Aircraft information acquisition service design method

Abstract

The invention relates to the technical field of urban infrastructure management, in particular to an aircraft information acquisition service design method, which comprises the following steps of: planning a task; collecting data; and data processing and information delivery. According to user requirements, the processed information is delivered to urban managers in a standardized report, GIS map or real-time data transmission mode, and accurate and real-time infrastructure monitoring information is provided for decision support. Through the method, the efficiency and accuracy of urban infrastructure management can be effectively improved, the emergency response capability is enhanced, and the method has important application value.

Description

Technical Field

[0001] The present invention relates to the technical field of urban infrastructure management, and in particular to a method for designing aircraft intelligence collection services. Background Art

[0002] With the rapid development of information technology and data-driven technology, aircraft, as a flexible and efficient intelligence collection tool, has been continuously expanded in application, covering many fields from military reconnaissance, civilian monitoring to scientific research and exploration. These applications all rely on high-quality intelligence data generated by aircraft. However, existing intelligence collection and generation services have obvious limitations when facing complex mission scenarios, and it is difficult to meet the demand for accurate, dynamic, and high-decision-making value intelligence.

[0003] At present, traditional intelligence generation models mostly focus on the initial stage of data collection and lack the ability to deeply integrate the entire chain from data to intelligence. Although the popularity of multimodal sensor technology (such as optical cameras, lidar, and infrared thermal imaging) enables aircraft to collect a large amount of heterogeneous data, these data are usually unstructured and multi-source heterogeneous, resulting in many technical challenges in data cleaning, fusion, and intelligent analysis. In addition, the existing intelligence generation process usually adopts a static fixed mode and lacks dynamic adaptability, showing problems of inefficiency and lack of accuracy when responding to changing mission requirements.

[0004] At the same time, the limitations of intelligence delivery methods also restrict the application value of aircraft intelligence generation services. At present, most services are mainly in the form of static reports or raw data, which cannot meet users' needs for real-time, visualization and interactive analysis. This single delivery method limits the practicality of intelligence products and may also lead to a lack of timely and intuitive support in the evaluation and decision-making process. In addition, the intelligence generation process requires not only comprehensive data collection and integration capabilities, but also a deep transformation from information to intelligence, which involves the fusion optimization of multimodal data, the application of intelligent analysis algorithms, and the realization of dynamic mission planning. However, in the key links of multi-source data fusion, complex scene adaptation and intelligence visualization delivery, the current technical solutions are still imperfect and it is difficult to support the needs of efficient and accurate intelligence generation.

[0005] Therefore, an innovative design method for aircraft intelligence collection services is urgently needed. Summary of the invention

[0006] The present invention provides a method for designing aircraft intelligence collection services, which should be able to achieve collaborative optimization of the entire chain from mission planning to intelligence delivery, integrate multimodal sensor technology in the data collection stage, apply intelligent analysis algorithms in the data processing link, and provide diversified, real-time, high-decision-making value information in the intelligence delivery stage. At the same time, the method should have high flexibility and customization capabilities to adapt to the specific needs of different fields and scenarios, thereby improving the quality and efficiency of intelligence services, promoting the widespread application and development of aircraft intelligence generation technology, and solving existing problems.

[0007] The present invention provides an aircraft intelligence collection service design method using the following technical solutions, including: Take urban infrastructure as the mission target and clarify intelligence requirements based on the mission target; select the type of aircraft and the sensor equipment it carries based on the intelligence requirements, and use the flight path optimization algorithm to generate the optimal flight route for the aircraft; Collect multimodal data of urban infrastructure while the aircraft is carrying out its mission along the optimal flight route. The multimodal data includes: high-definition image data, radar signal data, and infrared imaging data; Denoising the multimodal data to obtain target multimodal data, and fusing the target multimodal data to obtain fused data; Use deep learning algorithms and artificial intelligence algorithms to analyze fused data, identify targets in the fused data, and classify and track targets; Delivers target location, target category, and tracking data as reconnaissance data for intelligence delivery.

[0008] Preferably, urban infrastructure includes: roads and bridges, buildings, water supply and drainage pipelines and power facilities.

[0009] Preferably, intelligence requirements include: target mission area scope, data type, collection accuracy and timeliness.

[0010] Preferably, the aircraft is selected according to the scope of the target mission area, wherein the aircraft includes: a quad-rotor drone, a fixed-wing aircraft and a high-altitude long-flight aircraft.

