation characteristics and periodic change characteristics in the enhanced data.

[0096] Evaluate the integration effect of retrieving corresponding data according to the second integration feature, and optimize the data sharing mechanism based on the evaluation results. For example, if it is found that the integrated data is missing or inaccurate in some key indicators, adjust the data association recognition method or data retrieval strategy to improve the quality of data integration.

[0097] In this embodiment, by obtaining the operation results and determining the target behavior characteristics, it can be used to continuously improve the data transmission network protocol, optimize the data transmission process, improve the efficiency and stability of data sharing, ensure that relevant data of each department can be retrieved and processed more efficiently in subsequent data integration, and automatically adjust the data transmission method according to the department work mode, etc., to achieve efficient and stable data sharing.

[0098] In this embodiment, resource scheduling and rapid response for each department are carried out, specifically including:

[0099] When it is determined that there is an abnormal situation in the target city, the data sharing platform immediately pushes the abnormal information and the corresponding real-time comprehensive management data to each department. For example, if it is found that there is a fire in a certain area, the data sharing platform will simultaneously push the fire occurrence location, the size of the fire (obtained by analyzing the images taken by the drone), and the surrounding environment information (such as building distribution, road conditions, etc.) to the emergency management department, the public security department, and the urban management department;

[0100] Based on the received information, each department quickly activates the emergency plan, retrieves resources according to the event type and impact degree of the sudden and emergency major events, and feeds back the rescue progress to each department in real time through the data sharing platform;

[0101] When there is an abnormal situation in the target city of the intelligent scheduling platform, combined with the task requirements of each department, the flight tasks of the drones are adjusted in real time to obtain real-time monitoring data of the location of the sudden and emergency major events. For example: providing real-time on-site image monitoring for the rescue operations of the emergency management department, taking pictures of the fire scene from different angles to provide more comprehensive information for rescue command, and the urban management department using drones to inspect areas such as construction sites and garbage dumps around the fire that may have potential safety hazards to prevent secondary disasters from occurring.

[0102] In this embodiment, when the target city is abnormal, the data sharing platform can quickly push detailed abnormalities and real-time comprehensive management data to each department, realizing efficient information sharing and avoiding information lag and asymmetry. Based on this, each department quickly activates the emergency plan, reasonably retrieves resources, quickly responds to emergencies, improves the emergency efficiency, and controls the situation. At the same time, with the real-time feedback of the rescue progress through the data sharing platform, department collaboration is strengthened. The intelligent scheduling platform can also adjust the flight tasks of the drones in real time according to the department needs, accurately schedule resources, provide specific monitoring data for each department, and assist in rescue and potential hazard investigation.

[0103] As described above, it is only a preferred specific embodiment 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 of the present invention and its inventive concept, makes equivalent substitutions or changes, and should be covered within the protection scope of the present invention.

Claims

1. A method for urban comprehensive management and supervision based on a drone airport cluster, characterized in that Including: Determine the main urban area and key areas of the target city to deploy an unmanned aircraft airport cluster, and the unmanned aircraft airport cluster integrates existing ground monitoring resources to collect comprehensive management data of the target city; Process and integrate the comprehensively managed data collected in real time, and conduct real-time monitoring and analysis of the city based on the results of the processing and integration to determine whether there are abnormalities in the target city; Establish a data sharing platform with each management and supervision department of the target city, and based on the judgment results, combined with the data sharing platform, conduct resource scheduling and rapid response of each department when dealing with sudden and emergency major events.

2. The method for urban comprehensive management supervision based on a drone airport cluster according to claim 1, characterized in that Deploying an unmanned aircraft airport cluster specifically includes: Obtain the geographical information data of the target city, determine the functional attributes of different regions of the city, analyze the historical sudden abnormal event data, and identify the frequency and harm degree of abnormal events occurring in each region in the past; Based on the geographical information data, conduct exploration and mapping of the geographical forms of each region, determine the environmental characteristics of each region, evaluate the difficulty of unmanned aircraft flight and signal stability, screen out the regions suitable for unmanned aircraft deployment and operation, and determine them as the final key deployment regions; Within the determined key deployment regions, combine the environmental characteristics of the key regions and the requirements of the unmanned aircraft flight radius to determine the number of unmanned aircraft in the unmanned aircraft airport cluster, and establish a communication connection with the intelligent scheduling platform of the unmanned aircraft airport cluster.

3. The method for urban comprehensive management supervision based on a drone airport cluster according to claim 2, wherein, The intelligent scheduling platform of the unmanned aircraft airport cluster is used to receive task requests from different departments, generate a flight plan for the unmanned aircraft airport cluster according to the task requests, and dynamically adjust the flight plan allocation of the unmanned aircraft group according to the current position coordinates and task priorities of the unmanned aircraft.