[0011] Preferably, the steps of collecting multimodal data of urban infrastructure during the process of the aircraft performing a mission along the optimal flight route are: The aircraft is equipped with sensor equipment, including high-definition optical cameras, radar systems and infrared thermal imagers; Among them, high-definition optical cameras are used to collect high-definition image data of roads and bridges; radar systems are used to collect three-dimensional modeling data of buildings; and infrared thermal imagers are used to collect infrared imaging data of water supply and drainage pipelines and power facilities.

[0012] Preferably, the step of denoising the multimodal data to obtain target multimodal data is: Denoising the high-definition image data based on the non-local mean to obtain the target high-definition image data; De-noising the radar signal data based on wavelet transform to obtain target radar signal data; The infrared imaging data is denoised based on median filtering to obtain the target infrared imaging data.

[0013] Preferably, the step of fusing the target multimodal data to obtain fused data is: The target high-definition image data and the target radar signal data are integrated to obtain an accurate three-dimensional structural model of the urban infrastructure. The accurate three-dimensional structural model is used to analyze the deformation trend of bridges and the structural stability of buildings. Based on the accurate three-dimensional structural model and combined with the target infrared imaging data, the positioning data of hidden danger facilities in urban infrastructure are obtained, and the positioning data of hidden danger facilities are used as fusion data.

[0014] Preferably, the steps of analyzing the fused data using a deep learning algorithm and an artificial intelligence algorithm, identifying targets in the fused data, and classifying and tracking the targets are: The deep learning algorithm uses a convolutional neural network model, and the artificial intelligence algorithm uses a target tracking algorithm; Analyze the fused data and track the target through the target tracking algorithm; The convolutional neural network model is used to identify target areas of bridge cracks, road potholes or pipeline leakage in the fused data, and the target areas are classified and labeled.

[0015] Preferably, the forms of intelligence delivery include: standardized reports, interactive maps and real-time data visualization; wherein, during the intelligence delivery process, data is transmitted through an encrypted communication link to ensure the security of the data during transmission, and support mobile device access and historical data analysis.

[0016] An aircraft intelligence collection service design system, comprising: The mission planning module is used to take the city infrastructure as the mission target and clarify the intelligence requirements according to the mission target; select the aircraft type and the sensor equipment it carries according to the intelligence requirements, and use the flight path optimization algorithm to generate the optimal flight route for the aircraft; The data acquisition module is used to collect multimodal data of urban infrastructure when the aircraft is carrying out its mission according to the optimal flight route. The multimodal data includes: high-definition image data, radar signal data and infrared imaging data; The data processing module is used to denoise the multimodal data to obtain the target multimodal data, and to fuse the target multimodal data to obtain fused data; to analyze the fused data using deep learning algorithms and artificial intelligence algorithms, to identify the targets in the fused data, and to classify and track the targets; Intelligence delivery module, used to deliver target location, target category, and tracking data as reconnaissance data for intelligence delivery.

[0017] The beneficial effects of the present invention are: First, through mission planning, the target mission requirements are clarified, the appropriate aircraft and sensor equipment are selected, and the flight path is optimized; secondly, the aircraft integrates multimodal sensors to collect high-precision data, and realizes dynamic monitoring and adjustment to ensure the integrity and accuracy of data collection; then, the collected data is cleaned, noise is eliminated, and multimodal data is fused to generate a comprehensive state model of urban infrastructure, and target detection and dynamic change monitoring are performed through deep learning algorithms. Finally, according to user needs, the processed intelligence is delivered to city managers in the form of standardized reports, GIS maps or real-time data transmission, providing accurate and real-time infrastructure monitoring information for decision support. This method can effectively improve the efficiency and accuracy of urban infrastructure management and strengthen emergency response capabilities, which has important application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0019] Figure 1 A flowchart of an embodiment of an aircraft intelligence collection service design method of the present invention; Figure 2 A system block diagram of an aircraft intelligence collection service design system of the present invention. DETAILED DESCRIPTION

[0020] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0021] An exemplary embodiment of an aircraft intelligence collection service design method of the present invention is as follows: Figure 1 As shown, including: S1. Mission planning; Specifically, the urban infrastructure is taken as the mission target, and the intelligence requirements are clarified according to the mission target; the type of aircraft and the sensor equipment it carries are selected according to the intelligence requirements, and the flight path optimization algorithm is used to generate the optimal flight route for the aircraft.

[0022] Step 11: Task requirements analysis: For example, urban infrastructure includes roads and bridges, water supply and drainage networks, and power facilities. Therefore, intelligence requirements include: target mission area scope (roads and bridges, water supply and drainage networks, and power facilities), data type (high-definition image data, three-dimensional modeling data, infrared imaging data), collection accuracy, and timeliness.

[0023] For example, in road and bridge monitoring, the intelligence needs are: the need to collect the locations of cracks in the road and bridge structures, and to provide real-time feedback on the coordinates of the crack locations; in water supply and drainage system inspections, the intelligence needs are: the need to obtain the siltation data and leakage locations of the drainage pipes, and to provide real-time feedback on the geographical locations of the leakage and siltation data; in power facility management, the intelligence needs are: the need to identify the aging of electric wires and the tilt of transmission towers, and to provide real-time feedback on the geographical locations of the aging of electric wires and the tilt of transmission towers.

[0024] Step 12: Device configuration; Exemplarily, in the step of selecting the type of aircraft according to intelligence requirements: the aircraft is selected according to the scope of the target mission area, wherein the aircraft includes: quad-rotor drones, fixed-wing aircraft, and high-altitude long-flight aircraft. For example, if the target mission area is small and requires close-range inspections, such as short-distance pipe network inspections in cities, a small drone (such as a quad-rotor drone) is selected; if the mission involves a large area or long-term inspections, such as inspections of large-scale water supply system pipe networks or power grids, a fixed-wing aircraft or a long-flight aircraft is selected. The choice of aircraft directly affects the flight duration, coverage, and mission execution efficiency.

[0025] Exemplarily, in the step of selecting the sensor equipment to be carried: according to the specific mission objectives, suitable sensor equipment is selected to meet the data collection requirements, that is, the sensor equipment carried on the aircraft is a high-definition optical camera, a radar system and an infrared thermal imager; among which, the high-definition optical camera is used to collect high-definition image data of roads and bridges; the infrared thermal imager is used to perform infrared imaging of water supply and drainage pipelines and power facilities to obtain infrared imaging data; the lidar is used to model the building structure to obtain three-dimensional modeling data.

[0026] Step 13: Flight plan design: For example, in the step of using a flight path optimization algorithm to generate the optimal flight route for an aircraft: based on the aircraft performance, the spatial layout of the mission area, and urban airspace restrictions, an ant colony algorithm is used to optimize the path to obtain the optimal flight route. In the drainage system inspection, it is necessary to plan the path for the aircraft to cover all pipes while avoiding interference with buildings during low-altitude flight. In the road and bridge inspection mission, the flight path needs to be laid out around the key structures of the road and bridge to ensure that the sensor can obtain data on the road and bridge in all directions.

[0027] S2, collect multimodal data of urban infrastructure; Specifically, multimodal data of urban infrastructure is collected while the aircraft is performing its mission along the optimal flight route. The multimodal data includes: high-definition image data, radar signal data, and infrared imaging data.

[0028] Step 21: Sensor deployment: For example, in urban infrastructure missions, the aircraft integrates multiple sensors to achieve multimodal data collection to meet the monitoring needs of different scenarios.

[0029] Step 22: Real-time collection: High-definition optical cameras are used to collect high-definition image data of cracks in roads and bridges in real time; infrared thermal imagers are used to obtain infrared imaging data for leakage in water supply and drainage networks and overheating hazards in power facilities in real time; laser radars are used to model building structures in real time to obtain three-dimensional modeling data, and to assess whether the building structure is intact based on the three-dimensional modeling data. In addition, aircraft can also collect heat distribution data on building exterior walls through multi-spectral cameras for building energy-saving detection and safety hazard inspection. Through the comprehensive collection of multi-source sensor data, aircraft can obtain comprehensive infrastructure status information, providing reliable data support for subsequent data analysis and urban management.

[0030] Step 23: Data filling: It also includes: During the aircraft inspection process, the aircraft analyzes the integrity and abnormal points of multimodal data through the multimodal data received in real time. If data is missing or sudden problems are detected in key parts, such as incomplete data in the crack area of ​​the bridge main beam, or abnormal temperature distribution in a specific location of the water supply pipeline, the system will dynamically adjust the flight path and sensor operating parameters to ensure that accurate and complete monitoring data is collected. The dynamic monitoring and adjustment mechanism can not only improve the accuracy of intelligence collection, but also quickly locate problem points in emergency tasks, optimize resource allocation and response efficiency.