4. The method for urban comprehensive management supervision based on an unmanned aerial vehicle airport cluster as claimed in claim 3, wherein, Before the unmanned aircraft executes each flight plan, it also includes: Obtain the corresponding flight plan based on the allocation result, extract the flight area and time data in the flight plan, and match the extraction result with the flight restriction rules in the preset flight management database to determine the corresponding flight area and time restriction rules; Based on the corresponding flight area and time restriction rules, plan the flight route and task execution time of the unmanned aircraft at the current position, evaluate the endurance of the current unmanned aircraft, and provide feedback on the task execution status based on the evaluation result; At the same time, according to the urgency of the task and the flight speed of the unmanned aircraft, allocate the execution time of each task stage in the flight plan.

5. The method for urban comprehensive management supervision based on an unmanned aerial vehicle airport cluster according to claim 4, wherein, The unmanned aircraft airport cluster integrates existing ground monitoring resources, and also includes: Based on the existing ground monitoring resources, obtain the real-time environmental data of the location of each unmanned aircraft airport cluster, and based on the real-time environmental data, formulate corresponding flight plan response strategies; Based on the classification categories of the real-time environmental data, conduct classification evaluation in the trained environmental evaluation model to determine whether the real-time environmental data reaches the level of affecting the safe flight of the unmanned aircraft or the quality of data collection; Match the corresponding flight plan response strategy according to the performance parameters and task plan of the unmanned aircraft, and the intelligent scheduling platform monitors the status of the unmanned aircraft in real time and dynamically adjusts the response strategy.

6. The method for urban comprehensive management supervision based on a drone airport cluster according to claim 5, wherein Determining whether the real-time environmental data reaches the level of affecting the safe flight of the unmanned aircraft or the quality of data collection specifically includes: The collected real-time environmental data is traversed according to the characteristics of the parameters themselves, abnormal values that are obviously deviated from the normal range are identified, and the abnormal data are removed from the real-time environmental data to obtain normal environmental data; Classifying the normal environment data to obtain meteorological parameter data and electromagnetic interference parameter data, and normalizing the meteorological parameter data and the electromagnetic interference parameter data; The processed meteorological parameter data and electromagnetic interference parameter data are output to the environmental assessment model for evaluation. According to the evaluation results output by the model, the influencing parameter categories of the current real-time environmental data are clarified; Based on the impact parameter categories, the comprehensive impact of real-time environmental data on the safe flight of UAVs or the quality of data collection is determined.

7. The method for urban comprehensive management supervision based on an unmanned aerial vehicle airport cluster according to claim 6, wherein, Establish a data sharing platform with multiple departments in target cities, including: In the data resource catalog of each management and supervision department in the target city, find the standard data type and format of each department, and determine the initial data characteristics of each department based on the standard data type and format of each department; Determine the advanced data features in the initial data features based on the business characteristics and data usage habits of each department; Obtain the data retrieval rules of each department, and generate the data transmission network protocol between the department and the data sharing platform based on the advanced data features of each department; Establish a connection architecture between the data sharing platform and the databases of various departments based on the data transmission network protocol, and establish a data sharing mechanism between the sharing platform and the databases of various departments.

8. The method for urban comprehensive management supervision based on an unmanned aerial vehicle airport cluster according to claim 7, wherein The data sharing mechanism further includes: Based on the data sharing mechanism, data resource samples from various departments are retrieved from the data sharing platform, and a data association analysis model is built to explore the inherent connections between data from different departments; According to the identified data associations with strong correlation, corresponding data are retrieved from the databases of various departments based on the data transmission network protocol; In the process of data retrieval, the operation results based on the data transmission network protocol are obtained, and based on the operation results, multiple working modes of each department and the correlation relationship under each working mode are analyzed, and based on the analysis results, the data transmission working mode of each department under different working modes and its corresponding target behavior characteristics are determined; Perform feature extraction on the retrieved data based on the target behavior feature to obtain a first integrated feature, and perform gradient inversion processing on the first integrated feature to obtain a second integrated feature; The integration effect of the corresponding data is evaluated according to the second integration feature, and the data sharing mechanism is optimized based on the evaluation result.

9. The method for urban comprehensive management supervision based on an unmanned aerial vehicle airport cluster as claimed in claim 8, wherein, Carry out resource dispatch and rapid response of various departments, including: When an abnormal situation is determined in the target city, the data sharing platform immediately pushes the abnormal information and corresponding real-time comprehensive management data to various departments; Based on the information received, each department quickly launches emergency response plans, mobilizes resources according to the type and impact of major emergencies and emergency events, and provides real-time feedback on rescue progress to each department through the data sharing platform.

10. The method for urban comprehensive management supervision based on an unmanned aerial vehicle airport cluster according to claim 9, wherein If there are abnormal situations in the target city of the intelligent dispatching platform, the flight mission of the drone can be adjusted in real time according to the task requirements of various departments to obtain real-time monitoring data of the locations of sudden and emergency major events.

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