[0031] S3, data processing; Specifically, multimodal data is denoised to obtain target multimodal data, and target multimodal data is fused to obtain fused data; deep learning algorithms and artificial intelligence algorithms are used to analyze the fused data, identify targets in the fused data, and classify and track the targets; Step 31: Data cleaning: In the step of denoising the multimodal data to obtain the target multimodal data, in order to ensure the quality of the collected data, the multimodal data is first preprocessed to eliminate noise and redundant information. The image denoising algorithm can improve the clarity of the image of urban roads or bridge cracks in low-light environments; the radar signal denoising algorithm can improve the accuracy of bridge structure deformation or underground pipe network data; infrared thermal imaging data uses median filtering to eliminate random interference signals in hot spot detection.

[0032] For example, in the high-definition image data processing link, the non-local mean method is used for denoising, the edge details of the bridge cracks are accurately extracted, and the interference of noise on subsequent detection is reduced; in the processing of radar signal data, the wavelet transform technology is used to decompose and reconstruct the noisy signal to effectively separate the real signal of the infrastructure structure change. In addition, for infrared thermal imaging data, the median filtering technology is used to remove high-noise areas while retaining the key features of the heat source distribution, which is used to detect leakage points in water supply networks or overheating areas of power equipment. Through the above preprocessing methods, the clarity and accuracy of the data can be significantly improved, providing high-quality input for subsequent multimodal data fusion.

[0033] Step 32: Data fusion: For example, in the process of data fusion of target multimodal data, different types of data are integrated together through specific algorithms and technical means to form fused data, which solves the problem of single sensor data limitations. First, the target high-definition image data is fused with the target radar signal data, aligned through a common spatial reference coordinate system, and combined with point cloud processing technology to generate an accurate three-dimensional structural model for the deformation trend of the bridge and the structural stability analysis of the building; secondly, on the basis of the three-dimensional structural model, infrared thermal imaging data is superimposed, and the temperature distribution of the thermal imaging data is combined with the geometric features of the three-dimensional model to mark the location of the pipeline leakage point or the power overheating area, thereby improving the accuracy of problem location. In addition, the high-frequency vibration data of the lidar can be matched and analyzed with the geological information collected by the ground monitoring station to monitor the dynamic changes of the foundation settlement in real time and prevent the occurrence of structural disasters. Multimodal data fusion provides more comprehensive and accurate monitoring information for urban management, and supports urban planning and emergency management decisions.

[0034] Step 33: Intelligent analysis: For example, in the step of analyzing the fused data using deep learning algorithms and artificial intelligence algorithms, identifying targets in the fused data, and classifying and tracking the targets: the deep learning algorithm uses a convolutional neural network (CNN) model, which can quickly identify bridge cracks, road potholes, or pipe network leakage areas in the fused data after fusion, and classify and label the targets; at the same time, through the target tracking algorithm, real-time monitoring of dynamic changes, detection of dynamic changes in the temperature distribution of the building's exterior wall, and judgment of whether there is a potential fire hazard; or trend analysis of the cumulative effect of the deformation of the bridge main beam to predict its future safety. In addition, the system can also respond quickly to emergencies. After an earthquake or flood disaster occurs, by comparing the multimodal data before and after, the damage degree of the building structure is evaluated, and a real-time emergency assessment report is generated and fed back to the management center so that decision makers can quickly formulate disaster response plans.

[0035] S4, intelligence delivery; Specifically, the target location, target category, and tracking data are delivered as reconnaissance data for intelligence delivery.

[0036] Step 41, Intelligence Generation: For example, in the task of urban infrastructure management, the forms of intelligence delivery are flexible and diverse according to user needs. The forms of intelligence delivery include: standardized reports, interactive maps and real-time data visualization. Specifically, standardized reports are based on target location data (such as bridge cracks, pipeline leakage, and the geographical location of hidden dangers in power facilities), target category data (classification results such as bridge cracks, road potholes, pipeline leakage, and aging of power equipment) and dynamic tracking data, combined with statistical analysis of monitoring targets (such as crack length, area and other characteristics, hidden danger severity level and trend analysis) and comprehensive state model data (three-dimensional structural model generated by integrating multimodal data) to generate a complete state overview and risk assessment report. At the same time, historical monitoring data is used to predict future damage trends or failure probabilities of facilities, providing managers with decision support materials that are easy to archive and consult. By integrating the GIS platform, the interactive map uses precise geographic location data, three-dimensional structural models after multimodal data fusion, thermal imaging distribution maps, multi-target classification and annotation data, and dynamic data tracking and change records to intuitively present the monitoring targets as layered visualization results. Users can click on the map to view detailed facility status, such as the expansion range of bridge cracks or the real-time distribution of underground pipe network leakage points, thereby achieving accurate positioning and dynamic monitoring of target facilities. Real-time data visualization relies on real-time acquisition of data streams, target detection and classification results, dynamic change monitoring data, and high-frequency monitoring curves. It is presented in the form of charts, heat maps, and dynamic animations to meet users' monitoring needs for real-time status such as building exterior wall temperature distribution and bridge main beam stress changes. At the same time, it supports users to select monitoring areas or facilities for personalized display according to specific task requirements, providing strong support for emergency response and daily monitoring.

[0037] Step 42, Intelligence Security: Specifically, to ensure the security of intelligence transmission, data is transmitted through encrypted communication links during the intelligence delivery process to ensure the security of data during transmission, support mobile device access and historical data analysis, and use multi-layer encryption technology to protect data to prevent information leakage during transmission.

[0038] Exemplarily, in this embodiment, the aircraft transmits the collected multimodal data to the ground control center in real time through an encrypted communication link, and ensures the stability and timeliness of the data transmission process through high-bandwidth, low-latency wireless communication technology, such as 5G network or dedicated broadband channel. In the scenario of bridge monitoring or disaster emergency, the ground control center can quickly analyze the damage of the bridge structure, the spread of the fire or the collapse of the building based on the high-definition image data, thermal imaging data and three-dimensional modeling data received in real time, and generate a high-precision emergency response report. The encryption protection mechanism of real-time data transmission can also prevent the data from being intercepted or tampered with during the transmission process, ensuring the security of information. Among them, visualization services are provided through the client interface in the intelligence delivery platform, and users can query, download and analyze relevant data in real time. Urban planners can view the risk assessment report of infrastructure through mobile devices and monitor the real-time operation status of roads and bridges; operation and maintenance engineers can query the historical monitoring data of specific facilities through the platform and analyze the long-term change trend of their health status. Through the visualization of charts, heat maps and dynamic three-dimensional models, users can intuitively understand the operation status and potential hidden dangers of infrastructure, providing a reliable basis for accurate decision-making.

[0039] Step 43: Decision support: In order to facilitate subsequent decision-making, real-time decision support tools are provided according to different urban management needs. Through dynamic analysis of multimodal data, the system can predict the congestion trend of urban road traffic and provide optimization suggestions for road maintenance and traffic diversion; or by analyzing the cumulative deformation data of bridges, early warning of potential structural risks can be given to support bridge reinforcement or closure decisions. In addition, the system can also simulate the spread of underground pipeline leakage and its impact on the surrounding geology in real time, providing a scientific basis for the formulation of emergency repair plans. In the event of extreme weather or sudden disasters (such as earthquakes and floods), the system can quickly assess the degree of damage to urban infrastructure, generate emergency response plans and feedback to the management center to assist decision makers in scientific scheduling.

[0040] An aircraft intelligence collection service design system, such as Figure 2As shown, it includes: a mission planning module, a data acquisition module, a data processing module and an intelligence delivery module; the mission planning module is used to take the urban infrastructure as the mission target and clarify the intelligence requirements according to the mission target; and select the aircraft type and the sensor equipment it carries according to the intelligence requirements, and use the flight path optimization algorithm to generate the optimal flight route of the aircraft; the data acquisition module is used to collect multimodal data of urban infrastructure when the aircraft performs the mission according to the optimal flight route, and the multimodal data includes: high-definition image data, radar signal data and infrared imaging data; the data processing module is used to denoise the multimodal data to obtain target multimodal data, and perform data fusion on the target multimodal data to obtain fused data; the fused data is analyzed using deep learning algorithms and artificial intelligence algorithms, the targets in the fused data are identified, and the targets are classified and tracked; the intelligence delivery module is used to deliver the target position, target category and tracking data as reconnaissance data for intelligence delivery.

[0041] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for designing an aircraft intelligence collection service, characterized in that: include: Take urban infrastructure as the mission target and clarify intelligence requirements based on the mission target; The aircraft type and the sensor equipment it carries are selected based on intelligence requirements, and the optimal flight route for the aircraft is generated using a flight path optimization algorithm; Collect multimodal data of urban infrastructure while the aircraft is carrying out its mission along the optimal flight route. The multimodal data includes: high-definition image data, radar signal data, and infrared imaging data; Denoising the multimodal data to obtain target multimodal data, and fusing the target multimodal data to obtain fused data; Use deep learning algorithms and artificial intelligence algorithms to analyze fused data, identify targets in the fused data, and classify and track targets; Delivers target location, target category, and tracking data as reconnaissance data for intelligence delivery.

2. The method for designing an aircraft intelligence collection service according to claim 1, characterized in that: Urban infrastructure includes: roads and bridges, buildings, water supply and drainage pipelines, and power facilities.

3. The method for designing an aircraft intelligence collection service according to claim 1, characterized in that: Intelligence requirements include: scope of target mission area, data type, collection accuracy and timeliness.

4. The method for designing an aircraft intelligence collection service according to claim 3, characterized in that: Aircraft are selected according to the scope of the target mission area, including: quad-rotor drones, fixed-wing aircraft and high-altitude long-flight aircraft.

5. The method for designing an aircraft intelligence collection service according to claim 1, characterized in that: The steps to collect multimodal data of urban infrastructure during the aircraft's mission along the optimal flight route are: The aircraft is equipped with sensor equipment, including high-definition optical cameras, radar systems and infrared thermal imagers; Among them, high-definition optical cameras are used to collect high-definition image data of roads and bridges; radar systems are used to collect three-dimensional modeling data of buildings; and infrared thermal imagers are used to collect infrared imaging data of water supply and drainage pipelines and power facilities.

6. The method for designing an aircraft intelligence collection service according to claim 1, characterized in that: The steps for denoising multimodal data to obtain target multimodal data are: Denoising the high-definition image data based on the non-local mean to obtain the target high-definition image data; De-noising the radar signal data based on wavelet transform to obtain target radar signal data; The infrared imaging data is denoised based on median filtering to obtain the target infrared imaging data.

7. The method for designing an aircraft intelligence collection service according to claim 6, characterized in that: The steps of fusing the target multimodal data to obtain fused data are as follows: The target high-definition image data and the target radar signal data are integrated to obtain an accurate three-dimensional structural model of the urban infrastructure. The accurate three-dimensional structural model is used to analyze the deformation trend of bridges and the structural stability of buildings. Based on the accurate three-dimensional structural model and combined with the target infrared imaging data, the positioning data of hidden danger facilities in urban infrastructure are obtained, and the positioning data of hidden danger facilities are used as fusion data.

8. The method for designing an aircraft intelligence collection service according to claim 1, characterized in that: The steps of using deep learning algorithms and artificial intelligence algorithms to analyze fused data, identify targets in the fused data, and classify and track targets are as follows: The deep learning algorithm uses a convolutional neural network model, and the artificial intelligence algorithm uses a target tracking algorithm; Analyze the fused data and track the target through the target tracking algorithm; The convolutional neural network model is used to identify target areas of bridge cracks, road potholes or pipeline leakage in the fused data, and the target areas are classified and labeled.

9. The method for designing an aircraft intelligence collection service according to claim 1, characterized in that: The forms of intelligence delivery include: standardized reports, interactive maps and real-time data visualization. Among them, data is transmitted through encrypted communication links during the intelligence delivery process to ensure the security of data during transmission, and support mobile device access and historical data analysis.

10. An aircraft intelligence collection service design system, characterized in that: include: Mission planning module, which is used to take urban infrastructure as mission targets and to clarify intelligence requirements based on mission targets; The aircraft type and the sensor equipment it carries are selected based on intelligence requirements, and the optimal flight route for the aircraft is generated using a flight path optimization algorithm; The data acquisition module is used to collect multimodal data of urban infrastructure when the aircraft is carrying out its mission according to the optimal flight route. The multimodal data includes: high-definition image data, radar signal data and infrared imaging data; The data processing module is used to denoise the multimodal data to obtain the target multimodal data, and to fuse the target multimodal data to obtain fused data; to analyze the fused data using deep learning algorithms and artificial intelligence algorithms, to identify the targets in the fused data, and to classify and track the targets; Intelligence delivery module, used to deliver target location, target category, and tracking data as reconnaissance data for intelligence delivery.

Images